Intratumoral and Peritumoral Radiomics Based on Contrast-enhanced MRI for Preoperatively Predicting Treatment Response of Transarterial Chemoembolization in Hepatocellular Carcinoma

Intratumoral and Peritumoral Radiomics Based on Contrast-enhanced MRI for Preoperatively Predicting Treatment Response of Transarterial Chemoembolization in Hepatocellular Carcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Intratumoral and Peritumoral Radiomics Based on Contrast-enhanced MRI for Preoperatively Predicting Treatment Response of Transarterial Chemoembolization in Hepatocellular Carcinoma Ying Zhao, Jian Zhang, Nan Wang, Qihao Xu, Yuhui Liu, Jinghong Liu, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2190531/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Oct, 2023 Read the published version in BMC Cancer → Version 1 posted 8 You are reading this latest preprint version Abstract Background Noninvasive and precise methods to estimate treatment response and identify hepatocellular carcinoma (HCC) patients who could benefit from transarterial chemoembolization (TACE) are urgently required. The present study aimed to investigate the ability of intratumoral and peritumoral radiomics based on contrast-enhanced magnetic resonance imaging (CE-MRI) to preoperatively predict tumor response to TACE in HCC patients. Methods This retrospective study involved 138 HCC patients (objective response, n = 73 vs. non-response, n = 65) who were divided into the training ( n = 96) and validation ( n = 42) cohorts. Total 1206 radiomics features were extracted from arterial, venous, and delayed phases images. Radiomics models on intratumoral region (TR) and peritumoral region (PTR) (3 mm, 5 mm, and 10 mm) were established using logistic regression. Three integrated radiomics models, including intratumoral and peritumoral region (T-PTR) (3 mm), T-PTR (5 mm), and T-PTR (10 mm) models, were constructed by using TR and PTR radiomics scores. A clinical-radiological model and a combined model incorporating the optimal radiomics score and selected clinical-radiological predictors were constructed, and the combined model was presented as a nomogram. The discrimination, calibration, and clinical utilities were evaluated by receiver operating characteristic curve, calibration curve, and decision curve analysis, respectively. Results The (T-PTR) (3 mm) radiomics model demonstrated the best performance among all radiomics models with an area under the curve (AUC) of 0.911 (95% confidence interval(CI), 0.825–0.975) in the validation cohort. The (T-PTR) (3 mm) radiomics score, alkaline phosphatase, tumor size, and satellite nodule were combined to construct a combined nomogram. The combined nomogram outperformed the clinical-radiological model with the AUCs of 0.918 (95%CI, 0.831–0.986) and 0.782 (95%CI, 0.660–0.902) and achieved good calibration capability and clinical utility. Conclusions CE-MRI-based intratumoral and peritumoral radiomics approach can provide an effective tool for the precise and individualized estimation of treatment response for HCC patients treated with TACE. Hepatocellular carcinoma Radiomics Magnetic resonance imaging Transarterial chemoembolization Treatment response Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Hepatocellular carcinoma (HCC) is the most common liver malignancy and the third leading cause of death among various cancers [ 1 ] , with more than 850,000 associated deaths reported annually worldwide [ 2 ] . Liver transplantation and resection are the curative therapies for HCC, and surgical excision is considered as the preferred treatment for HCC patients at early stage [ 3 ] . Unfortunately, the majority of HCC patients are not suitable for curative resection at the time of diagnosis because of poor liver function, multifocal disease, vascular involvement, and extrahepatic spread [ 4 ] . Transarterial chemoembolization (TACE) is widely used as a bridge to liver transplantation, or as the standard treatment for patients with intermediate HCC [ 5 ] . Nevertheless, the therapeutic efficacy of TACE varies greatly between patients and patients because the biological behavior of HCC is highly heterogeneous [ 6 ] . Several studies have evidenced that the overall response rates following TACE range from 15–85% and the cumulative rates of local tumor progression at 1, 3, and 5 years are 33%, 52%, and 73%, respectively [ 7 , 8 ] . Therefore, it is beneficial to preoperatively estimate tumor response to TACE treatment and determine the therapeutic efficacy to aid in guiding subsequent therapeutic strategies to improve overall survival (OS) of HCC patients. Magnetic resonance imaging (MRI)-based evaluations that are noninvasive and repeatable can be used to preoperatively assess the tumor response, which take an advantage of depicting more soft-tissue characteristics than computed tomography (CT) and avoiding ionizing radiation [ 9 ] . Regarding the pretherapeutic imaging features of HCC, several scholars have reported that larger lesion diameter, irregular margin, arterial peritumoral enhancement, satellite nodule, and quantitative functional parameters of diffusion weighted imaging (DWI), intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), and dynamic contrast enhanced magnetic resonance imaging (DCE-MRI), are found to be associated with therapeutic efficacy of TACE treatment [ 9 – 15 ] . Although these imaging characteristics are encouraging, they are not sufficient for individual evaluations of tumor response to TACE, and the ability to predict TACE efficacy in HCC is limited when a high degree of tumor heterogeneity. Radiomics is an emerging methodology, with the goal of going beyond size or human-eye based semantic descriptors of tumors, to enable the non-invasive extraction of high-throughput quantitative data from medical images and to explore their correlation with clinical outcomes [ 16 ] . This approach would capture the three-dimensional (3D) information of the entire tumor and has been proved effective in characterizing the heterogeneity of the tumor by acting as a whole tumor virtual biopsy [ 17 , 18 ] . Numerous studies have demonstrated that radiomics-based models effectively identify the diagnosis and pathological characteristics or predict therapeutic efficacy and prognosis of cancer patients for clinical decision-making [ 19 – 22 ] . Recently, there has been increasing interest in evaluating radiomics patterns of the region surrounding the visible tumor [ 20 , 23 , 24 ] . Recurrence or metastasis of HCC is mainly intrahepatic, indicating that the peritumoral liver tissue may be a favorable soil for the spreading hepatoma cells [ 25 ] . Several scholars have reported that HCC patients with microvascular invasion (MVI), epithelial cell adhesion molecule (EpCAM), programmed death ligand 1 (PD-L1) expression, and higher CD68 + cell density in peritumoral tissues have a significantly higher risk of recurrence or metastasis and cancer-related death [ 26 – 29 ] ; thus, peritumoral tissues might have valuable predictive information of HCC prognosis. Several recent studies have focused on peritumoral radiomics for predicting MVI, early recurrence (ER), recurrence-free survival (RFS) of HCC patients after resection or TACE [ 20 , 23 , 24 ] ; however, the value of intratumoral and peritumoral radiomics based on MRI in predicting treatment response of HCC after TACE remains unknown. Therefore, the present study aimed to determine whether radiomics assessment of HCC peritumoral regions based on contrast-enhanced MR (CE-MR) images could provide valuable information about TACE response and enhance the ability of intratumoral radiomics to predict treatment efficacy of TACE in patients with HCC. Materials And Methods Patients This retrospective study was approved by the Institutional Review Board of the First Affiliated Hospital of Dalian Medical University and the requirement for informed consent was waived due to the retrospective nature of the study. From April 2008 to February 2022, 343 consecutive patients with HCC who underwent CE-MRI examination within two weeks before conventional TACE at our institution were recruited. HCCs were diagnosed by pathology or with reference to American Association for the Study of Liver Disease (AASLD) guidelines [ 30 ] . The exclusion criteria were: (1) previous oncological treatment, including liver resection, radiofrequency ablation (RFA), or chemotherapy ( n = 32); (2) diffuse or infiltrative HCCs or the largest lesion size < 1cm ( n = 16); (3) extrahepatic metastasis or portal vein occlusion ( n = 10); (4) unavailability of hepatic-arterial CE-MR imaging within 3 months after TACE treatment ( n = 145); (5) poor image quality ( n = 2). Figure 1 shows the flowchart of patient recruitment, and 138 patients were enrolled in this study and randomly divided into a training cohort ( n = 96) and a validation cohort ( n = 42) at a ratio of 7:3. Pretherapeutic clinical characteristics including age, gender, history of hepatitis B or C, alpha-fetoprotein (AFP), alanine aminotransferase (ALT), aspartate aminotransferase (AST), γ-glutamyltranspeptadase (GGT), alkaline phosphatase (ALP), total bilirubin (TBIL), albumin (ALB), platelet count (PLT), prothrombin time (PT), Child-Pugh class, Eastern Cooperative Oncology Group (ECOG) performance status, and Barcelona Clinic Liver Cancer (BCLC) stage for each patient were retrospectively collected within 1 week before TACE. MRI Protocol All MRI examinations were performed with 1.5T or 3.0T MR scanner (Signa, HDXT, GE Healthcare) with a phased-array 8-channel sensitivity encoding abdominal coil. The CE-MRI examination was performed using the liver acquisition with volume acceleration (LAVA) protocol with fat-suppressed T1-weighted 3D fast-spoiled gradient-recalled echo sequence. The contrast enhanced images consisted of arterial phase (AP), portal venous phase (PVP), and delayed phase (DP) images, which were obtained at 40 s, 70 s, and 90 s, respectively, after the start of contrast injection of Gd-diethylenetriamine pentaacetic acid (Gd-DTPA) (Bayer Schering Pharma AG, Germany) at a patient weight-dependent dose of 0.1 mmol/kg with an injection rate of 2.5 mL/s through a median cubital vein. Other MRI sequences were also performed, including in- and opposed-phase fast-spoiled gradient-recalled echo T1-weighted imaging (T1WI) and fat-suppressed fast spin-echo T2-weighted imaging (T2WI). The detailed scanning parameters are shown in Supplementary Data S1 . Image Analysis Two radiologists (reader 1, Y.Z. and reader 2, N.W., with 8-year and 7-year experience in abdominal MRI) independently analyzed pretherapeutic MR images, and they were aware of the diagnosis of HCC but blinded to clinical information and imaging report. The radiologists evaluated the following imaging traits for each patient: (1) tumor size; (2) tumor number; (3) tumor margin; (4) intratumoral necrosis; (5) intratumoral hemorrhage; (6) intratumoral fat; (7) tumor encapsulation; (8) arterial peritumoral enhancement; (9) satellite nodule; (10) internal arteries; (11) radiological cirrhosis. Tumor diameter was recorded as mean value, and any discrepancy in imaging feature assessment was resolved by means of reevaluation by another senior radiologist (J.H.L., with 20-year experience in abdominal MRI). Detailed definitions of these imaging features are described in Supplementary Data S2 . TACE Procedure and Treatment Response Assessment All conventional TACE procedures were carried out by interventional radiologists with no less than 10 years of clinical experience. The Seldinger technique was used to puncture the right femoral artery, and a 5F catheter (RH catheter, Terumo, Tokyo, Japan) was inserted for celiac and hepatic artery angiography in evaluation of the vascular anatomy and tumor burden. After superselective catheterization of hepatic artery feeding the tumor, an emulsion mixture of lobaplatin, raltitrexed, and Lipiodol was injected using a 2.7F microcatheter (Progreat microcatheter, Hengrui Pharmaceutical Co. Ltd., Lianyungang, China), followed by embolization of the tumor-feeding artery using blank microsphere (100–300 µm) or gelatin sponge particles (1–2 mm). The TACE procedure was stopped when tumor staining completely disappeared and regional arterial blood flow stopped. The modified Response Evaluation Criteria in Solid Tumors (mRECIST 1.1) criteria was utilized to assess the tumor response in patients with HCC based on pre- and post-therapeutic arterial MR images. Tumor response was classified into four categories according to the mRECIST system as follows: complete response (CR), partial response (PR), stable disease (SD), and progression disease (PD) [ 31 ] . In the present study, all patients were divided into the objective response (OR) group (CR and PR patients) and the non-response (NR) group (SD and PD patients). Tumor Segmentation and Radiomics Feature Extraction Pretherapeutic CE-MR images were exported from the picture archiving and communication system (PACS) and then used for tumor segmentation and radiomics feature extraction. The AK software (Artificial Intelligence Kit, Version 3.2.5, GE Healthcare) was used to process the images before segmenting the tumor. AP, PVP, and DP images were resampled to a uniform voxel size of 1 × 1 × 1 mm via linear interpolation algorithm to standardize the voxel spacing [ 22 ] . Intensity normalization of images was performed to correct the scanner effect. Tumor segmentation was performed by manually delineating the region of interest (ROI) along the tumor contour on each axial slice of AP, PVP, and DP images using an open-source software (ITK-SNAP, version 3.6.0, http://www.itksnap.org/ ). The ROI was required to include capsule surrounding the tumor and to exclude tumor surrounding vessels, and then every ROI was automatically merged into volume of interest (VOI). Notably, in terms of multifocal HCCs, the largest nodule was selected as the delineated lesion. To capture radiomics features from the tumor periphery, the VOIs of peritumoral region (PTR) were generated by automatically expanding 3 mm, 5 mm, and 10 mm from the lesion border using AK software. If the ROI was beyond the parenchyma of the liver after the expansion, the portion beyond the parenchyma was removed manually. Radiomics features were extracted using AK software for intratumoral region (TR), PTR (3 mm), PTR (5 mm), and PTR (10 mm). A total of 1206 radiomics features were extracted for each VOI (based on AP, PVP, and DP images). The extracted radiomics features included: 42 histograms, 15 form factors, 10 Haralick features, 144 gray level co-occurrence matrix (GLCM) with an offset of 1/4/7, 180 gray level run length matrix (GLRLM) with an offset of 1/4/7, and 11 grey-level zone size matrix (GLZSM). Details of radiomics features are listed in Supplementary Data S3 . Z-score normalization of radiomics features was performed to reduce the bias caused by different dimensions. The workflow of the radiomics analysis is depicted in Fig. 2 . Inter- and intraclass correlation coefficients (ICCs) were used to assess the reproducibility of each radiomics feature extracted from 30 randomly chosen patients. To assess the interobserver reproducibility, the ROI delineation was performed by two abdominal radiologists (readers 1 and 2, Y.Z. and N.W.) independently who were blinded to all patients’ information. To evaluate the intraobserver reproducibility, reader 1 repeated the ROI delineation and feature extraction at a 1-month interval. Finally, reader 1 completed the remaining image segmentation and feature extraction. Feature Selection and Radiomics Model Construction To remove potentially redundant features and decrease data dimensions, we followed a three-step procedure to identify the most predictive radiomics features. First, the radiomics features with ICCs of both interobserver and intraobserver greater than 0.80 indicated satisfactory reproducibility and high robustness and were selected for further analysis. Second, the redundant features with a correlation coefficient higher than 0.90 were eliminated following by the Spearman’s rank correlation test. Finally, the gradient boosting decision tree (GBDT) algorithm was applied to determine the top-ranked and most valuable features for predicting the tumor response. GBDT was proposed as a tree-based algorithm based on a greedy strategy (called gradient boosting) that evaluates the importance of a texture feature through the time it used as branching point for the tree [ 19 ] . After those steps, TR, PTR (3 mm), PTR (5 mm), and PTR (10 mm) radiomics models were separately established using logistic regression algorithm with 5-fold cross-validation. The radiomics score (Rad-score) was calculated for each patient using a linear combination of the selected features that were weighted by their respective coefficients. Finally, three integrated radiomics models, including intratumoral and peritumoral region (T-PTR) (3 mm), T-PTR (5 mm), and T-PTR (10 mm) radiomics models, were generated by logistic regression using TR rad-score and corresponding PTR rad-score. The optimal radiomics model with the highest area under the curve (AUC) was selected for further analysis. Clinical-radiological Model Construction Univariate analysis was used to identify significant variables among clinical-radiological