Clinical and Metabolic Predictors of Response to Transarterial Radioembolization in Primary and Metastatic Liver Tumors: The Role of 18F-FDG PET/CT and Lung Shunt Fraction

Clinical and Metabolic Predictors of Response to Transarterial Radioembolization in Primary and Metastatic Liver Tumors: The Role of 18F-FDG PET/CT and Lung Shunt Fraction | 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 Clinical and Metabolic Predictors of Response to Transarterial Radioembolization in Primary and Metastatic Liver Tumors: The Role of 18F-FDG PET/CT and Lung Shunt Fraction Merve Okuyan, Ertan Şahin, Umut Elboğa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7429304/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose This study aimed to investigate the predictive value of 18F-FDG PET/CT-derived metabolic parameters (SUVmax, MTV, TLG, TLR) and hepatopulmonary shunt fraction (LSF) on the treatment response of transarterial radioembolization (TARE) in patients with primary and metastatic liver tumors. Methods A total of 58 patients who underwent TARE between March 2024 and March 2025 were included. Prior to treatment, all patients underwent 18F-FDG PET/CT and 99mTc-MAA SPECT/CT imaging. PET-based parameters and LSF values were calculated. Treatment response was assessed at 1 and 6 months post-treatment based on mRECIST criteria. Responders were defined as patients with complete or partial response. Statistical analyses included Mann-Whitney U test, Chi-square test, and ROC analysis. Results Of 58 patients, 65.5% (n = 38) were responders. Among PET parameters, lower MTV and TLG values were significantly associated with treatment response (p = 0.004 and p = 0.014, respectively). TLG also demonstrated significance in the HCC subgroup (p = 0.049). SUVmax, TLR, LSF, and administered dose showed no significant association with response. ROC analysis revealed good predictive value for MTV (AUC = 0.703) and TLG (AUC = 0.699), with respective cut-off values of ≤ 61.07 and ≤ 303.99. Demographic and clinical variables such as age, gender, LSF, and dose were not predictive of treatment response. Conclusion Volumetric metabolic parameters, especially TLG, are effective predictors of response to TARE in liver tumors. Incorporating these parameters into clinical evaluation may enhance treatment planning and prognostic estimation. LSF and conventional dose estimation via BSA appear less predictive of localized treatment response. Transarterial radioembolization FDG PET/CT Metabolic tumor volume (MTV) Total lesion glycolysis (TLG) Figures Figure 1 Figure 2 INTRODUCTION Achieving local control in primary and metastatic liver tumors is crucial, and when other treatment options are unsuitable, transarterial radioembolization (TARE) may offer an alternative. TARE delivers targeted radiation through the hepatic artery using yttrium-90 (Y90) microspheres to treat liver tumors ( 1 ). Factors such as hepatopulmonary shunt fraction (LSF) and metabolic imaging parameters like SUVmax, MTV, TLG, and TLR are important in predicting TARE treatment response ( 2 , 3 ). These parameters are used as prognostic indicators to identify patients who are more likely to respond to TARE. SUVmax represents the highest 18F-FDG uptake in a tumor, indicating its metabolic activity and is often used to evaluate malignancy grade ( 4 ). Metabolic Tumor Volume (MTV) and Total Lesion Glycolysis (TLG) assess tumor volume and metabolic activity, while Tumor-to-Liver Ratio (TLR) compares tumor metabolic activity to normal liver tissue ( 5 ). These parameters have prognostic value, with higher levels often associated with poorer outcomes ( 6 , 7 ). Although 18F-FDG PET/CT is not commonly used for hepatocellular carcinoma (HCC) diagnosis due to low GLUT expression, it can be valuable in assessing high-grade HCC, as increased FDG uptake is associated with aggressive tumor behavior ( 8 , 9 ). Intrahepatic cholangiocarcinoma (CCA) and liver metastases, especially from colorectal and other cancers, are also well evaluated by 18F-FDG PET/CT, which shows higher sensitivity than conventional imaging methods ( 10 , 11 ). The aim of this study is to investigate the effect of 18F-FDG PET/CT parameters (SUVmax, MTV, TLG, TLR) and hepatopulmonary shunt fraction (LSF) in predicting the response to transarterial radioembolization (TARE) in primary and metastatic liver tumors. Additionally, this study aims to determine which patient groups these parameters may best predict treatment success. MATERIALS AND METHODS This prospective study includes 58 patients with primary or metastatic liver tumors who underwent transarterial radioembolization (TARE) treatment between March 2024 and March 2025. The study was approved by the Clinical Research Ethics Committee of Gaziantep University (Decision No: 2024/67) and was conducted in accordance with the 1964 Declaration of Helsinki. Data Collection and Patient Selection Demographic information, tumor pathologies, and treatment response patterns of the patients included in the study were collected during the study period from the hospital’s electronic patient records system. Prior to TARE treatment, 18F-FDG PET/CT imaging was performed at the Nuclear Medicine Clinic of Gaziantep University. Subsequently, 99mTc-MAA simulation SPECT/CT imaging was conducted for TARE treatment planning. The TARE treatment dose information calculated from the simulation was also obtained from the Nuclear Medicine Clinic. Throughout the study period, the parameters calculated from 18F-FDG PET/CT images and the LSF values from 99mTc-MAA SPECT/CT simulations, along with the patients' demographic data, treatment response patterns, tumor pathologies, and TARE doses, were organized into tables and prepared for statistical analysis. The data analysis aimed to understand the general characteristics of the sample group and explore the relationships between these parameters and treatment outcomes. Inclusion and Exclusion Criteria Patients with primary or metastatic liver tumors who were referred for TARE treatment by the multidisciplinary liver disease council (oncology, radiology, gastroenterology, nuclear medicine) were included in the study. Inclusion criteria were as follows: patients aged 18 years or older, having undergone 18F-FDG PET/CT imaging prior to treatment, and who voluntarily consented to participate in the study. Exclusion criteria included patients who did not undergo 18F-FDG PET/CT imaging prior to treatment or had liver lesions with no FDG uptake on PET/CT, patients under the age of 18, pregnant patients, and those who did not wish to participate in the study. 18F-FDG PET/CT Imaging Protocol Prior to treatment, all patients underwent PET/CT scans using a 5-ring PET/CT scanner with TOF features (Discovery IQ, GE Healthcare, Milwaukee, USA). Before scanning, patients were instructed to fast for at least 6 hours, ensuring their blood glucose levels were ≤ 11.1 mmol/L. Subsequently, each patient was injected with 3.5–4.5 MBq/kg of 18F-FDG. PET and CT scans covering the whole body, from the head to the proximal thigh, were initiated 60 minutes after injection. The acquired PET datasets and low-dose CT images with a 3 mm slice thickness were reconstructed using AW VolumeShare software (GE Healthcare, Milwaukee, USA) with QClear. Attenuation-corrected PET images, fused PET/CT images, and slices in coronal, sagittal, and axial planes were reviewed on the Xeleris workstation (GE Healthcare). Image Analysis and Measured Parameters In this study, metabolic parameters of liver tumors were calculated using semi-automatic analyses on PET/CT images. The software used for image analysis was Metavol ( 12 ). The primary parameters analyzed and calculated included SUVmax, TLR, MTV, and TLG. The threshold value used in the calculation was based on the average liver SUV value. MTV represents the metabolically active volume of the tumor region. This volume is calculated as the sum of the voxels in the tumor where FDG uptake exceeds a specified threshold value. The threshold value used in the calculation is based on the average liver SUV value. According to the PERCIST criteria, the threshold was determined using the proposed method, which involved adding 3 standard deviations (SD) to the average liver SUV value, or alternatively, using the formula 1.5 × average SUV + 2 SD. This method was applied to minimize SUV variation within the study and to obtain more reliable results in tumor volume measurements. The calculation of SUVmax was performed by identifying the highest SUV value within the region of interest (ROI). TLR was calculated by dividing the tumor SUVmax value by the average SUV value of the liver. TLG was obtained by multiplying MTV by the mean SUV value in the tumor region (TLG: MTV × SUVmean). LSF analysis was performed on the 99mTc-MAA SPECT/CT scintigraphic planar images. Regions of interest (ROIs) were drawn on the lungs and liver, and both anterior and posterior planar images were used to obtain gamma emission counts. The LSF was then calculated by dividing the lung counts by the total lung and liver counts. The segmentation process applied to primary and metastatic liver lesions was demonstrated to visually explain the distinction between tumor and non-tumor uptake regions, as well as the effects of metabolic heterogeneity on lesion delineation (Fig. 1 and Fig. 2 ). Segmentation of the primary malignancy at liver segment 4. (A) PET/CT fusion image, (B) CT slice, and (C) the segmented area are shown. Red arrows indicate the tumor region and the segmentation of the metabolically active area. The blue arrow points to a non-specific uptake area likely related to spillover effect, which does not have an anatomical correlate on CT images. This figure was prepared to illustrate how the segmentation process was performed and how non-tumoral uptakes were observed during segmentation. Segmentation of the metastatic lesion at liver segments 6–7. (A) PET/CT fusion image, (B) CT slice, and (C) the segmented area. Red arrows indicate the tumor region and its segmentation, while blue arrows show non-tumoral areas of the liver that were included in automatic segmentation due to metabolic differences. These regions were excluded through manual correction. This figure demonstrates the effect of metabolic heterogeneity in liver tissue on the segmentation process. TARE Treatment Planning Angiography and Simulation 99mTc-MAA SPECT/CT In the planning of 90Y radioembolization therapy, 150–200 MBq of 99mTc-MAA, which simulates the distribution of 90Y microspheres, was administered by the interventional radiologist into the relevant hepatic artery branch(es). This imaging procedure represents the distribution of 90Y activity. After administration, a whole-body scan and SPECT/CT were performed as soon as possible (within half to one hour) due to the risk of 99mTc-MAA degradation. The scans were carried out using a hybrid scanner equipped with low-energy, high-resolution collimators and a dual-head gamma camera, along with a 16-slice CT scanner (Discovery NM/CT 670, GE Healthcare, USA). During the whole-body scan, conjugate anterior and posterior images were obtained for 10 minutes using a 256×1024 matrix (table speed: 15 cm/min). SPECT images were acquired step-by-step in 3° increments to cover the liver and lungs, with each step lasting 20 seconds. After the SPECT scan, a helical CT scan was performed without