Quantitative analysis of tumor perfusion via contrast-enhanced ultrasound to predict the neoadjuvant chemotherapy efficacy for children with hepatoblastoma

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Quantitative analysis of contrast-enhanced ultrasound (CEUS) has been used to predict the chemotherapy efficacy in various types of tumors, but its value in hepatoblastoma has not been fully evaluated. Objective To explore the value of quantitative analysis of CEUS in predicting the neoadjuvant chemotherapy efficacy for hepatoblastoma. Materials and methods Thirty-five hepatoblastoma patients who require neoadjuvant chemotherapy and underwent liver CEUS in our hospital from January 2017 to July 2023 were enrolled. A CEUS examination was performed at baseline, and perfusion parameters were obtained via perfusion quantification software. Patients were classified into responder group and non-responder group after 2 courses. The differences in quantitative parameters between two groups were compared. Results The MeanLin ratio (1.7 vs. 0.6 and 1.7 vs. 0.8), PE ratio (1.8 vs. 0.9 and 1.9 vs. 0.9), WiAUC ratio (1.3 vs. 0.6 and 1.3 vs. 0.6), WiPI ratio (1.9 vs. 0.8 and 2.0 vs. 0.9), WoAUC ratio (1.3 vs. 0.5 and 1.3 vs. 0.7), and WiWoAUC ratio (1.3 vs. 0.6 and 1.3 vs. 0.7) in the response group were significantly higher than those in the non-response group (all p < 0.05); when tumor’s region of interest was the active area, the WiR ratio (3.1 vs. 1.4) of the responder group was significantly higher ( p = 0.04). The proportion of lesions with liquefaction necrosis before chemotherapy was significantly higher in the response group ( p = 0.049). Conclusion Quantitative analysis of CEUS may have the potential to predict the neoadjuvant chemotherapy efficacy for hepatoblastoma. Hepatoblastoma Pediatrics Contrast-enhanced ultrasound Quantitative Analysis Figures Figure 1 Figure 2 Figure 3 Introduction Hepatoblastoma (HB), which accounts for approximately 1% of all childhood cancers, is the most common malignant liver tumor in children under 5 years old [ 1 ]. Neoadjuvant chemotherapy followed by surgical resection is the cornerstone of treatment for hepatoblastoma[ 2 ]. Approximately two-thirds of patients with HB are not eligible for surgical resection at diagnosis and require neoadjuvant chemotherapy to downstage and to achieve complete tumor resection[ 3 ]. However, some of them are not sensitive to neoadjuvant chemotherapy[ 4 ]. For these patients, neoadjuvant chemotherapy not only prolongs the course but also reduces the tolerance of surgery. Interventional therapy, targeted drugs, and immunotherapy are their options[ 5 ]. Therefore, predicting the response of neoadjuvant chemotherapy may help choose appropriate treatment strategy for patients with newly diagnosed HB. Several studies have reported some genetic phenotypes and transcriptomic signature of hepatoblastoma might correlate with chemotherapy resistance response[ 6 – 8 ]. Nevertheless, molecular testing is expensive and time-consuming. Currently, some researches have investigated the value of contrast-enhanced computed tomography radiomics in predicting the response of neoadjuvant chemotherapy of HB and attained preliminary results[ 9 , 10 ]. It seems that the vascular perfusion of HB before neoadjuvant chemotherapy contains the useful information that can reflect the response of neoadjuvant chemotherapy. However, radiomics faces several challenges that need to be addressed, including but not limited to the explainability of the models, the reproducibility of the quantitative imaging features, and the sensitivity to variations in image acquisition and reconstruction parameters[ 11 ]. Contrast enhanced-ultrasound (CEUS) is a safe, convenient and easily repeatable imaging technique that can reflect real-time blood perfusion of lesions. Quantitative analysis of CEUS is a technique for obtaining quantitative parameters of tissue or tumor perfusion during CEUS by using specific models and functions, the use of which enables early diagnosis of tumors and early dynamic assessment of the efficacy of anti-tumor therapy[ 12 ]. Quantitative analysis of CEUS has been used to predict the response of chemotherapy in various types of solid tumors[ 13 – 16 ]. However, there is a lack of studies related to predicting the efficacy of neoadjuvant chemotherapy for hepatoblastoma in children by quantitative analysis of CEUS. This study was aimed to explore the value of quantitative analysis of CEUS images of hepatoblastoma in predicting the efficacy of neoadjuvant chemotherapy for children with hepatoblastoma. Methods Patient selection This study was approved by the institutional review board of our central. Prior to underwent CEUS examination, informed parental consent was obtained for each patient. Patients with HB admitted to our hospital from January 2017 to July 2023 were retrospectively collected. The inclusion criteria were as follows: (1) initially diagnosed as hepatoblastoma with histopathological confirmation; (2) had enhanced CT and CEUS examination before neoadjuvant chemotherapy within 1 week; and (3) received neoadjuvant chemotherapy based on the CCCG-HB-2016 protocol[17] ;The exclusion criteria were as follows: (1) incomplete clinical data before and after neoadjuvant chemotherapy; and (2) poor CEUS image quality (evaluated by experienced radiologist) or unavailable dynamic CEUS images (Fig. 1). Clinical staging and risk-stratified staging The clinical stage of the tumor was evaluated using the Pretreatment Extent of Disease (PRETEXT) system by a radiologist (C.Y.X., with 7 