characteristics associated with treatment response of HCC ( P < 0.05). Multivariate logistic regression analysis was performed to identify the independent risk factors for predicting tumor response. Odds ratio and 95% confidence interval (CI) were calculated for each risk factor. The clinical-radiological model was constructed using the chosen independent risk factors. Combined Model Building and Nomogram Construction A combined model integrating clinical-radiological risk factors and the optimal rad-score was constructed using the proposed logistic regression method. To provide an individual predictive graphical presentation, the combined model was presented as a nomogram. The nomogram could help calculate the predicted probability of NR for each individual patient. Statistical Analysis The Student’s t -test or Mann-Whitney U -test were used to compare the continuous variables between the OR and NR groups as appropriate. The chi-squared test or Fisher’s exact test were used to assess the categorical variables as appropriate. Interobserver agreement of the radiological feature evaluation was qualified by Cohen kappa coefficient. Kappa values of 0.81–1.00 indicated excellent agreement, 0.61–0.80 signified substantial agreement, and 0.41–0.60 denoted moderate agreement. Receiver operating characteristic curve (ROC) analysis was performed to evaluate the performance of each predictive model. The AUC, accuracy, sensitivity, and specificity were calculated. We compared the predictive performance between different models using the Delong’s test. Calibration curves were drawn to evaluate the degree of deviation between the predictions and actual outcomes with the Hosmer-Lemeshow test. Decision curve analysis (DCA) was performed to validate the clinical utility of the nomogram. All statistical analyses for the present study were performed with R software (version 3.6.1, http://www.R-project.org ). P < 0.05 was considered statistically significant. Results Patient Characteristics In total, 138 patients (mean age, 60.24 ± 8.91 years; range, 40–83 years; 115 male) were enrolled in this study. Among the 138 patients with HCC, 6 patients were determined by pathology and 132 patients were identified by specific imaging features according to the AASLD guidelines. On the basis of the mRECIST criteria, the patients for CR, PR, SD, and PD were 13 (9.4%), 60 (43.5%), 51 (37.0%), and 14 (10.1%), respectively. All patients underwent post-therapeutic MRI examination following by the initial TACE treatment, with a median interval of 44 days (range, 28–90 days) between the first TACE and the follow-up MRI examination. The clinical-radiological data in the OR and NR groups are summarized in Table 1 . No significant difference was found in the clinical-radiological characteristics, except for tumor encapsulation between the training and validation cohorts. Table 1. Patient clinical-radiological characteristics Characteristics Training cohort ( n = 96) Validation cohort ( n = 42) P value OR group ( n = 51) NR group ( n = 45) P value OR group ( n = 22) NR group ( n = 20) P value Age (years, mean ± SD) 60.29±8.41 60.42±10.25 0.947 60.18±9.02 59.75±7.31 0.866 0.820 Gender ( n , [%]) 0.410 0.890 1.000 Male 44(86.3) 36(80.0) 18(81.8) 17(85.0) Female 7(13.7) 9(20.0) 4(18.2) 3(15.0) History of hepatitis B or C ( n , [%]) 0.541 0.361 0.410 Positive 38(74.5) 31(68.9) 19(86.4) 14(70.0) Negative 13(25.5) 14(31.1) 3(13.6) 6(30.0) AFP (IU/ml) ( n , [%]) 0.050 0.231 0.523 ≤ 400 40(78.4) 27(60.0) 16(72.7) 11(55.0) > 400 11(21.6) 18(40.0) 6(27.3) 9(45.0) ALT (U/L) ( n , [%]) 0.521 0.827 0.904 ≤ 50 36(70.6) 29(64.4) 15(68.2) 13(65.0) > 50 15(29.4) 16(35.6) 7(31.8) 7(35.0) AST (U/L) ( n , [%]) 0.024 0.187 0.302 ≤ 40 31(60.8) 17(37.8) 11(50.0) 6(30.0) > 40 20(39.2) 28(62.2) 11(50.0) 14(70.0) GGT (U/L) ( n , [%]) 0.004 0.016 0.639 ≤ 60 26(51.0) 10(22.2) 11(50.0) 3(15.0) > 60 25(49.0) 35(77.8) 11(50.0) 17(85.0) ALP (U/L) ( n , [%]) 125 5(9.8) 19(42.2) 3(13.6) 11(55.0) TBIL (umol/L) ( n , [%]) 0.650 0.746 0.177 ≤ 19 34(66.7) 28(62.2) 11(50.0) 11(55.0) > 19 17(33.3) 17(37.8) 11(50.0) 9(45.0) ALB (g/L) ( n , [%]) 0.897 0.789 0.922 < 40 29(56.9) 25(55.6) 13(59.1) 11(55.0) ≥ 40 22(43.1) 20(44.4) 9(40.9) 9(45.0) PLT (×10 9 /L) ( n , [%]) 0.031 0.516 0.783 13 17(33.3) 11(24.4) 8(36.4) 9(45.0) Child-Pugh class ( n , [%]) 0.585 0.379 0.078 A 44(86.3) 37(82.2) 17(77.3) 13(65.0) B 7(13.7) 8(17.8) 5(22.7) 7(35.0) ECOG performance status ( n , [%]) 0.060 0.095 0.084 0 49(96.1) 37(82.2) 20(90.9) 13(65.0) 1 2(3.9) 8(17.8) 2(9.1) 7(35.0) BCLC stage ( n , [%]) 0.003 0.076 0.480 A 35(68.6) 18(40.0) 13(59.1) 8(40.0) B 13(25.5) 12(26.7) 7(31.8) 3(15.0) C 3(5.9) 15(33.3) 2(9.1) 9(45.0) Table 1. Patient clinical-radiological characteristics (continued) Characteristics Training cohort ( n = 96) Validation cohort ( n = 42) P value OR group ( n = 51) NR group ( n = 45) P value OR group ( n = 22) NR group ( n = 20) P value Tumor size ( n , [%]) 5 cm 15(29.4) 30(66.7) 5(22.7) 15(75.0) Tumor number ( n , [%]) 0.664 0.275 1.000 Unifocal 35(68.6) 29(64.4) 13(59.1) 15(75.0) Multifocal 16(31.4) 16(35.6) 9(40.9) 5(25.0) Tumor margin ( n , [%]) 0.541 0.031 0.245 Smooth 38(74.5) 31(68.9) 17(77.3) 9(45.0) Non-smooth 13(25.5) 14(31.1) 5(22.7) 11(55.0) Intratumoral necrosis ( n , [%]) 0.005 0.129 0.833 Present 16(31.4) 27(60.0) 7(31.8) 11(55.0) Absent 35(68.6) 18(40.0) 15(68.2) 9(45.0) Intratumoral hemorrhage ( n , [%]) 0.030 0.060 0.611 Present 13(25.5) 21(46.7) 4(18.2) 9(45.0) Absent 38(74.5) 24(53.3) 18(81.8) 11(55.0) Intratumoral fat ( n , [%]) 0.112 0.872 0.826 Present 7(13.7) 12(26.7) 5(22.7) 4(20.0) Absent 44(86.3) 33(73.3) 17(77.3) 16(80.0) Tumor encapsulation ( n , [%]) 0.286 0.032 0.026 Present 39(76.5) 30(66.7) 15(68.2) 7(35.0) Absent 12(23.5) 15(33.3) 7(31.8) 13(65.0) Arterial peritumoral enhancement ( n , [%]) 0.050 0.118 0.846 Present 11(21.6) 18(40.0) 4(18.2) 8(40.0) Absent 40(78.4) 27(60.0) 18(81.8) 12(60.0) Satellite nodule ( n , [%]) 0.001 0.670 0.831 Present 1(2.0%) 11(24.4) 2(9.1) 2(10.0) Absent 50(98.0) 34(75.6) 20(90.9) 18(90.0) Internal arteries ( n , [%]) 0.046 0.031 0.464 Present 18(35.3) 25(55.6) 5(22.7) 11(55.0) Absent 33(64.7) 20(44.4) 17(77.3) 9(45.0) Radiological cirrhosis ( n , [%]) 0.030 0.372 0.719 Present 36(70.6) 22(48.9) 14(63.6) 10(50.0) Absent 15(29.4) 23(51.1) 8(36.4) 10(50.0) OR, objective response; NR, non-response; SD, standard deviation; AFP, alpha-fetoprotein; ALT, alanine aminotransferase; AST, aspartate aminotransferase; GGT, γ-glutamyltranspeptadas; ALP, alkaline phosphatase; TBIL, total bilirubin; ALB, albumin; PLT, platelet count; PT, prothrombin time; ECOG, Eastern Cooperative Oncology Group; BCLC, Barcelona Clinic Liver Cancer. Radiomics Model Development and Evaluation After inter- and intraobserver reproducibility analysis, the dimensions of feature spaces were 853 for VOI TR , 424 for VOI PTR (3 mm) , 452 for VOI PTR (5 mm) , and 716 for VOI PTR (10 mm) . Spearman’s rank correlation test allowed the selection of 100, 35, 43, and 90 features, respectively. GBDT revealed that the radiomics feature numbers ultimately consisted of 25, 14, 17, and 21 from the VOI TR , VOI PTR (3 mm) , VOI PTR (5 mm) , and VOI PTR (10 mm) , respectively, and were used for the radiomics model building (TR, PTR (3 mm), PTR (5 mm), and PTR (10 mm) models). Finally, three T-PTR radiomics models based on TR and PTR rad-scores were constructed. The calculation formulae for the rad-score are shown in Supplementary Data S4 . The radiomics models demonstrated favorable discrimination in the both cohorts (AUC: training cohort, 0.810–0.892; validation cohort, 0.793–0.911). The TR, PTR (3mm), PTR (5mm) radiomics models showed comparative performance with the AUCs of 0.820 (95% CI, 0.705–0.917), 0.823 (95% CI, 0.701–0.927), and 0.823 (95% CI, 0.711–0.924) in the validation cohort, and the predictive performance of these three models were slightly higher than that of the PTR (10 mm) model (AUC: 0.793; 95%CI: 0.673–0.903). Compared with TR and PTR radiomics models, the T-PTR radiomics models (T-PTR (3 mm), T-PTR (5 mm), and T-PTR (10 mm) models) performed better in predicting tumor response with the AUCs of 0.911 (95%CI, 0.825–0.975), 0.909 (95%CI, 0.817–0.982), and 0.895 (95%CI, 0.815–0.964) in the validation cohort; thus, we chose the T-PTR (3 mm) model as the best-performing radiomics model for further analysis. ROC curves and discriminative performance of the seven radiomics models in the two cohorts are shown in Supplementary Data S5 and Table 2 . Model AUC (95% CI) Accuracy Sensitivity Specificity P value Table 2 Discrimination performance of different predictive models Clinical-radiological model TC 0.789 (0.709–0.863) 0.729 0.556 0.882 0.935 VC 0.782 (0.660–0.902) 0.714 0.600 0.818 TR model TC 0.836 (0.763–0.903) 0.771 0.778 0.765 0.840 VC 0.820 (0.705–0.917) 0.714 0.750 0.682 PTR (3 mm) model TC 0.817 (0.740–0.881) 0.750 0.889 0.627 0.940 VC 0.823 (0.701–0.927) 0.714 0.800 0.636 PTR (5 mm) model TC 0.810 (0.739–0.880) 0.771 0.800 0.745 0.877 VC 0.823 (0.711–0.924) 0.714 0.900 0.545 PTR (10 mm) model TC 0.852 (0.785–0.911) 0.760 0.822 0.706 0.468 VC 0.793 (0.673–0.903) 0.762 0.900 0.636 T-PTR (3 mm) model TC 0.884 (0.821–0.936) 0.812 0.822 0.804 0.633 VC 0.911 (0.825–0.975) 0.810 0.800 0.818 T-PTR (5 mm) model TC 0.883 (0.822–0.934) 0.823 0.733 0.902 0.661 VC 0.909 (0.817–0.982) 0.810 0.650 0.955 T-PTR (10 mm) model TC 0.892 (0.834–0.942) 0.844 0.800 0.882 0.945 VC 0.895 (0.815–0.964) 0.786 0.750 0.818 Combined nomogram TC 0.910 (0.854–0.958) 0.844 0.822 0.863 0.891 VC 0.918 (0.831–0.986) 0.857 0.800 0.909 TR, intratumoral region; PTR, peritumoral region; T-PTR, intratumoral and peritumoral region; TC, training cohort; VC, validation cohort; AUC, area under the curve; CI, confidence interval. Clinical-radiological Model Development and Evaluation Interobserver agreements on the radiological features were substantial to excellent (kappa-value range: 0.772–1.000). Univariate and multivariate analyses of clinical-radiological characteristics for predicting treatment response in the training cohort are shown in Table 3 . The univariate analysis indicated that AST, GGT, ALP, PLT, ECOG, BCLC stage, tumor size, intratumoral necrosis, intratumoral hemorrhage, satellite nodule, internal arteries, and radiological cirrhosis were significant clinical-radiological factors for discriminating the OR and NR groups in the training cohort (all P < 0.05). The multivariate logistic regression analysis demonstrated that ALP (odd ratio = 5.744; 95% CI: 1.780–18.532; P = 0.003), tumor size (odd ratio = 3.005; 95% CI: 1.154–7.826; P = 0.024), and satellite nodule (odd ratio = 9.865; 95% CI: 1.101–88.370; P = 0.041) were the independent risk factors for predicting NR in HCC patients. The clinical-radiological model was built by incorporating these three variables. The clinical-radiological model yielded the AUCs of 0.789 (95% CI, 0.709–0.863) and 0.782 (95% CI, 0.660–0.902) in the two cohorts, respectively (shown in Table 2 ). Table 3. Univariate and multivariate analyses of clinical-radiological characteristics for predicting treatment response Variables Univariate analysis Multivariate analysis Odd ratio (95% CI) P value Odd ratio (95% CI) P value AST 2.553 (1.120 - 5.820) 0.026 — — GGT 3.640 (1.492 - 8.880) 0.005 — — ALP 6.723 (2.246 - 20.122) <0.001 5.744 (1.780 - 18.532) 0.003 PLT 2.491 (1.079 - 5.753) 0.033 — — ECOG 5.297 (1.062 - 26.428) 0.042 — — BCLC stage 2.716 (1.515 - 4.872) <0.001 — — Tumor size 4.800 (2.023 - 11.392) <0.001 3.005 (1.154 - 7.826) 0.024 Intratumoral necrosis 3.281 (1.417 - 7.600) 0.006 — — Intratumoral hemorrhage 2.558 (1.082 - 6.044) 0.032 — — Satellite nodule 16.176 (1.996 - 131.069) 0.009 9.865 (1.101 - 88.370) 0.041 Internal arteries 2.292 (1.007 - 5.213) 0.048 — — Radiological cirrhosis 0.399 (0.172 - 0.923) 0.032 — — The clinical-radiological characteristics with P value less than 0.05 in the univariate analysis are listed in the table. AST, aspartate aminotransferase; GGT, γ-glutamyltranspeptadase; ALP, alkaline phosphatase; PLT, platelet count; ECOG, Eastern Cooperative Oncology Group; BCLC, Barcelona Clinic Liver Cancer; CI, confidence interval. Combined Model and Nomogram Development and Evaluation The T-PTR (3 mm) rad-score, ALP, tumor size, and satellite nodule were considered as input variables for logistic regression to build the combined model. We chose a nomogram as the graphical representation of the best-performing combined model as shown in Fig. 3 . The combined nomogram yielded an AUC, an accuracy, a sensitivity, and a specificity of 0.910 (95% CI, 0.854–0.958), 0.844, 0.822, and 0.863, respectively, for discriminating between the OR and NR groups in the training cohort and of 0.918 (95% CI, 0.831–0.986), 0.857, 0.800, and 0.909, respectively, in the validation cohort. The discriminating performance of the combined nomogram in the training cohort was significantly superior to that of the clinical-radiological model (AUC, 0.910 vs. 0.789, P = 0.028), whereas there was no significant difference between the models in the validation cohort (AUC, 0.918 vs. 0.782, P = 0.127). No significant differences were found between the T-PTR (3mm) radiomics model and combined nomogram (training cohort, P = 0.577; validation cohort, P = 0.918) and between the T-PTR (3mm) radiomics model and clinical-radiological model (training cohort, P = 0.094; validation cohort, P = 0.139). Table 2 summarizes the predictive performance of the constructed models. ROC curves for clinical-radiological model, T-PTR (3mm) radiomics model, and combined nomogram in the both cohorts are shown in Fig. 4 A and 4 B. The Delong’s test of different predictive models in the both cohorts is shown in Supplementary Data S6 . Calibration curves of the combined nomogram for the probability of treatment response demonstrated good agreements between prediction and observation in the training and validation cohorts (Fig. 5 A, 5 B). The Hosmer-Lemeshow test yielded non-significant results in the both cohorts ( P = 0.386 and 0.343), which suggested a satisfying fit of the nomogram. The DCA indicated that the combined nomogram obtained more net benefits than the clinical-radiological model, T-PTR (3mm) radiomics model, and “treat-all” or “treat-none” strategies for most of the threshold probabilities in the training and validation cohorts (Fig. 6 A, 6 B). Discussion In the study, we analyzed various radiomics models for intratumoral, peritumoral, and intratumoral combined peritumoral derived from preoperative CE-MR images for response estimation after TACE. Our study confirms that the radiomics models combining intratumoral and peritumoral are better than the intratumoral radiomics model in predicting TACE response of HCC patients. Furthermore, we developed and validated a combined nomogram that integrated the clinical-radiological risk factors and the optimal T-PTR (3mm) rad-score for predicting HCC response to TACE, demonstrating satisfactory performance, calibration, and clinical utility. The proposed radiomics approach successfully predicted therapeutic efficacy of TACE and may facilitate individualized treatment decision-making for patients with HCC. Patients with HCC receiving TACE have various treatment efficacy and clinical outcomes [ 7 , 8 ] . Treatment response after first TACE course has been proved to be an important and robust prognostic factor for clinical outcomes, which also impacts the individual therapeutic strategy in HCC patients [ 32 ] . In recent years, several studies have explored CT or MRI-based radiomics for the prediction of tumor response of HCC after TACE [ 33 – 37 ] . Chen et al. [ 33 ] developed a CT-based radiomics model on tumoral region for predicting TACE response, and their model merely showed moderate predictive performance with the AUCs of 0.760 and 0.720 in the training and testing cohorts, respectively. MRI-based radiomics might be more promising for response estimation due to better soft-tissue contrast and resolution in MR images. According to the related studies of Sun [ 34 ] , Kuang [ 35 ] , Kong [ 36 ] , and Liu [ 37 ] , they constructed radiomics models based on preoperative single MRI sequence or multiparametric MRI (MP-MRI) to predict tumor response of HCC patients receiving TACE, and the AUCs ranged from 0.692 to 0.866 in the validation cohort; however, their studies only focused on the intratumoral radiomics features of the HCC. Pathologically, peritumoral parenchyma is representative of cancerous heterogeneity, and the crucial information can be indicated by changes in the area surrounding tumors, such as biological aggressiveness, microinvasion, and micrometastasis [ 20 , 23 , 38 ] ; thus, accurate evaluation of the neighboring tissue around tumors may also be useful in predicting treatment response and prognosis of TACE in patients with HCC. Previous studies have reported that the regions on intratumoral and 3 mm peritumoral, intratumoral and 5 mm peritumoral, and intratumoral and 10 mm peritumoral can provide valuable information for prognosis prediction in HCC [ 23 , 24 , 39 ] . In addition, according to the practice guidelines for the pathological diagnosis of primary liver cancer (2015 update) [ 40 ] , the liver tissues within 10 mm surrounding the tumor are defined as the adjacent areas around the cancer, where the probability of