contrast agent, and the images were reconstructed in 3.75 mm slices. The SPECT images were post-processed using an iterative algorithm (2 iterations, 10 subsets) with attenuation correction based on the CT attenuation map, resolution improvement, and Butterworth filter on a 128×128 matrix. Based on the calculated LSF percentage from the 99mTc-MAA SPECT/CT, if the LSF ratio was between 10% and 15%, the treatment dose was reduced by 20%. If the LSF ratio was between 15% and 20%, the treatment dose was reduced by 40%. If the LSF exceeded 20%, the treatment was canceled. After simulation, the treatment dose was calculated based on the body surface area (BSA) method. The BSA value, calculated according to the patient's weight and height, was used along with the tumor and liver volumes obtained from SPECT/CT images in the relevant formula to calculate the treatment dose. The BSA formula and the treatment activity formula based on BSA are provided below. Activity (Gbq ): ( BSA – 0,2 ) + \(\:\frac{\text{T}\text{u}\text{m}\text{o}\text{r}\:\text{V}\text{o}\text{l}\text{u}\text{m}\text{e}}{\text{T}\text{u}\text{m}\text{o}\text{r}\:\text{V}\text{o}\text{l}\text{u}\text{m}\text{e}+\text{L}\text{i}\text{v}\text{e}\text{r}\:\text{V}\text{o}\text{l}\text{u}\text{m}\text{e}}\) Evaluation of Post-Treatment Response Patterns Patients who participated in the study underwent additional multifase contrast-enhanced CT or MRI scans using the liver protocol at 1 month and 6 months after TARE treatment. The mRECIST response was evaluated by comparing post-treatment imaging with pre-treatment images to assess the response of tumors in the liver region. Four categories were established for response evaluation: Complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD). Complete Response and partial response were defined as responders to treatment, while stable disease and progressive disease were classified as non-responders. Statistical Analysis Statistical analyses were performed using "IBM SPSS Statistics for Windows, Version 25.0 (Statistical Package for the Social Sciences, IBM Corp., Armonk, NY, USA)." Descriptive statistics for categorical variables were presented as n and %, while continuous variables were presented as Mean ± SD and Median (min-max). For binary group comparisons, the Mann-Whitney U test was used. ROC curve analysis was employed to assess the predictive value of various imaging parameters for treatment response. Pearson Chi-Square test and Fisher’s Exact test were used for comparisons of categorical variables. A p-value of < 0.05 was considered statistically significant. RESULTS A total of 58 patients were included in the study. The average age of the patients was 58.06 ± 12.25 years, with a median age of 59.00 (27–81) years. 70.7% of the patients were under 65 years old (n = 41), and 29.3% were over 65 years old (n = 17). Regarding gender distribution, 44.8% of the patients were female (n = 26), and 55.2% were male (n = 32). Among the metabolic parameters, the average SUVmax value was 9.38 ± 6.04, with a median value of 8.72 (1.86-31.0). The average MTV was 149.26 ± 209.73, with a median value of 58.90 (0.87-1139.12). The average TLG value was 675.14 ± 1135.25, with a median value of 247.98 (2.37-7543.27). The average TLR value was 5.51 ± 3.58, with a median value of 5.00 (2.00–16.00). The distributions of sociodemographic, clinical, and imaging parameters for the patients are shown in Table 1 . When evaluated by pathological subtype, 34.5% of the patients (n = 20) were diagnosed with hepatocellular carcinoma (HCC), while 65.5% (n = 38) were diagnosed with non-HCC tumors, including cholangiocellular carcinoma (n = 10), malignant melanoma (n = 6), breast cancer (n = 5), colorectal cancer (n = 5), lung cancer (n = 3), gastric-pancreatic cancer (n = 3), neuroendocrine carcinoma (n = 2), parotid gland tumor (n = 1), GIST (n = 1), ovarian cancer (n = 1), and sarcoma (n = 1). According to response status, 65.5% of the participants (n = 38) responded to the treatment, while 34.5% (n = 20) did not. The average LSF percentage value was 7.44 ± 4.89, with a median value of 6.00 (2.00–19.00). The average treatment dose administered to the patients was 43.46 ± 6.76, with a median value of 45.0 (27.0–58.0). Regarding the distribution of previous treatments, 77.5% of the patients (n = 45) had received chemotherapy, 13.7% (n = 8) had received immunotherapy, 18.9% (n = 11) had received TACE, and 20.6% (n = 12) had previously received TARE treatment (Table 1 ). Table 1 Distribution of Sociodemographic, Clinical, and Imaging Parameters Parameters N % Age Mean ± SD 58,06 ± 12,25 Median (min-max) 59,00 (27–81) ≤ 65 41 70,7 > 65 17 29,3 Gender Female 26 44,8 Male 32 55,2 SUVmax Mean ± SD 9,38 ± 6,04 Median (min-max) 8,72 (1,86 − 31,0) MTV Mean ± SD 149,26 ± 209,73 Median (min-max) 58,90 (0,87-1139,12) TLG Mean ± SD 675,14 ± 1135,25 Median (min-max) 247,98 (2,37-7543,27) TLR Mean ± SD 5,51 ± 3,58 Median (min-max) 5,00 (2,00–16,00) N : Number Table 1 . Distribution of Sociodemographic, Clinical, and Imaging Parameters Parameters N % Pathological Subtype HCC Non-HCC 20 34,5 65,5 38 Cholangiocellular Carcinoma Malignant Melanoma 10 17,25 6 10,35 Breast Cancer 5 8,6 Colorektal Cancer 5 8,6 Lung Cancer 3 5,2 Gastric-Pancreatic Cancer 3 5,2 Neuroendocrine Carcinoma 2 3,45 Parotid Cancer 1 1,7 GIST 1 1,7 Sarcoma 1 1,7 Ovarian Cancer 1 1,7 Treatment Response Yes 38 65,5 No 20 34,5 LSF% Mean ± SD 7,44 ± 4,89 Median (min-max) 6,00 (2,00–19,00) Treatment Dose (mci) Mean ± SD 43,46 ± 6,76 Median (min-max) 45,0 (27,0–58,0) Previous Therapies CT 45 77,5 Immunotherapy 8 13,7 TACE 11 18,9 TARE 12 20,6 HCC :hepatocellular carcinoma, GIST :gastrointestinal stromal tumor, CT :chemotherapy, TACE : Transarterial chemoembolization, TARE : Transarterial radioembolization, mci : milicurie, SD : standart deviation Regarding the association of sociodemographic, clinical and imaging parameters with response pattern, MTV (p = 0.004) and TLG (p = 0.014) values were significantly lower in the response group. However, no statistically significant relationship was observed between age (p = 0.617), age grouping (≤ 65 and > 65; p = 0.933), gender (p = 0.566), SUVmax (p = 0.987), TLR (p = 0.974), pathologic subtype (p = 0.952), LSF percentage (p = 0.361) and treatment dose (p = 0.272) and response pattern. Statistical analysis results of the comparison of sociodemographic, clinical and imaging parameters according to response groups are given in Table 2 . Table 2 Comparison of Sociodemographic, Clinical, and Imaging Parameters According to Treatment Response Groups Treatment Response Parameters No N = 20 Yes N = 38 p Age , Median (min-max) 59,00 (45–79) 59,00 (27–81) 0.617 a Age group, n (%) ≤ 65 14 (70,0) 27 (71,1) 0.933 b > 65 6 (30,0) 11 ( 28 , 9 ) Gender, n (%) Female 10 (50,0) 16 (42,1) 0.566 b Male 10 (50,0) 22 (57,9) SUVmax , Median (min-max) 8,32 (2,94 − 20,81) 8,64 (1,86 − 31,00) 0.987 a MTV , Median (min-max) 144,60 (15,34-1139,12) 42,18 (0,87–599,98) 0.004 a TLG , Median (min-max) 558,95 (49,04-7543,27) 201,32 (2,37-2543,83) 0.014 a TLR , Median (min-max) 5,00 (2,00–13,00) 5,00 (2,00–16,00) 0.974 a Pathology, n (%) HCC 7 (35,0) 13 (34,2) 0.952 b Non-HCC 13 (65,0) 25 (65,8) LSF% , Median (min-max) 8,00 (2,00–19,00) 6,00 (2,00–19,00) 0.361 a Treatment Dose , Median (min-max) 45,0 (30,0–52,0) 46,0 (27,0–58,0) 0.272 a a :Mann Whitney U test, b :Pearson Chi Square test Since the majority of the patients in the sample group (n = 20) were HCC patients; in the comparison of sociodemographic, clinical and imaging parameters in the HCC-specific subgroup according to the response groups; TLG (p = 0.049) values in HCC patients were found to be significantly lower in the response group. However, no statistically significant relationship was observed between age (p = 0.183), gender (p = 0.521), SUVmax (p = 0.191), MTV (p = 0.191), TLR (p = 0.742), LSF % (p = 0.780) and treatment dose (p = 0.451) and response groups. The results of the analysis are given in Table 3 . Table 3 Comparison of Sociodemographic, Clinical, and Imaging Parameters by Treatment Response in HCC Patients HCC-Response Parameters No N = 7 Yes N = 13 p Age , Median (min-max) 64,00 (55–79) 64,00 (27–78) 0.183 a Age group, n (%) ≤ 65 4 (57,1) 10 (76,9) 0.613 b > 65 3 (42,9) 3 ( 23 , 1 ) Gender, n (%) Female 0 (0,0) 2 ( 15 , 4 ) 0.521 b Male 7 (100,0) 11 (84,6) SUVmax , Median (min-max) 7,37 (2,94 − 11,51) 3,62 (1,86 − 13,28) 0.191 a MTV , Median (min-max) 109,58 (42,64–612,68) 78,73 (1,21–599,98) 0.191 a TLG , Median (min-max) 591,61 (131,04-2412,75) 258,13 (3,50-1913,02) 0.049 a TLR , Median (min-max) 3,00 (2,00–9,00) 3,00 (2,00–12,00) 0.742 a LSF% , Median (min-max) 10,00 (3,00–15,00) 8,00 (3,00–17,00) 0.780 a Treatment Dose , Median (min-max) 45,0 (32,0–48,0) 42,0 (28,0–50,0) 0.451 a a :Mann Whitney U test, b :Pearson Chi Square test Table 4 shows the ROC analysis of the predictive value of imaging parameters in differentiating response to treatment. The AUC value of the MTV variable was calculated as 0.703 (95% CI: 0.597–0.864) and the cut-off value was determined as ≤ 61.07. The sensitivity and specificity for MTV were 60.5% and 60.0%, respectively, and were found to be statistically significant in differentiating response to treatment (p = 0.004). The AUC value of the TLG variable was calculated as 0.699 (95% CI: 0.558–0.839) and the cut-off value was determined as ≤ 303.99. The sensitivity and specificity for TLG were 60.5% and 60.0%, respectively, and were statistically significant in discriminating response to treatment (p = 0.014). Table 4 Analysis of the Predictive Value of Imaging Parameters in Differentiating Treatment Response Parameters AUC %95 CI Cut-off Sensitivity (%) Specificity (%) p SUVmax 0.501 0.353–0.650 ≤ 8,83 52,6 50,0 0.987 MTV 0.703 0.597–0.864 ≤ 61,07 60,5 60,0 0.004 TLG 0.699 0.558–0.839 ≤ 303,99 60,5 60,0 0.014 TLR 0.503 0.352–0.653 ≤ 4,50 47,4 55,0 0.974 LSF% 0.573 0.413–0.733 ≤ 6,50 57,9 55,0 0.364 AUC : Area under the curve, %95CI : Confidence interval DISCUSSION Liver tumors, whether primary or metastatic, pose a significant clinical challenge due to their poor prognosis and limited treatment options. Transarterial radioembolization (TARE) with 90Y-labeled microspheres has emerged as an important locoregional therapy, particularly in patients ineligible for surgical resection or systemic therapy. While traditionally applied in hepatocellular carcinoma (HCC), its use has expanded to various metastatic liver tumors, demonstrating variable effectiveness depending on tumor biology and patient characteristics ( 13 ). In our study, we evaluated the predictive value of 18F-FDG PET/CT-derived metabolic parameters (SUVmax, MTV, TLG, TLR) and hepatopulmonary shunt fraction (LSF) in assessing response to TARE. Among these, MTV and TLG showed significant association with treatment response. Specifically, lower MTV and TLG values were observed in responders (p = 0.004 and p = 0.014, respectively). This suggests that tumors with lower metabolic burden respond more favorably to TARE. These findings align with prior studies indicating that volumetric PET parameters, which integrate tumor activity and extent, provide superior prognostic insight compared to point measurements like SUVmax ( 14 – 16 ). ROC analysis further supported the predictive role of metabolic parameters. The area under the curve (AUC) was 