years of experience in pediatric radiology) based on CT imaging findings before chemotherapy[18]. The risk-stratified of the tumor was evaluate using the Children’s Hepatic tumors International Collaboration—Hepatoblastoma Stratification system (CHIC-HS)[19]. Efficacy evaluation criteria To monitor the chemotherapy response, the follow-up imaging examination was performed after two cycles of neoadjuvant chemotherapy. In cases where patients received contrast-enhanced computed tomography examination after neoadjuvant chemotherapy, the Response Evaluation Criteria in Solid Tumors 1.1 (RECIST 1.1) was used preferentially to evaluate the effect of chemotherapy[20]. With RECIST 1.1 criteria, a 70% decline detected in the maximum diameters of the target lesions without progression of other lesions and appearance of new lesions was considered responders. In patients who did not undergo contrast-enhanced computed tomography after chemotherapy but had an ultrasound examination, the efficacy was evaluated by combining serum alpha-fetoprotein (AFP) levels and ultrasound examination. A 70% decline in the maximum diameters of the target lesions with a 90% decline in serum AFP levels were considered responders. Contrast‑enhanced ultrasound technique An Aplio 500 (Canon, Tokyo, Japan) equipped with a 1.0-6.0 MHz curvilinear transducer, an Aplio 900 (Canon, Tokyo, Japan) equipped with a 1.0-6.0 MHz curvilinear transducer and an Aixplorer (Supersonic, Provence, France) equipped with a 1.0-6.0 MHz curvilinear transducer SC6-1 were used to perform conventional ultrasound and CEUS examinations by a radiologist (Z.L.Y, with 10 years of experience in liver CEUS). Conventional ultrasound examination was performed before CEUS. If the patient presents with multiple lesions, the largest tumor was selected as the target lesion. The ultrasound imaging characteristics of the target lesion were recorded, including morphology, boundary, internal echo, and calcification. Subsequently, the section that displayed the maximum view of the tumor as well as a part of normal liver parenchyma was selected to perform CEUS. Then, the contrast-specific imaging mode was activated with a mechanical index <0.1. Following the recommendation of the Food and Drug Administration, the dose of contrast agent (SonoVue; Bracco, Milan, Italy) was 0.03 ml/ kg, up to a maximum of 2.4 mL. A bolus injection of the appropriate dose of contrast agent was administered through a vascular catheter needle placed in a superficial vein, followed by a 5 mL flush of 0.9% saline. A timer was started immediately after the injection. The target lesions were continuously observed for the first 60s, and then intermittently observed every 60 seconds until 5 minutes later. images and videos were recorded for further analysis. Image analysis Quantification of dynamic sequences was performed by a radiologist (C.M.X., with 3 years of experience in liver CEUS) using perfusion quantification software (VueBox, Bracco). There are two types of regions of interest (ROI) for target lesions: one covering the entire lesion and the other covering the active area with enhancement on CEUS. The ROI of the surrounding liver parenchyma was also delineated and should avoid larger vessels. The movement of lesion caused by respiration were compensated by the automatic respiratory motion compensation function. The following perfusion parameters of the target lesion and surrounding liver parenchyma were obtained: average contrast signal intensity (MeanLin), peak enhancement (PE), rising time (RT), fall time (FT), time to peak (TTP), mean transit time (mTT), wash-in area under the curve (WiAUC), wash-in rate (WiR), wash-in perfusion index (WiPI), wash-out area under the curve (WoAUC), wash-out rate (WoR) and WiAUC+WoAUC (WiWoAUC). WiPI was a ratio of WiAUC to RT. At the same time, the ratio of the lesion to the surrounding liver parenchyma was calculated: MeanLin ratio, PE ratio, RT ratio, FT ratio, TTP ratio, mTT ratio, WiAUC ratio, WiR ratio, WiPI ratio, WoAUC ratio, WoR ratio and WiWoAUC ratio. Quantification of target lesions was performed in duplicate at different time points at least 1 week apart by the same radiologist (C.M.X.) to evaluate intra-observer reproducibility. The enhancement level (hyper/iso/hypoenhancement), extent of liquefaction necrosis, the time to achieve full tumor enhancement, the time to washout to equal enhancement, and the time to washout to low enhancement were also reviewed. Statistical analyses Statistical analysis was performed using SPSS Version 26.0 (IBM, Armonk, NY, USA). Continuous variables were first analyzed for normality of distribution using the Kolmogorov-Smirnov test of normality. Continuous variables that conformed to the normal distribution are expressed as the mean ± standard deviation and those that did not are expressed as the median (interquartile range). Categorical variables are expressed as medians and ranges. Categorical variables were compared using Fisher’s exact test. Continuous variables were compared using the Mann-Whitney test. The Mann−Whitney non-parametric U-test was used to compare differences between responders and non-responders. Kappa test was used to evaluate the consistency of two efficacy evaluation indicators. The Intra-class correlation coefficient (ICC) was used to assess the reproducibility of intra-observer perfusion parameters. All differences with p < 0.05 were considered significant. Results Patient and lesion characteristics A total of 35 cases were included in the study. After two cycles of neoadjuvant chemotherapy, 27 patients were evaluated