MVI is high. Therefore, our study focused on predicting treatment response, and we selected the most stable and predictive radiomics features from intratumoral, 3 mm, 5 mm and 10 mm peritumoral regions based on preoperative CE-MR images for radiomics models construction, which can quantitatively assess the heterogeneity and invasiveness of intratumoral and peritumoral tissues in a non-invasive way. In the present study, the PTR (3 mm) and PTR (5 mm) radiomics models showed comparable performance compared with the TR model, which indicated that peritumoral tissues were possess of a clinical value in assessing treatment response. Hu et al. [ 41 ] reached a similar result that the intratumoral radiomics model based on enhanced CT images obtained the equivalent efficacy compared with peritumoral radiomics model in predicting the response to neoadjuvant radiotherapy and chemotherapy in esophageal cancer with the AUCs of 0.730 and 0.734, respectively. The radiomics features contributed to the peritumoral models construction in the study were most derived from the AP images. This finding was in agreement with previous studies, in which the presence of peritumoral enhancement in AP images indicated more aggressive biological behavior [ 23 , 42 ] . Additionally, the PTR (10 mm) radiomics model showed lower predictive performance than the PTR (3 mm) and PTR (5 mm) models. This may be due to the decrease in the number of microsatellites in the farther peritumoral area and inclusion of the peritumoral normal hepatic parenchyma [ 39 ] . We further combined intratumoral and peritumoral rad-scores to establish T-PTR (3 mm), T-PTR (5 mm), and T-PTR (10 mm) radiomics models for predicting TACE response, and the T-PTR (3 mm) radiomics model achieved the best-performing performance among the seven radiomics models mentioned above. A similar study reported by Kim et al. [ 39 ] found that the radiomics model with intratumoral and 3 mm border extension showed improved performance than the radiomics model with intratumoral and 5 mm border extension for predicting ER of HCC after curative resection. The T-PTR (3 mm) radiomics model demonstrated better predictive performance compared with the TR radiomics model, which indicated that peritumoral radiomics might potentially enhance the ability of intratumoral radiomics for TACE response prediction. This might be interpreted that arterial peritumoral enhancement and irregular margin presented in the peritumoral area are independent predictors of prognosis in HCC patients [ 23 , 39 ] . Only one of the published studies, conducted MRI-based radiomics on intratumoral and peritumoral regions for the prediction of TACE prognosis [ 23 ] . In their study, the radiomics models based on the entire tumor volumetric of AP (AP ETV ), PVP ETV , and the border extensions of 1 mm, 3 mm, and 5 mm on the PVP (PVP B1 , PVP B3 , and PVP B5 ) were constructed to predict RFS of HCC patients after TACE. The best C-index results for PVP ETV and PVP B3 radiomics models were 0.727 and 0.714 in the validation dataset, respectively. However, the above research analyzed the image information of whole areas including intratumoral and peritumoral regions, and did not explore the individual contribution of the area around the tumor to the prediction model; thus, it was unable to determine the significance of the separate peritumoral region in predicting recurrence or prognosis. Compared with the previous study [ 23 ] , our study may have the following advantages: first, radiomics features derived from three-phase enhanced MR images might more fully reflect tumor heterogeneity and vascularization patterns, which is helpful for efficacy estimation; second, the individual peritumoral (3 mm, 5 mm, and 10 mm) radiomics models were constructed, and the proper and valuable peritumoral distance was determined; third, intratumoral combined peritumoral radiomics analysis may contain more prognostic information, and potentially provide a more accurate and effective approach of individualized efficacy prediction for HCC patients. In this study, during the construction of the clinical-radiological model, ALP value, tumor size, and satellite nodule were independent predictors associated with treatment response of HCC after TACE. Previous researches on TACE clarified that a higher ALP value was an independent risk factor for unfavourable OS [ 43 , 44 ] . Our study showed that abnormal ALP value was a significant predictor for poor response of HCC. Serum ALP levels are usually elevated in patients with liver diseases and thus may reflect the status of liver injury. In addition, ALP has already been included in the Chinese University Prognostic Index, a HCC staging system that assigns a score of 3 when ALP is > 200 IU/L, indicating the potential role of ALP in predicting the prognosis of HCC patients [ 45 ] . Tumor size has been broadly recognized as a major predictive factor of treatment response for TACE [ 10 , 32 ] . Larger tumors usually have more satellite lesions or daughter nodules making it difficult for TACE to achieve CR [ 46 ] . In our study, maximal tumor size > 5 cm was a significant predictive factor for NR, a result similar to the study by Jeong et al. [ 47 ] , who reported that tumors > 5 cm were independently associated with failure to achieve CR after TACE. Several studies reported that satellite nodule surrounding the main tumor was closely related to tumor grade, MVI, and ER after curative resection and OS of TACE therapy [ 22 , 37 , 48 ] . Our study demonstrated that the presence of satellite nodule was inclined to show NR to TACE, which was consistent with a recent study reported by Li et al. [ 11 ] , their study showed that satellite nodule was statistically significant between the OR and NR groups. This may be interpreted that the development of satellite nodule favors vascular invasion and also tumor recurrence [ 48 ] . We ultimately developed a combined nomogram by integrating the T-PTR (3 mm) rad-score with clinical-radiological risk indicators (ALP value, tumor size, and satellite nodule) for treatment response prediction. The combined nomogram achieved good calibration and the strongest predictive performance based on AUCs in the training (nomogram vs. radiomics model vs. clinical-radiological model, 0.910 vs. 0.884 vs. 0.789) and validation (nomogram vs. radiomics model vs. clinical-radiological model, 0.918 vs. 0.911 vs. 0.782) cohorts. The novel combined prediction nomogram was evaluated by a decision curve to clarify the clinical usefulness, which could offer insight into clinical outcomes on the basis of threshold probability, from which the net benefit could be derived [ 36 ] . Our results clearly demonstrated that the combined nomogram could obtain more net benefit than either the treat-all-patients or the treat-none-patients strategies across a wide range of threshold probabilities. Therefore, our novel nomogram may provide a reliable and efficient tool that enables visualized and personalized decision-making for the treatment management of patients with HCC. This study has several limitations. First, this was a retrospective study with a limited patient cohort at a single center, which may introduce selection bias. A larger database from multiple centers is further needed to externally validate the robustness and reproducibility of the prediction models. Second, ROIs were delineated manually by radiologists, and thus is time-consuming and prone to error and user variability; thus, it’s essential to develop an automatic and reliable liver tumor segmentation tool. Third, it should be noted that the MP-MRI data are not included in this study. In the future, we will attempt to develop a radiomics approach based on MP-MRI for response evaluation in HCC patients after TACE. Finally, we did not include genomics hallmarks related with TACE response, which may provide additional valuable information for therapeutic efficacy prediction. A future direction to consider would be identification of genetic features of HCCs that can be incorporated into the prediction schema. Conclusions In conclusion, intratumoral and peritumoral radiomics based on preoperative CE-MR images can enhance the ability of radiomics model in predicting tumor response to TACE. The combined nomogram which incorporated the rad-score and clinical-radiological risk factors provides an effective tool for the precise and individualized estimation of treatment response for HCC patients treated with TACE. The accurate identification of HCC patients who would receive benefit from upfront TACE might potentially help decision-making for subsequent treatment strategies. Abbreviations AASLD, American Association for the Study of Liver Disease; AFP, alpha-fetoprotein; ALT, alanine aminotransferase; AST, aspartate aminotransferase; ALB, albumin; ALP, alkaline phosphatase; AP, arterial phase; BCLC, Barcelona Clinic Liver Cancer; CR, complete response; CE-MRI, contrast-enhanced magnetic resonance imaging; CI, confidence interval; DP, delayed phase; DCA, decision curve analysis; ECOG, Eastern Cooperative Oncology Group; ER, early recurrence; GLCM, grey level co-occurrence matrix; GLRLM, grey level run length matrix; GLZSM, grey-level zone size matrix; GGT, γ-glutamyltranspeptadase; GBDT, gradient boosting decision tree; HCC, hepatocellular carcinoma; ICC, intraclass correlation coefficient; mRECIST, modified Response Evaluation Criteria in Solid Tumors; MVI, microvascular invasion; MP-MRI, multiparametric magnetic resonance imaging; NR, non-response; OR, objective response; OS, overall survival; PR, partial response; PD, progression disease; PVP, portal venous phase; PLT, platelet count; PT, prothrombin time; PTR, peritumoral region; Rad-score, radiomics score; RFS, recurrence-free survival; SD, stable disease; TACE, transarterial chemoembolization; TBIL, total bilirubin; T-PTR, intratumoral and peritumoral region; TR, intratumoral region. Declarations Ethics approval and consent to participate This retrospective study was approved by the Institutional Review Board of the First Affiliated Hospital of Dalian Medical University, and was carried out in accordance with the Declaration of Helsinki. The requirement for the informed consent was waived. Consent for publication Not applicable. Availability of data and materials All data generated or analysed during this study are included in supplementary material of this article. Competing interests None. Funding The study was supported by the National Natural Science Foundation of China (No. 61971091). Authors' contributions Conceptualization, Y.Z., J.Z., and A.L.L.; Methodology, Y.Z. and Y.G.; Software, Y.G.; Validation, Y.Z., N.W., and Q.H.X.; Formal analysis, Y.Z., N.W., Y.H.L., and J.H.L.; Investigation, Y.Z., J.Z., N.W., Q.H.X., Y.H.L., J.H.L., X.Y.Z., A.L.C., and L.H.C; Resources, Y.Z., J.Z., and N.W.; Data curation, Y.Z., N.W., Q.H.X., J.H.L., and Q.H.Z.; Writing-original draft preparation, Y.Z. and J.Z.; Writing-review and editing, F.W., Y.G., and A.L.L.; Visualization, Y.Z., L.J.S, and Y.G.; Supervision, A.L.L.; Project administration, Q.W.S. and A.L.L.; Funding acquisition, A.L.L.. All authors have read and agreed to the published version of the manuscript. Acknowledgements Not applicable. 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Supplementary Files SupplementaryMaterial.doc Cite Share Download PDF Status: Published Journal Publication published 24 Oct, 2023 Read the published version in BMC Cancer → Version 1 posted Editorial decision: Major revision 05 Apr, 2023 Reviews received at journal 24 Mar, 2023 Reviewers agreed at journal 22 Mar, 2023 Reviewers invited by journal 09 Mar, 2023 Editor assigned by journal 09 Dec, 2022 Editor invited by journal 30 Nov, 2022 Submission checks completed at journal 30 Nov, 2022 First submitted to journal 21 Oct, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2190531","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":156205220,"identity":"0ed24252-666f-4b7b-b4f4-fed5e2dbde28","order_by":0,"name":"Ying Zhao","email":"","orcid":"","institution":"Department of Radiology, The First Affiliated Hospital, Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Zhao","suffix":""},{"id":156205222,"identity":"00241785-a8e7-49ad-9214-8e138fc53f40","order_by":1,"name":"Jian Zhang","email":"","orcid":"","institution":"Department of Interventional Radiology, The First Affiliated Hospital, Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Zhang","suffix":""},{"id":156205223,"identity":"bf0b2475-fa92-4dba-8b85-b5e918d245c8","order_by":2,"name":"Nan Wang","email":"","orcid":"","institution":"Department of Radiology, The First Affiliated Hospital, Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Nan","middleName":"","lastName":"Wang","suffix":""},{"id":156205224,"identity":"fc33c475-f952-4657-9d2c-901ed4bb2855","order_by":3,"name":"Qihao Xu","email":"","orcid":"","institution":"Department of Radiology, The First Affiliated Hospital, Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qihao","middleName":"","lastName":"Xu","suffix":""},{"id":156205225,"identity":"03c01252-833a-4cd8-8cdc-c9e6f38b0f22","order_by":4,"name":"Yuhui Liu","email":"","orcid":"","institution":"College of Medical Imaging, Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuhui","middleName":"","lastName":"Liu","suffix":""},{"id":156205226,"identity":"874e0d05-982c-4952-9c57-d5deb826580e","order_by":5,"name":"Jinghong Liu","email":"","orcid":"","institution":"Department of Radiology, The First Affiliated Hospital, Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jinghong","middleName":"","lastName":"Liu","suffix":""},{"id":156205227,"identity":"590b13e2-eb66-4076-89b2-043a936356af","order_by":6,"name":"Qinhe Zhang","email":"","orcid":"","institution":"Department of Radiology, The First Affiliated Hospital, Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qinhe","middleName":"","lastName":"Zhang","suffix":""},{"id":156205228,"identity":"07acb8b0-effa-424e-90a3-1912fac3c7af","order_by":7,"name":"Xinyuan Zhang","email":"","orcid":"","institution":"College of Medical Imaging, Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xinyuan","middleName":"","lastName":"Zhang","suffix":""},{"id":156205229,"identity":"7f44b0e0-f767-4faf-9447-c2a780b83879","order_by":8,"name":"Anliang Chen","email":"","orcid":"","institution":"Department of Radiology, The First Affiliated Hospital, Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Anliang","middleName":"","lastName":"Chen","suffix":""},{"id":156205230,"identity":"6bca9b8a-8676-4ddf-84a8-c60b2a62a2f6","order_by":9,"name":"Lihua Chen","email":"","orcid":"","institution":"Department of Radiology, The First Affiliated Hospital, Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lihua","middleName":"","lastName":"Chen","suffix":""},{"id":156205231,"identity":"3b4746a9-2cb7-4ae2-975e-22a8bb7766f5","order_by":10,"name":"Liuji Sheng","email":"","orcid":"","institution":"College of Medical Imaging, Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Liuji","middleName":"","lastName":"Sheng","suffix":""},{"id":156205232,"identity":"1c91f9d8-abdd-4ff6-9e45-478e0b36f881","order_by":11,"name":"Qingwei Song","email":"","orcid":"","institution":"Department of Radiology, The First Affiliated Hospital, Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qingwei","middleName":"","lastName":"Song","suffix":""},{"id":156205233,"identity":"63532aff-1cf3-454c-bb88-2c6d329849b7","order_by":12,"name":"Feng Wang","email":"","orcid":"","institution":"Department of Interventional Radiology, The First Affiliated Hospital, Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Wang","suffix":""},{"id":156205234,"identity":"a489c757-dfe9-4162-b826-17136613bb14","order_by":13,"name":"Yan Guo","email":"","orcid":"","institution":"GE Healthcare (China)","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Guo","suffix":""},{"id":156205235,"identity":"7e0d8fe3-082b-42d1-97b4-abe16af59e6f","order_by":14,"name":"Ailian Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYBACPmYgkVAgwcDA3mAAEmBsIKSFDazFAKiF5wCxWsAkSLFEArFa2HkMPzwwsJA3l3y8dTMPg43shgPMzx7gdxiPMdB8CcOds9PKbvMwpBlvOMBmbkBAiwFIC+OG2zlmQC2HEzcc4GGTIGTLD6AW+w03z4C0/CdKixnIlsQNN3hAWg4Qo4WtzAKoJXnDmbSym3MMko1nHmYzw6uFn//w5ps/KupsNxw/vO3Gmwo72b7jzc/wakEDoKBiJkH9KBgFo2AUjALsAAC0BUDVQ+4pmgAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Radiology, The First Affiliated Hospital, Dalian Medical University","correspondingAuthor":true,"prefix":"","firstName":"Ailian","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2022-10-21 12:29:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2190531/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2190531/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12885-023-11491-0","type":"published","date":"2023-10-24T15:02:18+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":29924473,"identity":"5b6fdd17-a159-4626-933c-bac89475681e","added_by":"auto","created_at":"2022-12-05 20:19:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":278440,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of patient recruitment.