0.703 for MTV and 0.699 for TLG, indicating a good level of diagnostic accuracy. The identified cut-off values (MTV ≤ 61.07 and TLG ≤ 303.99) showed balanced sensitivity and specificity (both approximately 60%), suggesting these thresholds may be useful in clinical practice to distinguish responders from non-responders. Among the two, TLG offers a more integrated assessment by combining metabolic activity and volume, potentially improving sensitivity in tumors with heterogeneous uptake. TLG, which combines metabolic activity (SUVmean) and volume (MTV), appears particularly useful in capturing intratumoral heterogeneity. This may explain why, in the HCC subgroup, TLG (but not MTV) maintained a statistically significant relationship with response (p = 0.049). HCC is known for its biological and metabolic heterogeneity, and TLG may better reflect this complexity than MTV alone ( 17 , 18 ). Our results support the growing consensus that TLG is a more comprehensive and sensitive parameter, especially in tumors with variable FDG uptake patterns. In contrast, no significant association was found between SUVmax or TLR and treatment response. While both parameters have been reported in the literature to correlate with tumor aggressiveness or treatment outcomes ( 14 , 19 , 20 ), their predictive value may be limited in heterogeneous cohorts. In our population, the inclusion of multiple tumor types with varying metabolic profiles and prior treatments likely contributed to the lack of statistical significance. Additionally, SUVmax captures only the most active voxel and may not represent the overall tumor behavior. The role of LSF in predicting treatment response remains uncertain. In our study, LSF was not significantly associated with treatment response in either the overall cohort or the HCC subgroup. Although high LSF can theoretically reduce therapeutic efficacy by diverting activity from the liver to the lungs, its primary clinical relevance may lie in safety and dosimetry planning rather than direct response prediction ( 21 , 22 ). Some studies have reported that LSF has stronger associations with survival metrics rather than immediate imaging response ( 23 – 25 ). Another key observation was the lack of a statistically significant relationship between administered dose and treatment response. While some studies suggest higher doses correlate with improved outcomes ( 21 , 26 – 28 ), our results highlight the limitations of the BSA-based dosimetry model. The BSA method does not account for tumor-specific metabolic activity or spatial distribution, which are critical factors in achieving effective radiation delivery ( 29 ). The discrepancy between planned and actual microsphere distribution particularly in the setting of 99mTc-MAA’s imperfect simulation of 90Y biodistribution may also contribute to the variability in response. Furthermore, individual tumor biology and prior systemic treatments likely modulate radiation sensitivity, complicating the dose-response relationship. In addition to imaging-based parameters, demographic and clinical variables such as age, gender, administered treatment dose, and LSF values were also assessed for their association with treatment response. In both the overall patient group and the HCC subgroup, none of these variables showed a statistically significant relationship with treatment response (p > 0.05). These findings are consistent with previous studies that report limited predictive value of demographic factors in TARE outcomes ( 14 , 15 ). However, imaging-based metabolic parameters demonstrated a more distinct association with therapeutic response, emphasizing their prognostic importance. Taken together, our findings emphasize the prognostic value of volumetric PET parameters in predicting response to TARE. TLG, in particular, may offer a more accurate reflection of tumor burden and heterogeneity, and thus better inform treatment planning and response evaluation. Conversely, parameters such as LSF and SUVmax may have limited predictive power in heterogeneous patient groups. Limitations of the study include the relatively small sample size, the heterogeneous tumor population, and the reliance on a non-personalized dosimetry model. Future research with larger cohorts, tumor-specific subgroups, and advanced dosimetry approaches (e.g., partition model or voxel-based dosimetry) will enhance the understanding of predictive imaging biomarkers and optimize TARE outcomes. In conclusion, volumetric metabolic parameters such as MTV and especially TLG are valuable tools in predicting response to TARE in liver tumors. Integrating these biomarkers into clinical decision-making may support individualized treatment strategies and improve therapeutic success. Declarations Conflict of Interest: The authors declare no conflict of interest. Author Contributions: All authors contributed to the study conception and design. All authors have read and agreed to the published version of the manuscript. Ethical approval: All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study (Decision No: 2024/67) Funding Information: No funding was received for conducting this study. Acknowledgments: We are deeply grateful to all those who played a role in the success of this project. Conceptualization: Ertan Sahin and Merve Okuyan.; methodology: Merve Okuyan; software: Ertan Sahin; validation: Umut Elboga; formal analysis: Ertan Sahin and Merve Okuyan; investigation: Merve Okuyan; data curation: Merve Okuyan; writing—original draft preparation: Merve Okuyan; writing-review and editing: Merve Okuyan; visualization: Ertan Sahin; supervision: Umut Elboga; Project administration: Ertan Sahin. References Viñal D, Minaya-Bravo A, Prieto I, Feliu J, Rodriguez-Salas N. Ytrrium-90 transarterial radioembolization in patients with gastrointestinal malignancies. Clin Transl Oncol. 2022;24(5):796–808. Xing M, Lahti S, Kokabi N, Schuster DM, Camacho JC, Kim HS. 90Y Radioembolization Lung Shunt Fraction in Primary and Metastatic Liver Cancer as a Biomarker for Survival. Clin Nucl Med. 2016;41(1):21–7. Puranik AD, Rangarajan V, Gosavi A, et al. Prognostic value of imaging-based parameters in patients with intermediate-stage hepatocellular carcinoma undergoing transarterial radioembolization. Nucl Med Commun. 2021;42(3):337–44. Sarikaya I, Sarikaya A. Assessing PET Parameters in Oncologic 18F-FDG Studies. J Nucl Med Technol. 2020;48(3):278–82. Hofheinz F, Bütof R, Apostolova I, et al. An investigation of the relation between tumor-to-liver ratio (TLR) and tumor-to-blood standard uptake ratio (SUR) in oncological FDG PET. EJNMMI Res. 2016;6(1):19. Pak K, Cheon GJ, Nam HY, et al. Prognostic value of metabolic tumor volume and total lesion glycolysis in head and neck cancer: a systematic review and meta-analysis. J Nucl Med. 2014;55(6):884–90. Ulaner GA, Eaton A, Morris PG, et al. Prognostic value of quantitative fluorodeoxyglucose measurements in newly diagnosed metastatic breast cancer. Cancer Med. 2013;2(5):725–33. Trojan J, Schroeder O, Raedle J, et al. Fluorine-18 FDG positron emission tomography for imaging of hepatocellular carcinoma. Am J Gastroenterol. 1999;94(11):3314–9. Hwang SH, Lee JW, Cho HJ, Kim KS, Choi GH, Yun M. Prognostic Value of Metabolic Tumor Volume and Total Lesion Glycolysis on Preoperative 18F-FDG PET/CT in Patients With Very Early and Early Hepatocellular Carcinoma. Clin Nucl Med. 2017;42(1):34–9. Liao X, Wei J, Li Y, et al. 18F-FDG PET with or without CT in the diagnosis of extrahepatic metastases or local residual/recurrent hepatocellular carcinoma. Med (Baltim). 2018;97(34):e11970. Kinkel K, Lu Y, Both M, Warren RS, Thoeni RF. Detection of hepatic metastases from cancers of the gastrointestinal tract by using noninvasive imaging methods (US, CT, MR imaging, PET): a meta-analysis. Radiology. 2002;224(3):748–56. Hirata K, Kobayashi K, Wong KP, et al. A semi-automated technique determining the liver standardized uptake value reference for tumor delineation in FDG PET-CT. PLoS ONE. 2014;9(8):e105682. Vente MAD, Wondergem M, van der Tweel I, van den Bosch MAAJ, Zonnenberg BA, Lam MGEH, et al. Yttrium-90 microsphere radioembolization for the treatment of liver malignancies: a structured meta-analysis. Eur Radiol. 2009;19(4):951–9. Hwang SH, Hong HS, Kim D, et al. Total Lesion Glycolysis on 18F-FDG PET/CT Is a Better Prognostic Factor Than Tumor Dose on 90Y PET/CT in Patients With Hepatocellular Carcinoma Treated With 90Y Transarterial Radioembolization. Clin Nucl Med. 2022;47(6):e437–43. Karahan Şen NP, Alataş Ö, Gülcü A, Özdoğan Ö, Derebek E, Çapa Kaya G. The role of volumetric and textural analysis of pretreatment 18F-fluorodeoxyglucose PET/computerized tomography images in predicting complete response to transarterial radioembolization in hepatocellular cancer. Nucl Med Commun. 2022;43(7):807–14. Burger IA, Casanova R, Steiger S, et al. 18F-FDG PET/CT of Non-Small Cell Lung Carcinoma Under Neoadjuvant Chemotherapy: Background-Based Adaptive-Volume Metrics Outperform TLG and MTV in Predicting Histopathologic Response. J Nucl Med. 2016;57(6):849–54. Yamashita T, Forgues M, Wang W, et al. EpCAM and alpha-fetoprotein expression defines novel prognostic subtypes of hepatocellular carcinoma. Cancer Res. 2008;68(5):1451–61. Kalasekar SM, VanSant-Webb CH, Evason KJ. Intratumor Heterogeneity in Hepatocellular Carcinoma: Challenges and Opportunities. Cancers (Basel). 2021;13(21). Kużdżał B, Moszczyński K, Żanowska K, et al. Correlation between 18-FDG standardized uptake value and tumor grade in patients with resectable non-small cell lung cancer. Transl Cancer Res. 2023;12(12):3530–7. Wang C, Zhao K, Hu S, et al. The PET-Derived Tumor-to-Liver Standard Uptake Ratio (SUV TLR) Is Superior to Tumor SUVmax in Predicting Tumor Response and Survival After Chemoradiotherapy in Patients With Locally Advanced Esophageal Cancer. Front Oncol. 2020;10:1630. Garin E, Lenoir L, Rolland Y, et al. Dosimetry based on 99mTc-macroaggregated albumin SPECT/CT accurately predicts tumor response and survival in hepatocellular carcinoma patients treated with 90Y-loaded glass microspheres: preliminary results. J Nucl Med. 2012;53(2):255–63. Gaba RC, Zivin SP, Dikopf MS, et al. Characteristics of primary and secondary hepatic malignancies associated with hepatopulmonary shunting. Radiology. 2014;271(2):602–12. Gosavi A, Puranik AD, Shah S, et al. Prognostic value of lung shunt fraction in hepatocellular carcinoma and unresectable liver dominant metastatic colorectal cancer undergoing transarterial radioembolisation. Nucl Med Commun. 2022;43(1):24–31. Deipolyi AR, Iafrate AJ, Zhu AX, Ergul EA, Ganguli S, Oklu R. High lung shunt fraction in colorectal liver tumors is associated with distant metastasis and decreased survival. J Vasc Interv Radiol. 2014;25(10):1604–8. Das A, Riaz A, Gabr A, et al. Safety and efficacy of radioembolization with glass microspheres in hepatocellular carcinoma patients with elevated lung shunt fraction: analysis of a 103-patient cohort. Eur J Nucl Med Mol Imaging. 2020;47(4):807–15. Cheng B, Villalobos A, Sethi I, et al. Determination of Tumor Dose Response Thresholds in Patients with Chemorefractory Intrahepatic Cholangiocarcinoma Treated with Resin and Glass-based Y90 Radioembolization. Cardiovasc Intervent Radiol. 