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-2190531/v1/4a8c989bf019fb3e6da2af36.png"},{"id":29924475,"identity":"003fb8b6-2e2a-4315-962b-564671123d9e","added_by":"auto","created_at":"2022-12-05 20:19:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1664844,"visible":true,"origin":"","legend":"\u003cp\u003eThe workflow of radiomics analysis in the present study. \u003cstrong\u003e(a)\u003c/strong\u003e Contrast-enhanced MR imaging was acquired. Tumors were manually delineated around the entire tumor outline on each axial slice of arterial phase (AP), portal venous phase (PVP), and delayed phase (DP) images, and peritumoral expansion (3 mm, 5 mm, and 10 mm) were automatically generated. \u003cstrong\u003e(b)\u003c/strong\u003e Total 1206 radiomics features on enhanced images were extracted. \u003cstrong\u003e(c)\u003c/strong\u003e Three steps of feature dimensionality reduction were applied for all extracted features. Seven radiomics models based on intratumoral region (TR), peritumoral region (PTR), and TR combined PTR radiomics scores were constructed using logistic regression analysis. A clinical-radiological model and a combined model incorporating the optimal radiomics score and clinical-radiological independent risk factors were constructed. \u003cstrong\u003e(d)\u003c/strong\u003eThe combined nomogram was presented to provide a more understandable treatment response measurement for individualized evaluation, followed by receiver operating curve analysis, calibration curve, and decision curve analysis.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-2190531/v1/e67ee22b94fca7053167ca9b.png"},{"id":29925646,"identity":"be150d81-3a4e-4ce6-b0f9-d4b7f0af9bd1","added_by":"auto","created_at":"2022-12-05 20:27:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":61403,"visible":true,"origin":"","legend":"\u003cp\u003eThe combined nomogram incorporated alkaline phosphatase (ALP), tumor size, satellite nodule, and radiomics score (rad-score).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-2190531/v1/6cb132086a3f7e8e359eebbb.png"},{"id":29924471,"identity":"e5f4793f-5a28-4ddc-a70e-b9f9f84ad9dc","added_by":"auto","created_at":"2022-12-05 20:19:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":377845,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves for the intratumoral and peritumoral region (T-PTR) (3 mm) radiomics model, clinical-radiological model, and combined nomogram in the training cohort \u003cstrong\u003e(A)\u003c/strong\u003e and the validation cohort \u003cstrong\u003e(B)\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-2190531/v1/796514559413b3d6bc3d546d.png"},{"id":29925647,"identity":"c70935b6-5973-42cd-82c5-2172f5eea68a","added_by":"auto","created_at":"2022-12-05 20:27:45","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":269712,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curves of the combined nomogram in the training cohort \u003cstrong\u003e(A)\u003c/strong\u003e and the validation cohort \u003cstrong\u003e(B)\u003c/strong\u003e. The x-axis represents the predicted probability, and the y-axis represents the actual result. The diagonal solid line indicates the ideal prediction by a perfect model. The dashed line indicates the predictive performance of the model. If the dashed line is closer to the diagonal solid line, it means a better prediction.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-2190531/v1/b83e5f3ecd807a3243e558b4.png"},{"id":29926309,"identity":"f2275e7a-5a83-4962-a508-844b7ee55e06","added_by":"auto","created_at":"2022-12-05 20:35:45","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":486794,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve analysis for the intratumoral and peritumoral region (T-PTR) (3 mm) radiomics model, clinical-radiological model, and combined nomogram in the training cohort \u003cstrong\u003e(A)\u003c/strong\u003eand the validation cohort \u003cstrong\u003e(B)\u003c/strong\u003e. The y-axis represents the net benefit, and the x-axis represents the threshold probability. The (T-PTR) (3 mm) radiomics model, clinical-radiological model, and combined nomogram obtained more net benefits than either the treat-all-patients scheme or the treat-none-patients scheme for most of the threshold probabilities for predicting treatment response to TACE in patients with HCC.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-2190531/v1/3f2b84d4fb7987a75012a319.png"},{"id":45454011,"identity":"9dc3e825-a653-4c35-9396-57348cc841ed","added_by":"auto","created_at":"2023-10-30 15:08:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1563116,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2190531/v1/fd35c7b3-b092-46cd-90fc-da243164a013.pdf"},{"id":29924478,"identity":"b6b18293-6eee-42bb-80f3-245d19310590","added_by":"auto","created_at":"2022-12-05 20:19:54","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":170845111,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.doc","url":"https://assets-eu.researchsquare.com/files/rs-2190531/v1/627a18efd4afb02249c59a18.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Intratumoral and Peritumoral Radiomics Based on Contrast-enhanced MRI for Preoperatively Predicting Treatment Response of Transarterial Chemoembolization in Hepatocellular Carcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHepatocellular carcinoma (HCC) is the most common liver malignancy and the third leading cause of death among various cancers\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e, with more than 850,000 associated deaths reported annually worldwide\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Liver transplantation and resection are the curative therapies for HCC, and surgical excision is considered as the preferred treatment for HCC patients at early stage\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Unfortunately, the majority of HCC patients are not suitable for curative resection at the time of diagnosis because of poor liver function, multifocal disease, vascular involvement, and extrahepatic spread\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Transarterial chemoembolization (TACE) is widely used as a bridge to liver transplantation, or as the standard treatment for patients with intermediate HCC\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Nevertheless, the therapeutic efficacy of TACE varies greatly between patients and patients because the biological behavior of HCC is highly heterogeneous\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Several studies have evidenced that the overall response rates following TACE range from 15\u0026ndash;85% and the cumulative rates of local tumor progression at 1, 3, and 5 years are 33%, 52%, and 73%, respectively\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Therefore, it is beneficial to preoperatively estimate tumor response to TACE treatment and determine the therapeutic efficacy to aid in guiding subsequent therapeutic strategies to improve overall survival (OS) of HCC patients.\u003c/p\u003e \u003cp\u003eMagnetic resonance imaging (MRI)-based evaluations that are noninvasive and repeatable can be used to preoperatively assess the tumor response, which take an advantage of depicting more soft-tissue characteristics than computed tomography (CT) and avoiding ionizing radiation\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Regarding the pretherapeutic imaging features of HCC, several scholars have reported that larger lesion diameter, irregular margin, arterial peritumoral enhancement, satellite nodule, and quantitative functional parameters of diffusion weighted imaging (DWI), intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), and dynamic contrast enhanced magnetic resonance imaging (DCE-MRI), are found to be associated with therapeutic efficacy of TACE treatment\u003csup\u003e[\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13 CR14\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Although these imaging characteristics are encouraging, they are not sufficient for individual evaluations of tumor response to TACE, and the ability to predict TACE efficacy in HCC is limited when a high degree of tumor heterogeneity.\u003c/p\u003e \u003cp\u003eRadiomics is an emerging methodology, with the goal of going beyond size or human-eye based semantic descriptors of tumors, to enable the non-invasive extraction of high-throughput quantitative data from medical images and to explore their correlation with clinical outcomes\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. This approach would capture the three-dimensional (3D) information of the entire tumor and has been proved effective in characterizing the heterogeneity of the tumor by acting as a whole tumor virtual biopsy\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Numerous studies have demonstrated that radiomics-based models effectively identify the diagnosis and pathological characteristics or predict therapeutic efficacy and prognosis of cancer patients for clinical decision-making\u003csup\u003e[\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Recently, there has been increasing interest in evaluating radiomics patterns of the region surrounding the visible tumor\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Recurrence or metastasis of HCC is mainly intrahepatic, indicating that the peritumoral liver tissue may be a favorable soil for the spreading hepatoma cells\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Several scholars have reported that HCC patients with microvascular invasion (MVI), epithelial cell adhesion molecule (EpCAM), programmed death ligand 1 (PD-L1) expression, and higher CD68\u0026thinsp;+\u0026thinsp;cell density in peritumoral tissues have a significantly higher risk of recurrence or metastasis and cancer-related death\u003csup\u003e[\u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e; thus, peritumoral tissues might have valuable predictive information of HCC prognosis. Several recent studies have focused on peritumoral radiomics for predicting MVI, early recurrence (ER), recurrence-free survival (RFS) of HCC patients after resection or TACE\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e; however, the value of intratumoral and peritumoral radiomics based on MRI in predicting treatment response of HCC after TACE remains unknown.\u003c/p\u003e \u003cp\u003eTherefore, the present study aimed to determine whether radiomics assessment of HCC peritumoral regions based on contrast-enhanced MR (CE-MR) images could provide valuable information about TACE response and enhance the ability of intratumoral radiomics to predict treatment efficacy of TACE in patients with HCC.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003e This retrospective study was approved by the Institutional Review Board of the First Affiliated Hospital of Dalian Medical University and the requirement for informed consent was waived due to the retrospective nature of the study.\u003c/p\u003e \u003cp\u003eFrom April 2008 to February 2022, 343 consecutive patients with HCC who underwent CE-MRI examination within two weeks before conventional TACE at our institution were recruited. HCCs were diagnosed by pathology or with reference to American Association for the Study of Liver Disease (AASLD) guidelines\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. The exclusion criteria were: (1) previous oncological treatment, including liver resection, radiofrequency ablation (RFA), or chemotherapy (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;32); (2) diffuse or infiltrative HCCs or the largest lesion size\u0026thinsp;\u0026lt;\u0026thinsp;1cm (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;16); (3) extrahepatic metastasis or portal vein occlusion (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10); (4) unavailability of hepatic-arterial CE-MR imaging within 3 months after TACE treatment (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;145); (5) poor image quality (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the flowchart of patient recruitment, and 138 patients were enrolled in this study and randomly divided into a training cohort (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;96) and a validation cohort (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;42) at a ratio of 7:3.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePretherapeutic clinical characteristics including age, gender, history of hepatitis B or C, alpha-fetoprotein (AFP), alanine aminotransferase (ALT), aspartate aminotransferase (AST), γ-glutamyltranspeptadase (GGT), alkaline phosphatase (ALP), total bilirubin (TBIL), albumin (ALB), platelet count (PLT), prothrombin time (PT), Child-Pugh class, Eastern Cooperative Oncology Group (ECOG) performance status, and Barcelona Clinic Liver Cancer (BCLC) stage for each patient were retrospectively collected within 1 week before TACE.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMRI Protocol\u003c/h2\u003e \u003cp\u003eAll MRI examinations were performed with 1.5T or 3.0T MR scanner (Signa, HDXT, GE Healthcare) with a phased-array 8-channel sensitivity encoding abdominal coil. The CE-MRI examination was performed using the liver acquisition with volume acceleration (LAVA) protocol with fat-suppressed T1-weighted 3D fast-spoiled gradient-recalled echo sequence. The contrast enhanced images consisted of arterial phase (AP), portal venous phase (PVP), and delayed phase (DP) images, which were obtained at 40 s, 70 s, and 90 s, respectively, after the start of contrast injection of Gd-diethylenetriamine pentaacetic acid (Gd-DTPA) (Bayer Schering Pharma AG, Germany) at a patient weight-dependent dose of 0.1 mmol/kg with an injection rate of 2.5 mL/s through a median cubital vein. Other MRI sequences were also performed, including in- and opposed-phase fast-spoiled gradient-recalled echo T1-weighted imaging (T1WI) and fat-suppressed fast spin-echo T2-weighted imaging (T2WI). The detailed scanning parameters are shown in \u003cb\u003eSupplementary Data S1\u003c/b\u003e.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eImage Analysis\u003c/h2\u003e \u003cp\u003eTwo radiologists (reader 1, Y.Z. and reader 2, N.W., with 8-year and 7-year experience in abdominal MRI) independently analyzed pretherapeutic MR images, and they were aware of the diagnosis of HCC but blinded to clinical information and imaging report. The radiologists evaluated the following imaging traits for each patient: (1) tumor size; (2) tumor number; (3) tumor margin; (4) intratumoral necrosis; (5) intratumoral hemorrhage; (6) intratumoral fat; (7) tumor encapsulation; (8) arterial peritumoral enhancement; (9) satellite nodule; (10) internal arteries; (11) radiological cirrhosis. Tumor diameter was recorded as mean value, and any discrepancy in imaging feature assessment was resolved by means of reevaluation by another senior radiologist (J.H.L., with 20-year experience in abdominal MRI). Detailed definitions of these imaging features are described in \u003cb\u003eSupplementary Data S2\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eTACE Procedure and Treatment Response Assessment\u003c/h2\u003e \u003cp\u003eAll conventional TACE procedures were carried out by interventional radiologists with no less than 10 years of clinical experience. The Seldinger technique was used to puncture the right femoral artery, and a 5F catheter (RH catheter, Terumo, Tokyo, Japan) was inserted for celiac and hepatic artery angiography in evaluation of the vascular anatomy and tumor burden. After superselective catheterization of hepatic artery feeding the tumor, an emulsion mixture of lobaplatin, raltitrexed, and Lipiodol was injected using a 2.7F microcatheter (Progreat microcatheter, Hengrui Pharmaceutical Co. Ltd., Lianyungang, China), followed by embolization of the tumor-feeding artery using blank microsphere (100\u0026ndash;300 \u0026micro;m) or gelatin sponge particles (1\u0026ndash;2 mm). The TACE procedure was stopped when tumor staining completely disappeared and regional arterial blood flow stopped.\u003c/p\u003e \u003cp\u003eThe modified Response Evaluation Criteria in Solid Tumors (mRECIST 1.1) criteria was utilized to assess the tumor response in patients with HCC based on pre- and post-therapeutic arterial MR images. Tumor response was classified into four categories according to the mRECIST system as follows: complete response (CR), partial response (PR), stable disease (SD), and progression disease (PD)\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. In the present study, all patients were divided into the objective response (OR) group (CR and PR patients) and the non-response (NR) group (SD and PD patients).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003eTumor Segmentation and Radiomics Feature Extraction\u003c/h2\u003e \u003cp\u003ePretherapeutic CE-MR images were exported from the picture archiving and communication system (PACS) and then used for tumor segmentation and radiomics feature extraction. The AK software (Artificial Intelligence Kit, Version 3.2.5, GE Healthcare) was used to process the images before segmenting the tumor. AP, PVP, and DP images were resampled to a uniform voxel size of 1 \u0026times; 1 \u0026times; 1 mm via linear interpolation algorithm to standardize the voxel spacing\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Intensity normalization of images was performed to correct the scanner effect. Tumor segmentation was performed by manually delineating the region of interest (ROI) along the tumor contour on each axial slice of AP, PVP, and DP images using an open-source software (ITK-SNAP, version 3.6.0, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.itksnap.org/\u003c/span\u003e\u003cspan address=\"http://www.itksnap.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The ROI was required to include capsule surrounding the tumor and to exclude tumor surrounding vessels, and then every ROI was automatically merged into volume of interest (VOI). Notably, in terms of multifocal HCCs, the largest nodule was selected as the delineated lesion. To capture radiomics features from the tumor periphery, the VOIs of peritumoral region (PTR) were generated by automatically expanding 3 mm, 5 mm, and 10 mm from the lesion border using AK software. If the ROI was beyond the parenchyma of the liver after the expansion, the portion beyond the parenchyma was removed manually.\u003c/p\u003e \u003cp\u003eRadiomics features were extracted using AK software for intratumoral region (TR), PTR (3 mm), PTR (5 mm), and PTR (10 mm). A total of 1206 radiomics features were extracted for each VOI (based on AP, PVP, and DP images). The extracted radiomics features included: 42 histograms, 15 form factors, 10 Haralick features, 144 gray level co-occurrence matrix (GLCM) with an offset of 1/4/7, 180 gray level run length matrix (GLRLM) with an offset of 1/4/7, and 11 grey-level zone size matrix (GLZSM). Details of radiomics features are listed in \u003cb\u003eSupplementary Data S3\u003c/b\u003e. Z-score normalization of radiomics features was performed to reduce the bias caused by different dimensions. The workflow of the radiomics analysis is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eInter- and intraclass correlation coefficients (ICCs) were used to assess the reproducibility of each radiomics feature extracted from 30 randomly chosen patients. To assess the interobserver reproducibility, the ROI delineation was performed by two abdominal radiologists (readers 1 and 2, Y.Z. and N.W.) independently who were blinded to all patients\u0026rsquo; information. To evaluate the intraobserver reproducibility, reader 1 repeated the ROI delineation and feature extraction at a 1-month interval. Finally, reader 1 completed the remaining image segmentation and feature extraction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003eFeature Selection and Radiomics Model Construction\u003c/h2\u003e \u003cp\u003eTo remove potentially redundant features and decrease data dimensions, we followed a three-step procedure to identify the most predictive radiomics features. First, the radiomics features with ICCs of both interobserver and intraobserver greater than 0.80 indicated satisfactory reproducibility and high robustness and were selected for further analysis. Second, the redundant features with a correlation coefficient higher than 0.90 were eliminated following by the Spearman\u0026rsquo;s rank correlation test. Finally, the gradient boosting decision tree (GBDT) algorithm was applied to determine the top-ranked and most valuable features for predicting the tumor response. GBDT was proposed as a tree-based algorithm based on a greedy strategy (called gradient boosting) that evaluates the importance of a texture feature through the time it used as branching point for the tree\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. After those steps, TR, PTR (3 mm), PTR (5 mm), and PTR (10 mm) radiomics models were separately established using logistic regression algorithm with 5-fold cross-validation. The radiomics score (Rad-score) was calculated for each patient using a linear combination of the selected features that were weighted by their respective coefficients. Finally, three integrated radiomics models, including intratumoral and peritumoral region (T-PTR) (3 mm), T-PTR (5 mm), and T-PTR (10 mm) radiomics models, were generated by logistic regression using TR rad-score and corresponding PTR rad-score. The optimal radiomics model with the highest area under the curve (AUC) was selected for further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eClinical-radiological Model Construction\u003c/h2\u003e \u003cp\u003eUnivariate analysis was used to identify significant variables among clinical-radiological characteristics associated with treatment response of HCC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Multivariate logistic regression analysis was performed to identify the independent risk factors for predicting tumor response. Odds ratio and 95% confidence interval (CI) were calculated for each risk factor. The clinical-radiological model was constructed using the chosen independent risk factors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eCombined Model Building and Nomogram Construction\u003c/h2\u003e \u003cp\u003eA combined model integrating clinical-radiological risk factors and the optimal rad-score was constructed using the proposed logistic regression method. To provide an individual predictive graphical presentation, the combined model was presented as a nomogram. The nomogram could help calculate the predicted probability of NR for each individual patient.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eThe Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test or Mann-Whitney \u003cem\u003eU\u003c/em\u003e-test were used to compare the continuous variables between the OR and NR groups as appropriate. The chi-squared test or Fisher\u0026rsquo;s exact test were used to assess the categorical variables as appropriate. Interobserver agreement of the radiological feature evaluation was qualified by Cohen kappa coefficient. Kappa values of 0.81\u0026ndash;1.00 indicated excellent agreement, 0.61\u0026ndash;0.80 signified substantial agreement, and 0.41\u0026ndash;0.60 denoted moderate agreement. Receiver operating characteristic curve (ROC) analysis was performed to evaluate the performance of each predictive model. The AUC, accuracy, sensitivity, and specificity were calculated. We compared the predictive performance between different models using the Delong\u0026rsquo;s test. Calibration curves were drawn to evaluate the degree of deviation between the predictions and actual outcomes with the Hosmer-Lemeshow test. Decision curve analysis (DCA) was performed to validate the clinical utility of the nomogram. All statistical analyses for the present study were performed with R software (version 3.6.1, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.R-project.org\u003c/span\u003e\u003cspan address=\"http://www.R-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv class=\"Section2\" id=\"Sec13\"\u003e\n \u003ch2\u003ePatient Characteristics\u003c/h2\u003e\n \u003cp\u003eIn total, 138 patients (mean age, 60.24\u0026thinsp;\u0026plusmn;\u0026thinsp;8.91 years; range, 40\u0026ndash;83 years; 115 male) were enrolled in this study. Among the 138 patients with HCC, 6 patients were determined by pathology and 132 patients were identified by specific imaging features according to the AASLD guidelines. On the basis of the mRECIST criteria, the patients for CR, PR, SD, and PD were 13 (9.4%), 60 (43.5%), 51 (37.0%), and 14 (10.1%), respectively. All patients underwent post-therapeutic MRI examination following by the initial TACE treatment, with a median interval of 44 days (range, 28\u0026ndash;90 days) between the first TACE and the follow-up MRI examination. The clinical-radiological data in the OR and NR groups are summarized in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. No significant difference was found in the clinical-radiological characteristics, except for tumor encapsulation between the training and validation cohorts.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003cstrong\u003eTable 1. Patient clinical-radiological characteristics\u003c/strong\u003e\n \u003cdiv style='margin:0in;text-align:justify;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\n \u003ctable style=\"float: width: 5.7e+2pt;border-collapse:collapse;border:none;margin-left:6.75pt;margin-right:6.75pt;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width:158.75pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;padding:0in 5.4pt 0in 5.4pt;height:14.15pt;\"\u003e\n \u003cp style='margin:0in;text-align:font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width:175.75pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;padding:0in 5.4pt 0in 5.4pt;height:14.15pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003eTraining cohort (\u003cem\u003en\u003c/em\u003e = 96)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:12.0pt;border:none;border-top:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:14.15pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width:175.75pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;padding:0in 5.4pt 0in 5.4pt;height:14.15pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003eValidation cohort (\u003cem\u003en\u003c/em\u003e = 42)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width:45.35pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;padding:0in 5.4pt 0in 5.4pt;height:14.15pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:65.2pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:25.5pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003eOR group\u003c/strong\u003e\u003c/p\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e(\u003cem\u003en\u003c/em\u003e = 51)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:65.2pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;padding:0in 5.4pt 0in 5.4pt;height:25.5pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003eNR group\u003c/strong\u003e\u003c/p\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e(\u003cem\u003en\u003c/em\u003e = 45)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:45.35pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;padding:0in 5.4pt 0in 5.4pt;height:25.5pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:12.0pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:25.5pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:65.2pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:25.5pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003eOR group\u003c/strong\u003e\u003c/p\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e(\u003cem\u003en\u003c/em\u003e = 22)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:65.2pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;padding:0in 5.4pt 0in 5.4pt;height:25.5pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003eNR group\u003c/strong\u003e\u003c/p\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e(\u003cem\u003en\u003c/em\u003e = 20)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:45.35pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;padding:0in 5.4pt 0in 5.4pt;height:25.5pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:158.75pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:14.15pt;\"\u003e\n \u003cp style='margin:0in;text-align:left;font-size:14px;font-family:\"Times New Roman\",serif;'\u003eAge (years, mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e60.29\u0026plusmn;8.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e60.42\u0026plusmn;10.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.35pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e0.947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:12.0pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:14.15pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e60.18\u0026plusmn;9.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e59.75\u0026plusmn;7.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.35pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e0.866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.35pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e0.820\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:158.75pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:14.15pt;\"\u003e\n \u003cp style='margin:0in;text-align:left;font-size:14px;font-family:\"Times New Roman\",serif;'\u003eGender (\u003cem\u003en\u003c/em\u003e, [%])\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:65.2pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:14.15pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:65.2pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:14.15pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.35pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e0.410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:12.0pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:14.15pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:65.2pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:14.15pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:65.2pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:14.15pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.35pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e0.890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.35pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:158.75pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:14.15pt;\"\u003e\n \u003cp style='margin:0in;text-align:left;font-size:14px;font-family:\"Times New Roman\",serif;text-indent:21.0pt;'\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2pt;border: none;padding: 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style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e31(60.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e17(37.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.35pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2pt;border: none;padding: 0in 5.4pt;height: 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style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.35pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e0.480\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:158.75pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:14.15pt;\"\u003e\n \u003cp style='margin:0in;text-align:left;font-size:14px;font-family:\"Times New Roman\",serif;text-indent:21.0pt;'\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e35(68.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e18(40.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.35pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e13(59.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e8(40.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.35pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.35pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:158.75pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:14.15pt;\"\u003e\n \u003cp style='margin:0in;text-align:left;font-size:14px;font-family:\"Times New Roman\",serif;text-indent:21.0pt;'\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e13(25.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e12(26.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.35pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e7(31.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e3(15.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.35pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.35pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:158.75pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:14.15pt;\"\u003e\n \u003cp style='margin:0in;text-align:left;font-size:14px;font-family:\"Times New Roman\",serif;text-indent:21.0pt;'\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e3(5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e15(33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.35pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e2(9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e9(45.