2021;44(8):1194–203. Sankhla T, Cheng B, Nezami N, et al. Role of Resin Microsphere Y90 Dosimetry in Predicting Objective Tumor Response, Survival and Treatment Related Toxicity in Surgically Unresectable Colorectal Liver Metastasis: A Retrospective Single Institution Study. Cancers (Basel). 2021;13:19. Cheng B, Sethi I, Davisson N, et al. Yttrium-90 dosimetry and implications on tumour response and survival after radioembolisation of chemo-refractory hepatic metastases from breast cancer. Nucl Med Commun. 2021;42(4):402–9. Kao YH, Tan EH, Ng CE, Goh SW. Clinical implications of the body surface area method versus partition model dosimetry for yttrium-90 radioembolization using resin microspheres: a technical review. Ann Nucl Med. 2011;25(7):455–61. Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7429304","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":507489197,"identity":"2fdd7b71-a3ed-4f4e-ac85-1d733fd83950","order_by":0,"name":"Merve Okuyan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYPCCBBDB+BjMZmZuIFoLszEDgwGQYiReC5s0WAsDAS267b3PJD5UpCVuOH72WHVBxZ9o/naglh8V23BqMTtz3ExyxpmcxA1n8tJuzzhjkDvjMGMDY8+Z27i13EhjNuZtq0jccCDH7DZvm0FuA1ALM2MbHi33nzEb/wVpOf/GrBikZT5BLTfYGB8ztgEddiPHjBmkZQNBLWfSGB/2nEkznnnjjbE0zxnj3I1ALQfx+uX4MYYDPyqSZfvO5xh+5qmQy513/vDBBz8qcGuBA4UDSJwDOBShAvkGopSNglEwCkbBSAQAv5Bdw/PqfcUAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-3605-7386","institution":"Gaziantep University: Gaziantep Universitesi","correspondingAuthor":true,"prefix":"","firstName":"Merve","middleName":"","lastName":"Okuyan","suffix":""},{"id":507489198,"identity":"996122d3-96f5-46d8-9858-70b424ac8250","order_by":1,"name":"Ertan Şahin","email":"","orcid":"","institution":"Gaziantep University: Gaziantep Universitesi","correspondingAuthor":false,"prefix":"","firstName":"Ertan","middleName":"","lastName":"Şahin","suffix":""},{"id":507489199,"identity":"36d621cc-b3f3-43af-bd4b-bcb9df059b61","order_by":2,"name":"Umut Elboğa","email":"","orcid":"","institution":"Gaziantep University: Gaziantep Universitesi","correspondingAuthor":false,"prefix":"","firstName":"Umut","middleName":"","lastName":"Elboğa","suffix":""}],"badges":[],"createdAt":"2025-08-21 21:42:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7429304/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7429304/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90929171,"identity":"8a52c7af-5ff5-468f-8978-cccc8b62d01b","added_by":"auto","created_at":"2025-09-09 16:04:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":209890,"visible":true,"origin":"","legend":"\u003cp\u003eSegmentation of the primary malignancy at liver segment 4. (A) PET/CT fusion image, (B) CT slice, and (C) the segmented area are shown. Red arrows indicate the tumor region and the segmentation of the metabolically active area. The blue arrow points to a non-specific uptake area likely related to spillover effect, which does not have an anatomical correlate on CT images. This figure was prepared to illustrate how the segmentation process was performed and how non-tumoral uptakes were observed during segmentation.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7429304/v1/4cd2625ac137a8fbadc53923.png"},{"id":90928345,"identity":"5951beb1-9533-41b9-b063-c47b561cbf5e","added_by":"auto","created_at":"2025-09-09 15:56:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":231981,"visible":true,"origin":"","legend":"\u003cp\u003eSegmentation of the metastatic lesion at liver segments 6–7. (A) PET/CT fusion image, (B) CT slice, and (C) the segmented area. Red arrows indicate the tumor region and its segmentation, while blue arrows show non-tumoral areas of the liver that were included in automatic segmentation due to metabolic differences. These regions were excluded through manual correction. This figure demonstrates the effect of metabolic heterogeneity in liver tissue on the segmentation process.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7429304/v1/21053556ba0ab14fbf0f2f8e.png"},{"id":97139612,"identity":"b6d439f6-c417-42ad-affb-a81bb0f13d96","added_by":"auto","created_at":"2025-12-01 10:00:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1507928,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7429304/v1/ac425645-2bf7-4c1d-a262-0a3b83492485.pdf"}],"financialInterests":"","formattedTitle":"Clinical and Metabolic Predictors of Response to Transarterial Radioembolization in Primary and Metastatic Liver Tumors: The Role of 18F-FDG PET/CT and Lung Shunt Fraction","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eAchieving local control in primary and metastatic liver tumors is crucial, and when other treatment options are unsuitable, transarterial radioembolization (TARE) may offer an alternative. TARE delivers targeted radiation through the hepatic artery using yttrium-90 (Y90) microspheres to treat liver tumors (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Factors such as hepatopulmonary shunt fraction (LSF) and metabolic imaging parameters like SUVmax, MTV, TLG, and TLR are important in predicting TARE treatment response (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). These parameters are used as prognostic indicators to identify patients who are more likely to respond to TARE.\u003c/p\u003e\u003cp\u003eSUVmax represents the highest 18F-FDG uptake in a tumor, indicating its metabolic activity and is often used to evaluate malignancy grade (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Metabolic Tumor Volume (MTV) and Total Lesion Glycolysis (TLG) assess tumor volume and metabolic activity, while Tumor-to-Liver Ratio (TLR) compares tumor metabolic activity to normal liver tissue (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). These parameters have prognostic value, with higher levels often associated with poorer outcomes (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlthough 18F-FDG PET/CT is not commonly used for hepatocellular carcinoma (HCC) diagnosis due to low GLUT expression, it can be valuable in assessing high-grade HCC, as increased FDG uptake is associated with aggressive tumor behavior (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Intrahepatic cholangiocarcinoma (CCA) and liver metastases, especially from colorectal and other cancers, are also well evaluated by 18F-FDG PET/CT, which shows higher sensitivity than conventional imaging methods (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe aim of this study is to investigate the effect of 18F-FDG PET/CT parameters (SUVmax, MTV, TLG, TLR) and hepatopulmonary shunt fraction (LSF) in predicting the response to transarterial radioembolization (TARE) in primary and metastatic liver tumors. Additionally, this study aims to determine which patient groups these parameters may best predict treatment success.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003eThis prospective study includes 58 patients with primary or metastatic liver tumors who underwent transarterial radioembolization (TARE) treatment between March 2024 and March 2025. The study was approved by the Clinical Research Ethics Committee of Gaziantep University (Decision No: 2024/67) and was conducted in accordance with the 1964 Declaration of Helsinki.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData Collection and Patient Selection\u003c/h2\u003e\u003cp\u003eDemographic information, tumor pathologies, and treatment response patterns of the patients included in the study were collected during the study period from the hospital\u0026rsquo;s electronic patient records system. Prior to TARE treatment, 18F-FDG PET/CT imaging was performed at the Nuclear Medicine Clinic of Gaziantep University. Subsequently, 99mTc-MAA simulation SPECT/CT imaging was conducted for TARE treatment planning. The TARE treatment dose information calculated from the simulation was also obtained from the Nuclear Medicine Clinic. Throughout the study period, the parameters calculated from 18F-FDG PET/CT images and the LSF values from 99mTc-MAA SPECT/CT simulations, along with the patients' demographic data, treatment response patterns, tumor pathologies, and TARE doses, were organized into tables and prepared for statistical analysis. The data analysis aimed to understand the general characteristics of the sample group and explore the relationships between these parameters and treatment outcomes.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eInclusion and Exclusion Criteria\u003c/h3\u003e\n\u003cp\u003ePatients with primary or metastatic liver tumors who were referred for TARE treatment by the multidisciplinary liver disease council (oncology, radiology, gastroenterology, nuclear medicine) were included in the study. Inclusion criteria were as follows: patients aged 18 years or older, having undergone 18F-FDG PET/CT imaging prior to treatment, and who voluntarily consented to participate in the study. Exclusion criteria included patients who did not undergo 18F-FDG PET/CT imaging prior to treatment or had liver lesions with no FDG uptake on PET/CT, patients under the age of 18, pregnant patients, and those who did not wish to participate in the study.\u003c/p\u003e\u003cp\u003e\u003cb\u003e18F-FDG PET/CT Imaging Protocol\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePrior to treatment, all patients underwent PET/CT scans using a 5-ring PET/CT scanner with TOF features (Discovery IQ, GE Healthcare, Milwaukee, USA). Before scanning, patients were instructed to fast for at least 6 hours, ensuring their blood glucose levels were \u0026le;\u0026thinsp;11.1 mmol/L. Subsequently, each patient was injected with 3.5\u0026ndash;4.5 MBq/kg of 18F-FDG. PET and CT scans covering the whole body, from the head to the proximal thigh, were initiated 60 minutes after injection. The acquired PET datasets and low-dose CT images with a 3 mm slice thickness were reconstructed using AW VolumeShare software (GE Healthcare, Milwaukee, USA) with QClear. Attenuation-corrected PET images, fused PET/CT images, and slices in coronal, sagittal, and axial planes were reviewed on the Xeleris workstation (GE Healthcare).