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.35pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.35pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003eTable 1. Patient clinical-radiological characteristics (continued)\u003c/strong\u003e\u003c/p\u003e\n \u003cdiv style='margin:0in;text-align:justify;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\n \u003ctable style=\"float: width: 5.7e+2pt;border-collapse:collapse;border:none;margin-left:6.75pt;margin-right:6.75pt;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width:158.75pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;padding:0in 5.4pt 0in 5.4pt;height:14.15pt;\"\u003e\n \u003cp style='margin:0in;text-align:left;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width:175.75pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;padding:0in 5.4pt 0in 5.4pt;height:14.15pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003eTraining cohort (\u003cem\u003en\u003c/em\u003e = 96)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:12.0pt;border:none;border-top:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:14.15pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width:175.75pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;padding:0in 5.4pt 0in 5.4pt;height:14.15pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003eValidation cohort (\u003cem\u003en\u003c/em\u003e = 42)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width:45.35pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;padding:0in 5.4pt 0in 5.4pt;height:14.15pt;\"\u003e\n \u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:65.2pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:25.5pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003eOR group\u003c/strong\u003e\u003c/p\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e(\u003cem\u003en\u003c/em\u003e = 51)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:65.2pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;padding:0in 5.4pt 0in 5.4pt;height:25.5pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003eNR group\u003c/strong\u003e\u003c/p\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e(\u003cem\u003en\u003c/em\u003e = 45)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:45.35pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;padding:0in 5.4pt 0in 5.4pt;height:25.5pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:12.0pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:25.5pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:65.2pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:25.5pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003eOR group\u003c/strong\u003e\u003c/p\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e(\u003cem\u003en\u003c/em\u003e = 22)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:65.2pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;padding:0in 5.4pt 0in 5.4pt;height:25.5pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003eNR group\u003c/strong\u003e\u003c/p\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e(\u003cem\u003en\u003c/em\u003e = 20)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:45.35pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;padding:0in 5.4pt 0in 5.4pt;height:25.5pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:158.75pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:14.15pt;\"\u003e\n \u003cp style='margin:0in;text-align:left;font-size:14px;font-family:\"Times New Roman\",serif;'\u003eTumor size (\u003cem\u003en\u003c/em\u003e, [%])\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:65.2pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:14.15pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:65.2pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:14.15pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.35pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:12.0pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:14.15pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:65.2pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:14.15pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:65.2pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:14.15pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n 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style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:45.35pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:14.15pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:158.75pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:14.15pt;\"\u003e\n \u003cp style='margin:0in;text-align:left;font-size:14px;font-family:\"Times New Roman\",serif;'\u003eTumor encapsulation\u0026nbsp;(\u003cem\u003en\u003c/em\u003e, [%])\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd 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top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e18(90.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.35pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.35pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:158.75pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:14.15pt;\"\u003e\n \u003cp style='margin:0in;text-align:left;font-size:14px;font-family:\"Times New 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style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.35pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.35pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e0.464\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:158.75pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:14.15pt;\"\u003e\n \u003cp style='margin:0in;text-align:left;font-size:14px;font-family:\"Times New Roman\",serif;text-indent:21.0pt;'\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e18(35.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e25(55.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.35pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:12.0pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:14.15pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e5(22.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e11(55.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.35pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.35pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:158.75pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:14.15pt;\"\u003e\n \u003cp style='margin:0in;text-align:left;font-size:14px;font-family:\"Times New Roman\",serif;text-indent:21.0pt;'\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e33(64.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e20(44.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.35pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:12.0pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:14.15pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e17(77.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e9(45.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.35pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.35pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:158.75pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:14.15pt;\"\u003e\n \u003cp style='margin:0in;text-align:left;font-size:14px;font-family:\"Times New Roman\",serif;'\u003eRadiological cirrhosis\u0026nbsp;(\u003cem\u003en\u003c/em\u003e, [%])\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.35pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:12.0pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:14.15pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.35pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e0.372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.35pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e0.719\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:158.75pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:14.15pt;\"\u003e\n \u003cp style='margin:0in;text-align:left;font-size:14px;font-family:\"Times New Roman\",serif;text-indent:21.0pt;'\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e36(70.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e22(48.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.35pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:12.0pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:14.15pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e14(63.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e10(50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.35pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.35pt;border: none;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:158.75pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:14.15pt;\"\u003e\n \u003cp style='margin:0in;text-align:left;font-size:14px;font-family:\"Times New Roman\",serif;text-indent:21.0pt;'\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e15(29.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e23(51.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.35pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:12.0pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:14.15pt;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e8(36.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e10(50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.35pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.35pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 5.4pt;height: 14.15pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Times New Roman\",serif;'\u003eOR, objective response; NR, non-response; SD, standard deviation; AFP, alpha-fetoprotein; ALT, alanine aminotransferase; AST, aspartate aminotransferase; GGT, \u0026gamma;-glutamyltranspeptadas; ALP, alkaline phosphatase; TBIL, total bilirubin; ALB, albumin; PLT, platelet count; PT, prothrombin time; ECOG, Eastern Cooperative Oncology Group; BCLC, Barcelona Clinic Liver Cancer.\u003cspan style=\"font-size:16px;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003ch2\u003eRadiomics Model Development and Evaluation\u003c/h2\u003e\n \u003cp\u003eAfter inter- and intraobserver reproducibility analysis, the dimensions of feature spaces were 853 for VOI\u003csub\u003eTR\u003c/sub\u003e, 424 for VOI\u003csub\u003ePTR (3 mm)\u003c/sub\u003e, 452 for VOI\u003csub\u003ePTR (5 mm)\u003c/sub\u003e, and 716 for VOI\u003csub\u003ePTR (10 mm)\u003c/sub\u003e. Spearman\u0026rsquo;s rank correlation test allowed the selection of 100, 35, 43, and 90 features, respectively. GBDT revealed that the radiomics feature numbers ultimately consisted of 25, 14, 17, and 21 from the VOI\u003csub\u003eTR\u003c/sub\u003e, VOI\u003csub\u003ePTR (3 mm)\u003c/sub\u003e, VOI\u003csub\u003ePTR (5 mm)\u003c/sub\u003e, and VOI\u003csub\u003ePTR (10 mm)\u003c/sub\u003e, respectively, and were used for the radiomics model building (TR, PTR (3 mm), PTR (5 mm), and PTR (10 mm) models). Finally, three T-PTR radiomics models based on TR and PTR rad-scores were constructed. The calculation formulae for the rad-score are shown in \u003cstrong\u003eSupplementary Data S4\u003c/strong\u003e.\u003c/p\u003e\n \u003cp\u003eThe radiomics models demonstrated favorable discrimination in the both cohorts (AUC: training cohort, 0.810\u0026ndash;0.892; validation cohort, 0.793\u0026ndash;0.911). The TR, PTR (3mm), PTR (5mm) radiomics models showed comparative performance with the AUCs of 0.820 (95% CI, 0.705\u0026ndash;0.917), 0.823 (95% CI, 0.701\u0026ndash;0.927), and 0.823 (95% CI, 0.711\u0026ndash;0.924) in the validation cohort, and the predictive performance of these three models were slightly higher than that of the PTR (10 mm) model (AUC: 0.793; 95%CI: 0.673\u0026ndash;0.903). Compared with TR and PTR radiomics models, the T-PTR radiomics models (T-PTR (3 mm), T-PTR (5 mm), and T-PTR (10 mm) models) performed better in predicting tumor response with the AUCs of 0.911 (95%CI, 0.825\u0026ndash;0.975), 0.909 (95%CI, 0.817\u0026ndash;0.982), and 0.895 (95%CI, 0.815\u0026ndash;0.964) in the validation cohort; thus, we chose the T-PTR (3 mm) model as the best-performing radiomics model for further analysis. ROC curves and discriminative performance of the seven radiomics models in the two cohorts are shown in \u003cstrong\u003eSupplementary Data S5\u003c/strong\u003e and Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab3\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAUC (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAccuracy\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDiscrimination performance of different predictive models\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eClinical-radiological model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.789 (0.709\u0026ndash;0.863)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e0.935\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.782 (0.660\u0026ndash;0.902)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.818\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eTR model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.836 (0.763\u0026ndash;0.903)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e0.840\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.820 (0.705\u0026ndash;0.917)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.682\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePTR (3 mm) model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.817 (0.740\u0026ndash;0.881)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e0.940\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.823 (0.701\u0026ndash;0.927)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.636\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePTR (5 mm) model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.810 (0.739\u0026ndash;0.880)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e0.877\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.823 (0.711\u0026ndash;0.924)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.545\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePTR (10 mm) model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.852 (0.785\u0026ndash;0.911)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.822\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e0.468\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.793 (0.673\u0026ndash;0.903)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.636\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eT-PTR (3 mm) model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.884 (0.821\u0026ndash;0.936)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.822\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e0.633\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.911 (0.825\u0026ndash;0.975)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.818\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eT-PTR (5 mm) model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.883 (0.822\u0026ndash;0.934)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e0.661\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.909 (0.817\u0026ndash;0.982)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.955\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eT-PTR (10 mm) model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.892 (0.834\u0026ndash;0.942)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e0.945\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.895 (0.815\u0026ndash;0.964)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.818\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCombined nomogram\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.910 (0.854\u0026ndash;0.958)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.822\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e0.891\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.918 (0.831\u0026ndash;0.986)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.909\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eTR, intratumoral region; PTR, peritumoral region; T-PTR, intratumoral and peritumoral region; TC, training cohort; VC, validation cohort; AUC, area under the curve; CI, confidence interval.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cdiv class=\"Section3\" id=\"Sec15\"\u003e\n \u003ch2\u003eClinical-radiological Model Development and Evaluation\u003c/h2\u003e\n \u003cp\u003eInterobserver agreements on the radiological features were substantial to excellent (kappa-value range: 0.772\u0026ndash;1.000). Univariate and multivariate analyses of clinical-radiological characteristics for predicting treatment response in the training cohort are shown in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. The univariate analysis indicated that AST, GGT, ALP, PLT, ECOG, BCLC stage, tumor size, intratumoral necrosis, intratumoral hemorrhage, satellite nodule, internal arteries, and radiological cirrhosis were significant clinical-radiological factors for discriminating the OR and NR groups in the training cohort (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The multivariate logistic regression analysis demonstrated that ALP (odd ratio\u0026thinsp;=\u0026thinsp;5.744; 95% CI: 1.780\u0026ndash;18.532; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), tumor size (odd ratio\u0026thinsp;=\u0026thinsp;3.005; 95% CI: 1.154\u0026ndash;7.826; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024), and satellite nodule (odd ratio\u0026thinsp;=\u0026thinsp;9.865; 95% CI: 1.101\u0026ndash;88.370; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.041) were the independent risk factors for predicting NR in HCC patients. The clinical-radiological model was built by incorporating these three variables. The clinical-radiological model yielded the AUCs of 0.789 (95% CI, 0.709\u0026ndash;0.863) and 0.782 (95% CI, 0.660\u0026ndash;0.902) in the two cohorts, respectively (shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003cstrong\u003eTable 3. Univariate and multivariate analyses of clinical-radiological characteristics for predicting treatment response\u003c/strong\u003e\n \u003cdiv align=\"center\" style='margin:0in;text-align:justify;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\n \u003ctable style=\"width: 4.8e+2pt;border-collapse:collapse;border:none;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width:119.05pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:left;'\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width:175.75pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e\u003cstrong\u003eUnivariate analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:12.0pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width:175.75pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e\u003cstrong\u003eMultivariate analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:119.05pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e\u003cstrong\u003eOdd ratio (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:56.7pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e\u003cstrong\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:12.0pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:119.05pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e\u003cstrong\u003eOdd