\u003c/p\u003e\n\u003ch3\u003eImage Analysis and Measured Parameters\u003c/h3\u003e\n\u003cp\u003eIn this study, metabolic parameters of liver tumors were calculated using semi-automatic analyses on PET/CT images. The software used for image analysis was Metavol (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). The primary parameters analyzed and calculated included SUVmax, TLR, MTV, and TLG. The threshold value used in the calculation was based on the average liver SUV value. MTV represents the metabolically active volume of the tumor region. This volume is calculated as the sum of the voxels in the tumor where FDG uptake exceeds a specified threshold value. The threshold value used in the calculation is based on the average liver SUV value. According to the PERCIST criteria, the threshold was determined using the proposed method, which involved adding 3 standard deviations (SD) to the average liver SUV value, or alternatively, using the formula 1.5 \u0026times; average SUV\u0026thinsp;+\u0026thinsp;2 SD. This method was applied to minimize SUV variation within the study and to obtain more reliable results in tumor volume measurements. The calculation of SUVmax was performed by identifying the highest SUV value within the region of interest (ROI). TLR was calculated by dividing the tumor SUVmax value by the average SUV value of the liver. TLG was obtained by multiplying MTV by the mean SUV value in the tumor region (TLG: MTV \u0026times; SUVmean). LSF analysis was performed on the 99mTc-MAA SPECT/CT scintigraphic planar images. Regions of interest (ROIs) were drawn on the lungs and liver, and both anterior and posterior planar images were used to obtain gamma emission counts. The LSF was then calculated by dividing the lung counts by the total lung and liver counts. The segmentation process applied to primary and metastatic liver lesions was demonstrated to visually explain the distinction between tumor and non-tumor uptake regions, as well as the effects of metabolic heterogeneity on lesion delineation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSegmentation of the primary malignancy at liver segment 4. (A) PET/CT fusion image, (B) CT slice, and (C) the segmented area are shown. Red arrows indicate the tumor region and the segmentation of the metabolically active area. The blue arrow points to a non-specific uptake area likely related to spillover effect, which does not have an anatomical correlate on CT images. This figure was prepared to illustrate how the segmentation process was performed and how non-tumoral uptakes were observed during segmentation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSegmentation of the metastatic lesion at liver segments 6\u0026ndash;7. (A) PET/CT fusion image, (B) CT slice, and (C) the segmented area. Red arrows indicate the tumor region and its segmentation, while blue arrows show non-tumoral areas of the liver that were included in automatic segmentation due to metabolic differences. These regions were excluded through manual correction. This figure demonstrates the effect of metabolic heterogeneity in liver tissue on the segmentation process.\u003c/p\u003e\n\u003ch3\u003eTARE Treatment Planning Angiography and Simulation 99mTc-MAA SPECT/CT\u003c/h3\u003e\n\u003cp\u003eIn the planning of 90Y radioembolization therapy, 150\u0026ndash;200 MBq of 99mTc-MAA, which simulates the distribution of 90Y microspheres, was administered by the interventional radiologist into the relevant hepatic artery branch(es). This imaging procedure represents the distribution of 90Y activity. After administration, a whole-body scan and SPECT/CT were performed as soon as possible (within half to one hour) due to the risk of 99mTc-MAA degradation. The scans were carried out using a hybrid scanner equipped with low-energy, high-resolution collimators and a dual-head gamma camera, along with a 16-slice CT scanner (Discovery NM/CT 670, GE Healthcare, USA). During the whole-body scan, conjugate anterior and posterior images were obtained for 10 minutes using a 256\u0026times;1024 matrix (table speed: 15 cm/min). SPECT images were acquired step-by-step in 3\u0026deg; increments to cover the liver and lungs, with each step lasting 20 seconds. After the SPECT scan, a helical CT scan was performed without contrast agent, and the images were reconstructed in 3.75 mm slices. The SPECT images were post-processed using an iterative algorithm (2 iterations, 10 subsets) with attenuation correction based on the CT attenuation map, resolution improvement, and Butterworth filter on a 128\u0026times;128 matrix.\u003c/p\u003e\u003cp\u003eBased on the calculated LSF percentage from the 99mTc-MAA SPECT/CT, if the LSF ratio was between 10% and 15%, the treatment dose was reduced by 20%. If the LSF ratio was between 15% and 20%, the treatment dose was reduced by 40%. If the LSF exceeded 20%, the treatment was canceled. After simulation, the treatment dose was calculated based on the body surface area (BSA) method. The BSA value, calculated according to the patient's weight and height, was used along with the tumor and liver volumes obtained from SPECT/CT images in the relevant formula to calculate the treatment dose. The BSA formula and the treatment activity formula based on BSA are provided below.\u003c/p\u003e\u003cp\u003e\u003cb\u003eActivity (Gbq\u003c/b\u003e): ( BSA \u0026ndash; 0,2 ) + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\text{T}\\text{u}\\text{m}\\text{o}\\text{r}\\:\\text{V}\\text{o}\\text{l}\\text{u}\\text{m}\\text{e}}{\\text{T}\\text{u}\\text{m}\\text{o}\\text{r}\\:\\text{V}\\text{o}\\text{l}\\text{u}\\text{m}\\text{e}+\\text{L}\\text{i}\\text{v}\\text{e}\\text{r}\\:\\text{V}\\text{o}\\text{l}\\text{u}\\text{m}\\text{e}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003ch3\u003eEvaluation of Post-Treatment Response Patterns\u003c/h3\u003e\n\u003cp\u003ePatients who participated in the study underwent additional multifase contrast-enhanced CT or MRI scans using the liver protocol at 1 month and 6 months after TARE treatment. The mRECIST response was evaluated by comparing post-treatment imaging with pre-treatment images to assess the response of tumors in the liver region. Four categories were established for response evaluation: Complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD). Complete Response and partial response were defined as responders to treatment, while stable disease and progressive disease were classified as non-responders.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were performed using \"IBM SPSS Statistics for Windows, Version 25.0 (Statistical Package for the Social Sciences, IBM Corp., Armonk, NY, USA).\" Descriptive statistics for categorical variables were presented as n and %, while continuous variables were presented as Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD and Median (min-max). For binary group comparisons, the Mann-Whitney U test was used. ROC curve analysis was employed to assess the predictive value of various imaging parameters for treatment response. Pearson Chi-Square test and Fisher\u0026rsquo;s Exact test were used for comparisons of categorical variables. A p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eA total of 58 patients were included in the study. The average age of the patients was 58.06\u0026thinsp;\u0026plusmn;\u0026thinsp;12.25 years, with a median age of 59.00 (27\u0026ndash;81) years. 70.7% of the patients were under 65 years old (n\u0026thinsp;=\u0026thinsp;41), and 29.3% were over 65 years old (n\u0026thinsp;=\u0026thinsp;17). Regarding gender distribution, 44.8% of the patients were female (n\u0026thinsp;=\u0026thinsp;26), and 55.2% were male (n\u0026thinsp;=\u0026thinsp;32). Among the metabolic parameters, the average SUVmax value was 9.38\u0026thinsp;\u0026plusmn;\u0026thinsp;6.04, with a median value of 8.72 (1.86-31.0). The average MTV was 149.26\u0026thinsp;\u0026plusmn;\u0026thinsp;209.73, with a median value of 58.90 (0.87-1139.12). The average TLG value was 675.14\u0026thinsp;\u0026plusmn;\u0026thinsp;1135.25, with a median value of 247.98 (2.37-7543.27). The average TLR value was 5.51\u0026thinsp;\u0026plusmn;\u0026thinsp;3.58, with a median value of 5.00 (2.00\u0026ndash;16.00). The distributions of sociodemographic, clinical, and imaging parameters for the patients are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. When evaluated by pathological subtype, 34.5% of the patients (n\u0026thinsp;=\u0026thinsp;20) were diagnosed with hepatocellular carcinoma (HCC), while 65.5% (n\u0026thinsp;=\u0026thinsp;38) were diagnosed with non-HCC tumors, including cholangiocellular carcinoma (n\u0026thinsp;=\u0026thinsp;10), malignant melanoma (n\u0026thinsp;=\u0026thinsp;6), breast cancer (n\u0026thinsp;=\u0026thinsp;5), colorectal cancer (n\u0026thinsp;=\u0026thinsp;5), lung cancer (n\u0026thinsp;=\u0026thinsp;3), gastric-pancreatic cancer (n\u0026thinsp;=\u0026thinsp;3), neuroendocrine carcinoma (n\u0026thinsp;=\u0026thinsp;2), parotid gland tumor (n\u0026thinsp;=\u0026thinsp;1), GIST (n\u0026thinsp;=\u0026thinsp;1), ovarian cancer (n\u0026thinsp;=\u0026thinsp;1), and sarcoma (n\u0026thinsp;=\u0026thinsp;1). According to response status, 65.5% of the participants (n\u0026thinsp;=\u0026thinsp;38) responded to the treatment, while 34.5% (n\u0026thinsp;=\u0026thinsp;20) did not. The average LSF percentage value was 7.44\u0026thinsp;\u0026plusmn;\u0026thinsp;4.89, with a median value of 6.00 (2.00\u0026ndash;19.00). The average treatment dose administered to the patients was 43.46\u0026thinsp;\u0026plusmn;\u0026thinsp;6.76, with a median value of 45.0 (27.0\u0026ndash;58.0). Regarding the distribution of previous treatments, 77.5% of the patients (n\u0026thinsp;=\u0026thinsp;45) had received chemotherapy, 13.7% (n\u0026thinsp;=\u0026thinsp;8) had received immunotherapy, 18.9% (n\u0026thinsp;=\u0026thinsp;11) had received TACE, and 20.6% (n\u0026thinsp;=\u0026thinsp;12) had previously received TARE treatment (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of Sociodemographic, Clinical, and Imaging Parameters\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eParameters\u003c/span\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eN\u003c/span\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e%\u003c/span\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58,06\u0026thinsp;\u0026plusmn;\u0026thinsp;12,25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian (min-max)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59,00 (27\u0026ndash;81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70,7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29,3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44,8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55,2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSUVmax\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9,38\u0026thinsp;\u0026plusmn;\u0026thinsp;6,04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian (min-max)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8,72 (1,86\u0026thinsp;\u0026minus;\u0026thinsp;31,0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMTV\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e149,26\u0026thinsp;\u0026plusmn;\u0026thinsp;209,73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian (min-max)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58,90 (0,87-1139,12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTLG\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e675,14\u0026thinsp;\u0026plusmn;\u0026thinsp;1135,25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian (min-max)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e247,98 (2,37-7543,27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTLR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5,51\u0026thinsp;\u0026plusmn;\u0026thinsp;3,58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian (min-max)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5,00 (2,00\u0026ndash;16,00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003eN\u003c/b\u003e: Number\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Distribution of Sociodemographic, Clinical, and Imaging Parameters\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eParameters\u003c/span\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eN\u003c/span\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e%\u003c/span\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePathological Subtype\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u003cp\u003eHCC\u003c/p\u003e\u003cp\u003eNon-HCC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e34,5\u003c/p\u003e\u003cp\u003e65,5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"10\" rowspan=\"11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCholangiocellular Carcinoma\u003c/p\u003e\u003cp\u003eMalignant Melanoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17,25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10,35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBreast Cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8,6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eColorektal Cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8,6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLung Cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5,2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGastric-Pancreatic Cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5,2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNeuroendocrine Carcinoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3,45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eParotid Cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGIST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSarcoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOvarian Cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTreatment Response\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65,5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34,5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLSF%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7,44\u0026thinsp;\u0026plusmn;\u0026thinsp;4,89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMedian (min-max)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6,00 (2,00\u0026ndash;19,00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTreatment Dose (mci)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43,46\u0026thinsp;\u0026plusmn;\u0026thinsp;6,76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMedian (min-max)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45,0 (27,0\u0026ndash;58,0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePrevious Therapies\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eCT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e77,5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eImmunotherapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13,7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eTACE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18,9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eTARE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20,6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHCC\u003c/b\u003e:hepatocellular carcinoma, \u003cb\u003eGIST\u003c/b\u003e:gastrointestinal stromal tumor, \u003cb\u003eCT\u003c/b\u003e:chemotherapy, \u003cb\u003eTACE\u003c/b\u003e: Transarterial chemoembolization, \u003cb\u003eTARE\u003c/b\u003e: Transarterial radioembolization, \u003cb\u003emci\u003c/b\u003e: milicurie, \u003cb\u003eSD\u003c/b\u003e: standart deviation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eRegarding the association of sociodemographic, clinical and imaging parameters with response pattern, MTV (p\u0026thinsp;=\u0026thinsp;0.004) and TLG (p\u0026thinsp;=\u0026thinsp;0.014) values were significantly lower in the response group. However, no statistically significant relationship was observed between age (p\u0026thinsp;=\u0026thinsp;0.617), age grouping (\u0026le;\u0026thinsp;65 and \u0026gt;\u0026thinsp;65; p\u0026thinsp;=\u0026thinsp;0.933), gender (p\u0026thinsp;=\u0026thinsp;0.566), SUVmax (p\u0026thinsp;=\u0026thinsp;0.987), TLR (p\u0026thinsp;=\u0026thinsp;0.974), pathologic subtype (p\u0026thinsp;=\u0026thinsp;0.952), LSF percentage (p\u0026thinsp;=\u0026thinsp;0.361) and treatment dose (p\u0026thinsp;=\u0026thinsp;0.272) and response pattern. Statistical analysis results of the comparison of sociodemographic, clinical and imaging parameters according to response groups are given in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of Sociodemographic, Clinical, and Imaging Parameters According to Treatment Response Groups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eTreatment Response\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;20\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;38\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e, Median (min-max)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59,00 (45\u0026ndash;79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59,00 (27\u0026ndash;81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.617\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge group, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14 (70,0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27 (71,1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.933\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (30,0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (50,0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (42,1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.566\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (50,0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22 (57,9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSUVmax\u003c/b\u003e, Median (min-max)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8,32 (2,94\u0026thinsp;\u0026minus;\u0026thinsp;20,81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8,64 (1,86\u0026thinsp;\u0026minus;\u0026thinsp;31,00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.987\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMTV\u003c/b\u003e, Median (min-max)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e144,60 (15,34-1139,12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42,18 (0,87\u0026ndash;599,98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTLG\u003c/b\u003e, Median (min-max)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e558,95 (49,04-7543,27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e201,32 (2,37-2543,83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTLR\u003c/b\u003e, Median (min-max)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5,00 (2,00\u0026ndash;13,00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5,00 (2,00\u0026ndash;16,00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.974\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePathology, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHCC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (35,0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (34,2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.952\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-HCC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13 (65,0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25 (65,8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLSF%\u003c/b\u003e, Median (min-max)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8,00 (2,00\u0026ndash;19,00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6,00 (2,00\u0026ndash;19,00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.361\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTreatment Dose\u003c/b\u003e, Median (min-max)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45,0 (30,0\u0026ndash;52,0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46,0 (27,0\u0026ndash;58,0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.272\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003ea\u003c/b\u003e:Mann Whitney U test, \u003cb\u003eb\u003c/b\u003e:Pearson Chi Square test\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSince the majority of the patients in the sample group (n\u0026thinsp;=\u0026thinsp;20) were HCC patients; in the comparison of sociodemographic, clinical and imaging parameters in the HCC-specific subgroup according to the response groups; TLG (p\u0026thinsp;=\u0026thinsp;0.049) values in HCC patients were found to be significantly lower in the response group. However, no statistically significant relationship was observed between age (p\u0026thinsp;=\u0026thinsp;0.183), gender (p\u0026thinsp;=\u0026thinsp;0.521), SUVmax (p\u0026thinsp;=\u0026thinsp;0.191), MTV (p\u0026thinsp;=\u0026thinsp;0.191), TLR (p\u0026thinsp;=\u0026thinsp;0.742), LSF % (p\u0026thinsp;=\u0026thinsp;0.780) and treatment dose (p\u0026thinsp;=\u0026thinsp;0.451) and response groups. The results of the analysis are given in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of Sociodemographic, Clinical, and Imaging Parameters by Treatment Response in HCC Patients\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eHCC-Response\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;7\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;13\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e, Median (min-max)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64,00 (55\u0026ndash;79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64,00 (27\u0026ndash;78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.183\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge group, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (57,1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (76,9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.613\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (42,9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0,0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.521\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (100,0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (84,6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSUVmax\u003c/b\u003e, Median (min-max)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7,37 (2,94\u0026thinsp;\u0026minus;\u0026thinsp;11,51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,62 (1,86\u0026thinsp;\u0026minus;\u0026thinsp;13,28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.191\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMTV\u003c/b\u003e, Median (min-max)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e109,58 (42,64\u0026ndash;612,68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78,73 (1,21\u0026ndash;599,98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.191\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTLG\u003c/b\u003e, Median (min-max)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e591,61 (131,04-2412,75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e258,13 (3,50-1913,02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.049\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTLR\u003c/b\u003e, Median (min-max)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,00 (2,00\u0026ndash;9,00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,00 (2,00\u0026ndash;12,00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.742\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLSF%\u003c/b\u003e, Median (min-max)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10,00 (3,00\u0026ndash;15,00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8,00 (3,00\u0026ndash;17,00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.780\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTreatment Dose\u003c/b\u003e, Median (min-max)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45,0 (32,0\u0026ndash;48,0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42,0 (28,0\u0026ndash;50,0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.451\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003ea\u003c/b\u003e:Mann Whitney U test, \u003cb\u003eb\u003c/b\u003e:Pearson Chi Square test\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the ROC analysis of the predictive value of imaging parameters in differentiating response to treatment. The AUC value of the MTV variable was calculated as 0.703 (95% CI: 0.597\u0026ndash;0.864) and the cut-off value was determined as \u0026le;\u0026thinsp;61.07. The sensitivity and specificity for MTV were 60.5% and 60.0%, respectively, and were found to be statistically significant in differentiating response to treatment (p\u0026thinsp;=\u0026thinsp;0.004). The AUC value of the TLG variable was calculated as 0.699 (95% CI: 0.558\u0026ndash;0.839) and the cut-off value was determined as \u0026le;\u0026thinsp;303.99. The sensitivity and specificity for TLG were 60.5% and 60.0%, respectively, and were statistically significant in discriminating response to treatment (p\u0026thinsp;=\u0026thinsp;0.014).