ratio (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e\u003cstrong\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:119.05pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:left;'\u003eAST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:119.05pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e2.553 (1.120 - 5.820)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:56.7pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:12.0pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:14px;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:119.05pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e\u003cspan style=\"font-size:14px;font-family:SimSun;\"\u003e\u0026mdash;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:56.7pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e\u003cspan style=\"font-size:14px;font-family:SimSun;\"\u003e\u0026mdash;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:119.05pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:left;'\u003eGGT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:119.05pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e3.640 (1.492 - 8.880)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:56.7pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:12.0pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:14px;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:119.05pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e\u003cspan style=\"font-size:14px;font-family:SimSun;\"\u003e\u0026mdash;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:56.7pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e\u003cspan style=\"font-size:14px;font-family:SimSun;\"\u003e\u0026mdash;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:119.05pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:left;'\u003eALP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:119.05pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e6.723 (2.246 - 20.122)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:56.7pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:12.0pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:14px;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:119.05pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e\u003cspan style=\"font-size:14px;\"\u003e5.744 (1.780 - 18.532)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:56.7pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e\u003cspan style=\"font-size:14px;\"\u003e0.003\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:119.05pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:left;'\u003ePLT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:119.05pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e2.491 (1.079 - 5.753)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:56.7pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:12.0pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:14px;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:119.05pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e\u003cspan style=\"font-size:14px;font-family:SimSun;\"\u003e\u0026mdash;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:56.7pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e\u003cspan style=\"font-size:14px;font-family:SimSun;\"\u003e\u0026mdash;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:119.05pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:left;'\u003eECOG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:119.05pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e5.297 (1.062 - 26.428)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:56.7pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:12.0pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:14px;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:119.05pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e\u003cspan style=\"font-size:14px;font-family:SimSun;\"\u003e\u0026mdash;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:56.7pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e\u003cspan style=\"font-size:14px;font-family:SimSun;\"\u003e\u0026mdash;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:119.05pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:left;'\u003eBCLC stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:119.05pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e2.716 (1.515 - 4.872)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:56.7pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:12.0pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:14px;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:119.05pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e\u003cspan style=\"font-size:14px;font-family:SimSun;\"\u003e\u0026mdash;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:56.7pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e\u003cspan style=\"font-size:14px;font-family:SimSun;\"\u003e\u0026mdash;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:119.05pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:left;'\u003eTumor size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:119.05pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e4.800 (2.023 - 11.392)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:56.7pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:12.0pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:14px;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:119.05pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e\u003cspan style=\"font-size:14px;\"\u003e3.005 (1.154 - 7.826)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:56.7pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e\u003cspan style=\"font-size:14px;\"\u003e0.024\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:119.05pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:left;'\u003eIntratumoral necrosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:119.05pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e3.281 (1.417 - 7.600)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:56.7pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:12.0pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:14px;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:119.05pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e\u003cspan style=\"font-size:14px;font-family:SimSun;\"\u003e\u0026mdash;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:56.7pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e\u003cspan style=\"font-size:14px;font-family:SimSun;\"\u003e\u0026mdash;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:119.05pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:left;'\u003eIntratumoral hemorrhage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:119.05pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e2.558 (1.082 - 6.044)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:56.7pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:12.0pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;margin-top:0in;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:14px;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:119.05pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e\u003cspan style=\"font-size:14px;font-family:SimSun;\"\u003e\u0026mdash;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:56.7pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e\u003cspan style=\"font-size:14px;font-family:SimSun;\"\u003e\u0026mdash;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:119.05pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:left;'\u003eSatellite nodule\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:119.05pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e16.176 (1.996 - 131.069)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:56.7pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:12.0pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;margin-top:0in;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:14px;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:119.05pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e9.865 (1.101 - 88.370)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:56.7pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:119.05pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:left;'\u003eInternal arteries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:119.05pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e2.292 (1.007 - 5.213)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:56.7pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:12.0pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;margin-top:0in;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:14px;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:119.05pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e\u003cspan style=\"font-size:14px;font-family:SimSun;\"\u003e\u0026mdash;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:56.7pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e\u003cspan style=\"font-size:14px;font-family:SimSun;\"\u003e\u0026mdash;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:119.05pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:left;'\u003eRadiological cirrhosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:119.05pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e0.399 (0.172 - 0.923)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:12.0pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;margin-top:0in;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:14px;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:119.05pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e\u003cspan style=\"font-size:14px;font-family:SimSun;\"\u003e\u0026mdash;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:19.85pt;\"\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:center;'\u003e\u003cspan style=\"font-size:14px;font-family:SimSun;\"\u003e\u0026mdash;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:14px;font-family:\"Times New Roman\",serif;margin:0in;text-align:justify;'\u003eThe clinical-radiological characteristics with \u003cem\u003eP\u003c/em\u003e value less than 0.05 in the univariate analysis are listed in the table. AST, aspartate aminotransferase; GGT, \u0026gamma;-glutamyltranspeptadase; ALP, alkaline phosphatase; PLT, platelet count; ECOG, Eastern Cooperative Oncology Group; BCLC, Barcelona Clinic Liver Cancer; CI, confidence interval.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec16\"\u003e\n \u003ch2\u003eCombined Model and Nomogram Development and Evaluation\u003c/h2\u003e\n \u003cp\u003eThe T-PTR (3 mm) rad-score, ALP, tumor size, and satellite nodule were considered as input variables for logistic regression to build the combined model. We chose a nomogram as the graphical representation of the best-performing combined model as shown in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. The combined nomogram yielded an AUC, an accuracy, a sensitivity, and a specificity of 0.910 (95% CI, 0.854\u0026ndash;0.958), 0.844, 0.822, and 0.863, respectively, for discriminating between the OR and NR groups in the training cohort and of 0.918 (95% CI, 0.831\u0026ndash;0.986), 0.857, 0.800, and 0.909, respectively, in the validation cohort. The discriminating performance of the combined nomogram in the training cohort was significantly superior to that of the clinical-radiological model (AUC, 0.910 vs. 0.789, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028), whereas there was no significant difference between the models in the validation cohort (AUC, 0.918 vs. 0.782, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.127). No significant differences were found between the T-PTR (3mm) radiomics model and combined nomogram (training cohort, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.577; validation cohort, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.918) and between the T-PTR (3mm) radiomics model and clinical-radiological model (training cohort, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.094; validation cohort, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.139). Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the predictive performance of the constructed models. ROC curves for clinical-radiological model, T-PTR (3mm) radiomics model, and combined nomogram in the both cohorts are shown in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA and \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB. The Delong\u0026rsquo;s test of different predictive models in the both cohorts is shown in \u003cstrong\u003eSupplementary Data S6\u003c/strong\u003e.\u003c/p\u003e\n \u003cp\u003eCalibration curves of the combined nomogram for the probability of treatment response demonstrated good agreements between prediction and observation in the training and validation cohorts (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA, \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB). The Hosmer-Lemeshow test yielded non-significant results in the both cohorts (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.386 and 0.343), which suggested a satisfying fit of the nomogram. The DCA indicated that the combined nomogram obtained more net benefits than the clinical-radiological model, T-PTR (3mm) radiomics model, and \u0026ldquo;treat-all\u0026rdquo; or \u0026ldquo;treat-none\u0026rdquo; strategies for most of the threshold probabilities in the training and validation cohorts (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA, \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the study, we analyzed various radiomics models for intratumoral, peritumoral, and intratumoral combined peritumoral derived from preoperative CE-MR images for response estimation after TACE. Our study confirms that the radiomics models combining intratumoral and peritumoral are better than the intratumoral radiomics model in predicting TACE response of HCC patients. Furthermore, we developed and validated a combined nomogram that integrated the clinical-radiological risk factors and the optimal T-PTR (3mm) rad-score for predicting HCC response to TACE, demonstrating satisfactory performance, calibration, and clinical utility. The proposed radiomics approach successfully predicted therapeutic efficacy of TACE and may facilitate individualized treatment decision-making for patients with HCC.\u003c/p\u003e \u003cp\u003ePatients with HCC receiving TACE have various treatment efficacy and clinical outcomes\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Treatment response after first TACE course has been proved to be an important and robust prognostic factor for clinical outcomes, which also impacts the individual therapeutic strategy in HCC patients\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. In recent years, several studies have explored CT or MRI-based radiomics for the prediction of tumor response of HCC after TACE\u003csup\u003e[\u003cspan additionalcitationids=\"CR34 CR35 CR36\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e–\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. Chen et al.\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e developed a CT-based radiomics model on tumoral region for predicting TACE response, and their model merely showed moderate predictive performance with the AUCs of 0.760 and 0.720 in the training and testing cohorts, respectively. MRI-based radiomics might be more promising for response estimation due to better soft-tissue contrast and resolution in MR images. According to the related studies of Sun\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e, Kuang\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e, Kong\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e, and Liu\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e, they constructed radiomics models based on preoperative single MRI sequence or multiparametric MRI (MP-MRI) to predict tumor response of HCC patients receiving TACE, and the AUCs ranged from 0.692 to 0.866 in the validation cohort; however, their studies only focused on the intratumoral radiomics features of the HCC. Pathologically, peritumoral parenchyma is representative of cancerous heterogeneity, and the crucial information can be indicated by changes in the area surrounding tumors, such as biological aggressiveness, microinvasion, and micrometastasis\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e; thus, accurate evaluation of the neighboring tissue around tumors may also be useful in predicting treatment response and prognosis of TACE in patients with HCC.\u003c/p\u003e \u003cp\u003ePrevious studies have reported that the regions on intratumoral and 3 mm peritumoral, intratumoral and 5 mm peritumoral, and intratumoral and 10 mm peritumoral can provide valuable information for prognosis prediction in HCC\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. In addition, according to the practice guidelines for the pathological diagnosis of primary liver cancer (2015 update)\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e, the liver tissues within 10 mm surrounding the tumor are defined as the adjacent areas around the cancer, where the probability of MVI is high. Therefore, our study focused on predicting treatment response, and we selected the most stable and predictive radiomics features from intratumoral, 3 mm, 5 mm and 10 mm peritumoral regions based on preoperative CE-MR images for radiomics models construction, which can quantitatively assess the heterogeneity and invasiveness of intratumoral and peritumoral tissues in a non-invasive way. In the present study, the PTR (3 mm) and PTR (5 mm) radiomics models showed comparable performance compared with the TR model, which indicated that peritumoral tissues were possess of a clinical value in assessing treatment response. Hu et al.\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e reached a similar result that the intratumoral radiomics model based on enhanced CT images obtained the equivalent efficacy compared with peritumoral radiomics model in predicting the response to neoadjuvant radiotherapy and chemotherapy in esophageal cancer with the AUCs of 0.730 and 0.734, respectively. The radiomics features contributed to the peritumoral models construction in the study were most derived from the AP images. This finding was in agreement with previous studies, in which the presence of peritumoral enhancement in AP images indicated more aggressive biological behavior\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. Additionally, the PTR (10 mm) radiomics model showed lower predictive performance than the PTR (3 mm) and PTR (5 mm) models. This may be due to the decrease in the number of microsatellites in the farther peritumoral area and inclusion of the peritumoral normal hepatic parenchyma\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. We further combined intratumoral and peritumoral rad-scores to establish T-PTR (3 mm), T-PTR (5 mm), and T-PTR (10 mm) radiomics models for predicting TACE response, and the T-PTR (3 mm) radiomics model achieved the best-performing performance among the seven radiomics models mentioned above. A similar study reported by Kim et al.\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e found that the radiomics model with intratumoral and 3 mm border extension showed improved performance than the radiomics model with intratumoral and 5 mm border extension for predicting ER of HCC after curative resection. The T-PTR (3 mm) radiomics model demonstrated better predictive performance compared with the TR radiomics model, which indicated that peritumoral radiomics might potentially enhance the ability of intratumoral radiomics for TACE response prediction. This might be interpreted that arterial peritumoral enhancement and irregular margin presented in the peritumoral area are independent predictors of prognosis in HCC patients\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. Only one of the published studies, conducted MRI-based radiomics on intratumoral and peritumoral regions for the prediction of TACE prognosis\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. In their study, the radiomics models based on the entire tumor volumetric of AP (AP\u003csup\u003eETV\u003c/sup\u003e), PVP\u003csup\u003eETV\u003c/sup\u003e, and the border extensions of 1 mm, 3 mm, and 5 mm on the PVP (PVP\u003csup\u003eB1\u003c/sup\u003e, PVP\u003csup\u003eB3\u003c/sup\u003e, and PVP\u003csup\u003eB5\u003c/sup\u003e) were constructed to predict RFS of HCC patients after TACE. The best C-index results for PVP\u003csup\u003eETV\u003c/sup\u003e and PVP\u003csup\u003eB3\u003c/sup\u003e radiomics models were 0.727 and 0.714 in the validation dataset, respectively. However, the above research analyzed the image information of whole areas including intratumoral and peritumoral regions, and did not explore the individual contribution of the area around the tumor to the prediction model; thus, it was unable to determine the significance of the separate peritumoral region in predicting recurrence or prognosis. Compared with the previous study\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e, our study may have the following advantages: first, radiomics features derived from three-phase enhanced MR images might more fully reflect tumor heterogeneity and vascularization patterns, which is helpful for efficacy estimation; second, the individual peritumoral (3 mm, 5 mm, and 10 mm) radiomics models were constructed, and the proper and valuable peritumoral distance was determined; third, intratumoral combined peritumoral radiomics analysis may contain more prognostic information, and potentially provide a more accurate and effective approach of individualized efficacy prediction for HCC patients.\u003c/p\u003e \u003cp\u003eIn this study, during the construction of the clinical-radiological model, ALP value, tumor size, and satellite nodule were independent predictors associated with treatment response of HCC after TACE. Previous researches on TACE clarified that a higher ALP value was an independent risk factor for unfavourable OS\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. Our study showed that abnormal ALP value was a significant predictor for poor response of HCC. Serum ALP levels are usually elevated in patients with liver diseases and thus may reflect the status of liver injury. In addition, ALP has already been included in the Chinese University Prognostic Index, a HCC staging system that assigns a score of 3 when ALP is \u0026gt; 200 IU/L, indicating the potential role of ALP in predicting the prognosis of HCC patients\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e. Tumor size has been broadly recognized as a major predictive factor of treatment response for TACE\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Larger tumors usually have more satellite lesions or daughter nodules making it difficult for TACE to achieve CR\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. In our study, maximal tumor size \u0026gt; 5 cm was a significant predictive factor for NR, a result similar to the study by Jeong et al.\u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e, who reported that tumors \u0026gt; 5 cm were independently associated with failure to achieve CR after TACE. Several studies reported that satellite nodule surrounding the main tumor was closely related to tumor grade, MVI, and ER after curative resection and OS of TACE therapy\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e. Our study demonstrated that the presence of satellite nodule was inclined to show NR to TACE, which was consistent with a recent study reported by Li et al.\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, their study showed that satellite nodule was statistically significant between the OR and NR groups. This may be interpreted that the development of satellite nodule favors vascular invasion and also tumor recurrence\u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe ultimately developed a combined nomogram by integrating the T-PTR (3 mm) rad-score with clinical-radiological risk indicators (ALP value, tumor size, and satellite nodule) for treatment response prediction. The combined nomogram achieved good calibration and the strongest predictive performance based on AUCs in the training (nomogram vs. radiomics model vs. clinical-radiological model, 0.910 vs. 0.884 vs. 0.789) and validation (nomogram vs. radiomics model vs. clinical-radiological model, 0.918 vs. 0.911 vs. 0.782) cohorts. The novel combined prediction nomogram was evaluated by a decision curve to clarify the clinical usefulness, which could offer insight into clinical outcomes on the basis of threshold probability, from which the net benefit could be derived\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. Our results clearly demonstrated that the combined nomogram could obtain more net benefit than either the treat-all-patients or the treat-none-patients strategies across a wide range of threshold probabilities. Therefore, our novel nomogram may provide a reliable and efficient tool that enables visualized and personalized decision-making for the treatment management of patients with HCC.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, this was a retrospective study with a limited patient cohort at a single center, which may introduce selection bias. A larger database from multiple centers is further needed to externally validate the robustness and reproducibility of the prediction models. Second, ROIs were delineated manually by radiologists, and thus is time-consuming and prone to error and user variability; thus, it’s essential to develop an automatic and reliable liver tumor segmentation tool. Third, it should be noted that the MP-MRI data are not included in this study. In the future, we will attempt to develop a radiomics approach based on MP-MRI for response evaluation in HCC patients after TACE. Finally, we did not include genomics hallmarks related with TACE response, which may provide additional valuable information for therapeutic efficacy prediction. A future direction to consider would be identification of genetic features of HCCs that can be incorporated into the prediction schema.\u003c/p\u003e "},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, intratumoral and peritumoral radiomics based on preoperative CE-MR images can enhance the ability of radiomics model in predicting tumor response to TACE. The combined nomogram which incorporated the rad-score and clinical-radiological risk factors provides an effective tool for the precise and individualized estimation of treatment response for HCC patients treated with TACE. The accurate identification of HCC patients who would receive benefit from upfront TACE might potentially help decision-making for subsequent treatment strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAASLD, American Association for the Study of Liver Disease; AFP, alpha-fetoprotein; ALT, alanine aminotransferase; AST, aspartate aminotransferase; ALB, albumin; ALP, alkaline phosphatase; AP, arterial phase; BCLC, Barcelona Clinic Liver Cancer; CR, complete response; CE-MRI, contrast-enhanced magnetic resonance imaging; CI, confidence interval; DP, delayed phase; DCA, decision curve analysis; ECOG, Eastern Cooperative Oncology Group; ER, early recurrence; GLCM, grey level co-occurrence matrix; GLRLM, grey level run length matrix; GLZSM, grey-level zone size matrix; GGT, \u0026gamma;-glutamyltranspeptadase; GBDT, gradient boosting decision tree; HCC, hepatocellular carcinoma; ICC, intraclass correlation coefficient; mRECIST, modified Response Evaluation Criteria in Solid Tumors; MVI, microvascular invasion; MP-MRI, multiparametric magnetic resonance imaging; NR, non-response; OR, objective response; OS, overall survival; PR, partial response; PD, progression disease; PVP, portal venous phase; PLT, platelet count; PT, prothrombin time; PTR, peritumoral region; Rad-score, radiomics score; RFS, recurrence-free survival; SD, stable disease; TACE, transarterial chemoembolization; TBIL, total bilirubin; T-PTR, intratumoral and peritumoral region; TR, intratumoral region. \u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective study was approved by the Institutional Review Board of the First Affiliated Hospital of Dalian Medical University, and was carried out in accordance with the Declaration of Helsinki. The requirement for the informed consent was waived.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in supplementary material of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was supported by the National Natural Science Foundation of China (No. 61971091).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, Y.Z., J.Z., and A.L.L.; Methodology, Y.Z. and Y.G.; Software, Y.G.; Validation, Y.Z., N.W., and Q.H.X.; Formal analysis, Y.Z., N.W., Y.H.L., and J.H.L.; Investigation, Y.Z., J.Z., N.W., Q.H.X., Y.H.L., J.H.L., X.Y.Z., A.L.C., and L.H.C; Resources, Y.Z., J.Z., and N.W.; Data curation, Y.Z., N.W., Q.H.X., J.H.L., and Q.H.Z.; Writing-original draft preparation, Y.Z. and J.Z.; Writing-review and editing, F.W., Y.G., and A.L.L.; Visualization, Y.Z., L.J.S, and Y.G.; Supervision, A.L.L.; Project administration, Q.W.S. and A.L.L.; Funding acquisition, A.L.L.. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information (optional)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eDepartment of Radiology, The First Affiliated Hospital, Dalian Medical University, Dalian, China; \u003csup\u003e2\u003c/sup\u003eDepartment of Interventional Radiology, The First Affiliated Hospital, Dalian Medical University, Dalian, China; \u003csup\u003e3\u003c/sup\u003eCollege of Medical Imaging, Dalian Medical University, Dalian, China; \u003csup\u003e4\u003c/sup\u003eGE Healthcare (China), Shanghai, China.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References ","content":"\u003col\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. 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Abdom Radiol (NY). 2019;44:539-548.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Hepatocellular carcinoma, Radiomics, Magnetic resonance imaging, Transarterial chemoembolization, Treatment response","lastPublishedDoi":"10.21203/rs.3.rs-2190531/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2190531/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eNoninvasive and precise methods to estimate treatment response and identify hepatocellular carcinoma (HCC) patients who could benefit from transarterial chemoembolization (TACE) are urgently required. The present study aimed to investigate the ability of intratumoral and peritumoral radiomics based on contrast-enhanced magnetic resonance imaging (CE-MRI) to preoperatively predict tumor response to TACE in HCC patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective study involved 138 HCC patients (objective response, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;73 vs. non-response, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;65) who were divided into the training (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;96) and validation (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;42) cohorts. Total 1206 radiomics features were extracted from arterial, venous, and delayed phases images. Radiomics models on intratumoral region (TR) and peritumoral region (PTR) (3 mm, 5 mm, and 10 mm) were established using logistic regression. Three integrated radiomics models, including intratumoral and peritumoral region (T-PTR) (3 mm), T-PTR (5 mm), and T-PTR (10 mm) models, were constructed by using TR and PTR radiomics scores. A clinical-radiological model and a combined model incorporating the optimal radiomics score and selected clinical-radiological predictors were constructed, and the combined model was presented as a nomogram. The discrimination, calibration, and clinical utilities were evaluated by receiver operating characteristic curve, calibration curve, and decision curve analysis, respectively.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe (T-PTR) (3 mm) radiomics model demonstrated the best performance among all radiomics models with an area under the curve (AUC) of 0.911 (95% confidence interval(CI), 0.825\u0026ndash;0.975) in the validation cohort. The (T-PTR) (3 mm) radiomics score, alkaline phosphatase, tumor size, and satellite nodule were combined to construct a combined nomogram. The combined nomogram outperformed the clinical-radiological model with the AUCs of 0.918 (95%CI, 0.831\u0026ndash;0.986) and 0.782 (95%CI, 0.660\u0026ndash;0.902) and achieved good calibration capability and clinical utility.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eCE-MRI-based intratumoral and peritumoral radiomics approach can provide an effective tool for the precise and individualized estimation of treatment response for HCC patients treated with TACE.\u003c/p\u003e","manuscriptTitle":"Intratumoral and Peritumoral Radiomics Based on Contrast-enhanced MRI for Preoperatively Predicting Treatment Response of Transarterial Chemoembolization in Hepatocellular Carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-12-05 20:19:40","doi":"10.21203/rs.3.rs-2190531/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-04-05T14:12:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-03-24T12:03:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"11909afa-6e8d-4068-aba4-55f75adb6c7d","date":"2023-03-23T00:26:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-03-09T20:13:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-12-09T14:25:01+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2022-11-30T06:38:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2022-11-30T06:30:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2022-10-21T12:15:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1b39762f-1f3a-4cd2-a3bc-2439208e479b","owner":[],"postedDate":"December 5th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-30T15:06:15+00:00","versionOfRecord":{"articleIdentity":"rs-2190531","link":"https://doi.org/10.1186/s12885-023-11491-0","journal":{"identity":"bmc-cancer","isVorOnly":false,"title":"BMC Cancer"},"publishedOn":"2023-10-24 15:02:18","publishedOnDateReadable":"October 24th, 2023"},"versionCreatedAt":"2022-12-05 20:19:40","video":"","vorDoi":"10.1186/s12885-023-11491-0","vorDoiUrl":"https://doi.org/10.1186/s12885-023-11491-0","workflowStages":[]},"version":"v1","identity":"rs-2190531","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2190531","identity":"rs-2190531","version":["v1"]},"buildId":"J0_U0BvcaRcwD8yVFaRlm","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}