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAnalysis of the Predictive Value of Imaging Parameters in Differentiating Treatment Response\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAUC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e%95 CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCut-off\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSensitivity (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSpecificity (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSUVmax\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.501\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.353\u0026ndash;0.650\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;8,83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e52,6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e50,0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.987\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMTV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.703\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.597\u0026ndash;0.864\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;61,07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e60,5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e60,0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTLG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.699\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.558\u0026ndash;0.839\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;303,99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e60,5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e60,0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.503\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.352\u0026ndash;0.653\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;4,50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e47,4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e55,0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.974\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLSF%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.573\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.413\u0026ndash;0.733\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;6,50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e57,9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e55,0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.364\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eAUC\u003c/b\u003e: Area under the curve, \u003cb\u003e%95CI\u003c/b\u003e: Confidence interval\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eLiver tumors, whether primary or metastatic, pose a significant clinical challenge due to their poor prognosis and limited treatment options. Transarterial radioembolization (TARE) with 90Y-labeled microspheres has emerged as an important locoregional therapy, particularly in patients ineligible for surgical resection or systemic therapy. While traditionally applied in hepatocellular carcinoma (HCC), its use has expanded to various metastatic liver tumors, demonstrating variable effectiveness depending on tumor biology and patient characteristics (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn our study, we evaluated the predictive value of 18F-FDG PET/CT-derived metabolic parameters (SUVmax, MTV, TLG, TLR) and hepatopulmonary shunt fraction (LSF) in assessing response to TARE. Among these, MTV and TLG showed significant association with treatment response. Specifically, lower MTV and TLG values were observed in responders (p\u0026thinsp;=\u0026thinsp;0.004 and p\u0026thinsp;=\u0026thinsp;0.014, respectively). This suggests that tumors with lower metabolic burden respond more favorably to TARE. These findings align with prior studies indicating that volumetric PET parameters, which integrate tumor activity and extent, provide superior prognostic insight compared to point measurements like SUVmax (\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). ROC analysis further supported the predictive role of metabolic parameters. The area under the curve (AUC) was 0.703 for MTV and 0.699 for TLG, indicating a good level of diagnostic accuracy. The identified cut-off values (MTV\u0026thinsp;\u0026le;\u0026thinsp;61.07 and TLG\u0026thinsp;\u0026le;\u0026thinsp;303.99) showed balanced sensitivity and specificity (both approximately 60%), suggesting these thresholds may be useful in clinical practice to distinguish responders from non-responders. Among the two, TLG offers a more integrated assessment by combining metabolic activity and volume, potentially improving sensitivity in tumors with heterogeneous uptake.\u003c/p\u003e\u003cp\u003eTLG, which combines metabolic activity (SUVmean) and volume (MTV), appears particularly useful in capturing intratumoral heterogeneity. This may explain why, in the HCC subgroup, TLG (but not MTV) maintained a statistically significant relationship with response (p\u0026thinsp;=\u0026thinsp;0.049). HCC is known for its biological and metabolic heterogeneity, and TLG may better reflect this complexity than MTV alone (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Our results support the growing consensus that TLG is a more comprehensive and sensitive parameter, especially in tumors with variable FDG uptake patterns.\u003c/p\u003e\u003cp\u003eIn contrast, no significant association was found between SUVmax or TLR and treatment response. While both parameters have been reported in the literature to correlate with tumor aggressiveness or treatment outcomes (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), their predictive value may be limited in heterogeneous cohorts. In our population, the inclusion of multiple tumor types with varying metabolic profiles and prior treatments likely contributed to the lack of statistical significance. Additionally, SUVmax captures only the most active voxel and may not represent the overall tumor behavior.\u003c/p\u003e\u003cp\u003eThe role of LSF in predicting treatment response remains uncertain. In our study, LSF was not significantly associated with treatment response in either the overall cohort or the HCC subgroup. Although high LSF can theoretically reduce therapeutic efficacy by diverting activity from the liver to the lungs, its primary clinical relevance may lie in safety and dosimetry planning rather than direct response prediction (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Some studies have reported that LSF has stronger associations with survival metrics rather than immediate imaging response (\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAnother key observation was the lack of a statistically significant relationship between administered dose and treatment response. While some studies suggest higher doses correlate with improved outcomes (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), our results highlight the limitations of the BSA-based dosimetry model. The BSA method does not account for tumor-specific metabolic activity or spatial distribution, which are critical factors in achieving effective radiation delivery (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). The discrepancy between planned and actual microsphere distribution particularly in the setting of 99mTc-MAA\u0026rsquo;s imperfect simulation of 90Y biodistribution may also contribute to the variability in response. Furthermore, individual tumor biology and prior systemic treatments likely modulate radiation sensitivity, complicating the dose-response relationship.\u003c/p\u003e\u003cp\u003eIn addition to imaging-based parameters, demographic and clinical variables such as age, gender, administered treatment dose, and LSF values were also assessed for their association with treatment response. In both the overall patient group and the HCC subgroup, none of these variables showed a statistically significant relationship with treatment response (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). These findings are consistent with previous studies that report limited predictive value of demographic factors in TARE outcomes (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). However, imaging-based metabolic parameters demonstrated a more distinct association with therapeutic response, emphasizing their prognostic importance.\u003c/p\u003e\u003cp\u003eTaken together, our findings emphasize the prognostic value of volumetric PET parameters in predicting response to TARE. TLG, in particular, may offer a more accurate reflection of tumor burden and heterogeneity, and thus better inform treatment planning and response evaluation. Conversely, parameters such as LSF and SUVmax may have limited predictive power in heterogeneous patient groups.\u003c/p\u003e\u003cp\u003eLimitations of the study include the relatively small sample size, the heterogeneous tumor population, and the reliance on a non-personalized dosimetry model. Future research with larger cohorts, tumor-specific subgroups, and advanced dosimetry approaches (e.g., partition model or voxel-based dosimetry) will enhance the understanding of predictive imaging biomarkers and optimize TARE outcomes.\u003c/p\u003e\u003cp\u003eIn conclusion, volumetric metabolic parameters such as MTV and especially TLG are valuable tools in predicting response to TARE in liver tumors. Integrating these biomarkers into clinical decision-making may support individualized treatment strategies and improve therapeutic success.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u0026nbsp;\u003c/strong\u003eThe authors declare no conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e All authors contributed to the study conception and design. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval:\u0026nbsp;\u003c/strong\u003eAll procedures performed in studies involving human participants were in accordance with\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ethe ethical standards of the institutional and/or national research committee and with the 1964\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eHelsinki declaration and its later amendments or comparable ethical standards. Informed\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003econsent was obtained from all individual participants included in the study (Decision No: 2024/67)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Information:\u0026nbsp;\u003c/strong\u003eNo funding was received for conducting this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e We are deeply grateful to all those who played a role in the success of this project. Conceptualization: Ertan Sahin \u0026nbsp; and Merve Okuyan.; methodology: Merve Okuyan; software: Ertan Sahin; validation: Umut Elboga; formal analysis: Ertan Sahin \u0026nbsp;and Merve Okuyan; investigation: Merve Okuyan; data curation: Merve Okuyan; writing\u0026mdash;original draft preparation: Merve Okuyan; writing-review and editing: Merve Okuyan; visualization: Ertan Sahin; supervision: Umut Elboga; Project administration: Ertan Sahin.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVi\u0026ntilde;al D, Minaya-Bravo A, Prieto I, Feliu J, Rodriguez-Salas N. Ytrrium-90 transarterial radioembolization in patients with gastrointestinal malignancies. Clin Transl Oncol. 2022;24(5):796\u0026ndash;808.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXing M, Lahti S, Kokabi N, Schuster DM, Camacho JC, Kim HS. 90Y Radioembolization Lung Shunt Fraction in Primary and Metastatic Liver Cancer as a Biomarker for Survival. Clin Nucl Med. 2016;41(1):21\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePuranik AD, Rangarajan V, Gosavi A, et al. Prognostic value of imaging-based parameters in patients with intermediate-stage hepatocellular carcinoma undergoing transarterial radioembolization. Nucl Med Commun. 2021;42(3):337\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSarikaya I, Sarikaya A. Assessing PET Parameters in Oncologic 18F-FDG Studies. J Nucl Med Technol. 2020;48(3):278\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHofheinz F, B\u0026uuml;tof R, Apostolova I, et al. An investigation of the relation between tumor-to-liver ratio (TLR) and tumor-to-blood standard uptake ratio (SUR) in oncological FDG PET. EJNMMI Res. 2016;6(1):19.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePak K, Cheon GJ, Nam HY, et al. Prognostic value of metabolic tumor volume and total lesion glycolysis in head and neck cancer: a systematic review and meta-analysis. J Nucl Med. 2014;55(6):884\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUlaner GA, Eaton A, Morris PG, et al. Prognostic value of quantitative fluorodeoxyglucose measurements in newly diagnosed metastatic breast cancer. Cancer Med. 2013;2(5):725\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTrojan J, Schroeder O, Raedle J, et al. Fluorine-18 FDG positron emission tomography for imaging of hepatocellular carcinoma. Am J Gastroenterol. 1999;94(11):3314\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHwang SH, Lee JW, Cho HJ, Kim KS, Choi GH, Yun M. Prognostic Value of Metabolic Tumor Volume and Total Lesion Glycolysis on Preoperative 18F-FDG PET/CT in Patients With Very Early and Early Hepatocellular Carcinoma. Clin Nucl Med. 2017;42(1):34\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiao X, Wei J, Li Y, et al. 18F-FDG PET with or without CT in the diagnosis of extrahepatic metastases or local residual/recurrent hepatocellular carcinoma. Med (Baltim). 2018;97(34):e11970.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKinkel K, Lu Y, Both M, Warren RS, Thoeni RF. Detection of hepatic metastases from cancers of the gastrointestinal tract by using noninvasive imaging methods (US, CT, MR imaging, PET): a meta-analysis. Radiology. 2002;224(3):748\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHirata K, Kobayashi K, Wong KP, et al. A semi-automated technique determining the liver standardized uptake value reference for tumor delineation in FDG PET-CT. PLoS ONE. 2014;9(8):e105682.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVente MAD, Wondergem M, van der Tweel I, van den Bosch MAAJ, Zonnenberg BA, Lam MGEH, et al. Yttrium-90 microsphere radioembolization for the treatment of liver malignancies: a structured meta-analysis. Eur Radiol. 2009;19(4):951\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHwang SH, Hong HS, Kim D, et al. Total Lesion Glycolysis on 18F-FDG PET/CT Is a Better Prognostic Factor Than Tumor Dose on 90Y PET/CT in Patients With Hepatocellular Carcinoma Treated With 90Y Transarterial Radioembolization. Clin Nucl Med. 2022;47(6):e437\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKarahan Şen NP, Alataş \u0026Ouml;, G\u0026uuml;lc\u0026uuml; A, \u0026Ouml;zdoğan \u0026Ouml;, Derebek E, \u0026Ccedil;apa Kaya G. The role of volumetric and textural analysis of pretreatment 18F-fluorodeoxyglucose PET/computerized tomography images in predicting complete response to transarterial radioembolization in hepatocellular cancer. Nucl Med Commun. 2022;43(7):807\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBurger IA, Casanova R, Steiger S, et al. 18F-FDG PET/CT of Non-Small Cell Lung Carcinoma Under Neoadjuvant Chemotherapy: Background-Based Adaptive-Volume Metrics Outperform TLG and MTV in Predicting Histopathologic Response. J Nucl Med. 2016;57(6):849\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYamashita T, Forgues M, Wang W, et al. EpCAM and alpha-fetoprotein expression defines novel prognostic subtypes of hepatocellular carcinoma. Cancer Res. 2008;68(5):1451\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKalasekar SM, VanSant-Webb CH, Evason KJ. Intratumor Heterogeneity in Hepatocellular Carcinoma: Challenges and Opportunities. Cancers (Basel). 2021;13(21).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKużdżał B, Moszczyński K, Żanowska K, et al. Correlation between 18-FDG standardized uptake value and tumor grade in patients with resectable non-small cell lung cancer. Transl Cancer Res. 2023;12(12):3530\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang C, Zhao K, Hu S, et al. The PET-Derived Tumor-to-Liver Standard Uptake Ratio (SUV TLR) Is Superior to Tumor SUVmax in Predicting Tumor Response and Survival After Chemoradiotherapy in Patients With Locally Advanced Esophageal Cancer. Front Oncol. 2020;10:1630.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGarin E, Lenoir L, Rolland Y, et al. Dosimetry based on 99mTc-macroaggregated albumin SPECT/CT accurately predicts tumor response and survival in hepatocellular carcinoma patients treated with 90Y-loaded glass microspheres: preliminary results. J Nucl Med. 2012;53(2):255\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGaba RC, Zivin SP, Dikopf MS, et al. Characteristics of primary and secondary hepatic malignancies associated with hepatopulmonary shunting. Radiology. 2014;271(2):602\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGosavi A, Puranik AD, Shah S, et al. Prognostic value of lung shunt fraction in hepatocellular carcinoma and unresectable liver dominant metastatic colorectal cancer undergoing transarterial radioembolisation. Nucl Med Commun. 2022;43(1):24\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDeipolyi AR, Iafrate AJ, Zhu AX, Ergul EA, Ganguli S, Oklu R. High lung shunt fraction in colorectal liver tumors is associated with distant metastasis and decreased survival. J Vasc Interv Radiol. 2014;25(10):1604\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDas A, Riaz A, Gabr A, et al. Safety and efficacy of radioembolization with glass microspheres in hepatocellular carcinoma patients with elevated lung shunt fraction: analysis of a 103-patient cohort. Eur J Nucl Med Mol Imaging. 2020;47(4):807\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCheng B, Villalobos A, Sethi I, et al. Determination of Tumor Dose Response Thresholds in Patients with Chemorefractory Intrahepatic Cholangiocarcinoma Treated with Resin and Glass-based Y90 Radioembolization. Cardiovasc Intervent Radiol. 2021;44(8):1194\u0026ndash;203.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSankhla T, Cheng B, Nezami N, et al. Role of Resin Microsphere Y90 Dosimetry in Predicting Objective Tumor Response, Survival and Treatment Related Toxicity in Surgically Unresectable Colorectal Liver Metastasis: A Retrospective Single Institution Study. Cancers (Basel). 2021;13:19.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCheng B, Sethi I, Davisson N, et al. Yttrium-90 dosimetry and implications on tumour response and survival after radioembolisation of chemo-refractory hepatic metastases from breast cancer. Nucl Med Commun. 2021;42(4):402\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKao YH, Tan EH, Ng CE, Goh SW. Clinical implications of the body surface area method versus partition model dosimetry for yttrium-90 radioembolization using resin microspheres: a technical review. Ann Nucl Med. 2011;25(7):455\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Transarterial radioembolization, FDG PET/CT, Metabolic tumor volume (MTV), Total lesion glycolysis (TLG)","lastPublishedDoi":"10.21203/rs.3.rs-7429304/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7429304/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e\u003cp\u003eThis study aimed to investigate the predictive value of 18F-FDG PET/CT-derived metabolic parameters (SUVmax, MTV, TLG, TLR) and hepatopulmonary shunt fraction (LSF) on the treatment response of transarterial radioembolization (TARE) in patients with primary and metastatic liver tumors.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA total of 58 patients who underwent TARE between March 2024 and March 2025 were included. Prior to treatment, all patients underwent 18F-FDG PET/CT and 99mTc-MAA SPECT/CT imaging. PET-based parameters and LSF values were calculated. Treatment response was assessed at 1 and 6 months post-treatment based on mRECIST criteria. Responders were defined as patients with complete or partial response. Statistical analyses included Mann-Whitney U test, Chi-square test, and ROC analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eOf 58 patients, 65.5% (n\u0026thinsp;=\u0026thinsp;38) were responders. Among PET parameters, lower MTV and TLG values were significantly associated with treatment response (p\u0026thinsp;=\u0026thinsp;0.004 and p\u0026thinsp;=\u0026thinsp;0.014, respectively). TLG also demonstrated significance in the HCC subgroup (p\u0026thinsp;=\u0026thinsp;0.049). SUVmax, TLR, LSF, and administered dose showed no significant association with response. ROC analysis revealed good predictive value for MTV (AUC\u0026thinsp;=\u0026thinsp;0.703) and TLG (AUC\u0026thinsp;=\u0026thinsp;0.699), with respective cut-off values of \u0026le;\u0026thinsp;61.07 and \u0026le;\u0026thinsp;303.99. Demographic and clinical variables such as age, gender, LSF, and dose were not predictive of treatment response.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eVolumetric metabolic parameters, especially TLG, are effective predictors of response to TARE in liver tumors. Incorporating these parameters into clinical evaluation may enhance treatment planning and prognostic estimation. LSF and conventional dose estimation via BSA appear less predictive of localized treatment response.\u003c/p\u003e","manuscriptTitle":"Clinical and Metabolic Predictors of Response to Transarterial Radioembolization in Primary and Metastatic Liver Tumors: The Role of 18F-FDG PET/CT and Lung Shunt Fraction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-09 15:56:05","doi":"10.21203/rs.3.rs-7429304/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"edc54fbe-a211-42f0-ace3-abc76ad4b387","owner":[],"postedDate":"September 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-29T16:58:37+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-09 15:56:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7429304","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7429304","identity":"rs-7429304","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}