according to RECIST1.1 criteria, and 8 were evaluated according to the criteria that combined serum AFP levels with abdominal ultrasound. According to these criteria, 16 patients were considered to be responders and 19 patients were considered to be non-responders. Demographic and clinical data of the study population are outlined in Table 1. The median age of patients was 15 months. Twenty-eight (80.0%) were male. Serum AFP level at diagnosis were higher than normal in all patients and the median AFP at diagnosis was 293,655ng/mL. The average maximum diameter of all target lesions was 11.5±2.7 cm, 3 (5.7%) patients had multifocal lesions, and 3 (5.7%) manifested distant metastases. There was no significant difference in clinical characteristics between the responder and non-responder groups. Conventional and contrast-enhanced ultrasound characteristics of HB in the study population are outlined in Table 2. The proportion of lesions with liquefaction necrosis was significantly higher in the responder group than in the non-responder group ( p < 0.05). Consistency of the two evaluation criteria Of the 35 patients, 21 patients had the results of serum AFP level, chest and abdominal enhanced CT examination, and liver ultrasound after 2 courses of chemotherapy. The Kappa value of the two efficacy evaluation criteria for the 21 patients was 0.904 (Table 3). Only one patient showed a difference in the evaluation results. The AFP baseline level of this patient was 309.9 ng/mL. And he was assessed as responder by RECIST 1.1, but non-responder by the other criterion due to a decrease in serum AFP was less than 90% after two cycles. Reproducibility of perfusion parameters Intra-observer reproducibility of perfusion parameters is outlined in Table S1. For amplitude parameters, the ICC range for correlations between Intra-observer quantification was 0.803−0.999. For time parameters, the ICC range for correlations between Intra-observer quantification was 0.620−0.936. Correlation of CEUS quantitative parameters with tumor response Table 4 and Table S2 show the results of perfusion parameters between the responder group and the non-responder group in the case of the two tumor ROIs, respectively. The results were similar for both tumor ROIs. The amplitude parameters ratio including MeanLin ratio, PE ratio, WiAUC ratio, WiPI ratio, WoAUC ratio, and WiWoAUC ratio of the responder group were significantly higher than those of the non-responder group ( p < 0.05). And when the tumor ROI only included tumor active region, the WiR ratio of the responder group was significantly higher than that of the non-responder group ( p < 0.05). Differences between the patients in the responder and non-responder groups are illustrated in Figures 2 and 3. Discussion This study collected 35 patients with hepatoblastoma requiring neoadjuvant chemotherapy and analyzed the relationship between pre-chemotherapy CEUS perfusion parameters and chemotherapy efficacy after 2 courses. Our finding shows that amplitude parameters ratio in the responder group, including MeanLin ratio, PE ratio, WiAUC ratio, WiPI ratio, WoAUC ratio, and WiWoAUC ratio, were significantly higher than those in the non-responder group. Moreover, in case of ROI of tumor was the active area, the WiR ratio of the responder group was significantly higher than that of the non-responder group. All these findings indicates that quantitative analysis of CEUS might have the potential to predict the efficacy of neoadjuvant chemotherapy for hepatoblastoma and facilitate the precise treatment of hepatoblastoma. On CEUS, perfusion parameters can be obtained by continuously measuring the change in contrast agents concentration in ROI over time using quantitative analysis software. In the present study, the responder group exhibited greater amplitude parameters. Studies have found that the greater the amplitude parameter such as PE, the higher the angiogenesis and the richer the blood supply[ 21 ]. The proportion of lesions with liquefaction necrosis was also significantly higher in the responder group. The rapid growth of tumor may result in the local liquefaction necrosis due to relative nutrient deficiency. Consequently, the presence of liquefaction necrosis in untreated tumors may indicate that the tumors are more active and have richer blood supply. Based on these findings, it can be postulated that hepatoblastoma with a high angiogenesis and rich blood supply would respond better to neoadjuvant chemotherapy. Previous studies employed other imaging modalities to assess the efficacy of targeted therapy in hepatocellular carcinoma had similar findings. Chen et al. analyzed MRI blood flow parameters of patients received systemic therapy and found that patients with high peak intensity before treatment had longer overall survival than those with low peak intensity[ 22 ]. Ippolito et al. analyzed CT perfusion parameters of patients treated with sorafenib and found that compared with progressor group, the non-progressor group had significantly higher hepatic perfusion, TTP, and arterial perfusion at baseline[ 23 ]. Investigators explained that a higher vascularization of lesions may be responsible for their higher response rate to antiangiogenic treatment. Although patients in present study did not receive antiangiogenic treatment, the cytotoxicity of chemotherapeutic agents play an important role in destructing tumor vessels. Saxena et al. found evident vascular changes in HB after chemotherapy, including mild intimal proliferation, complete occlusion of the vessels by myxomatous tissue, and thickening of vessel walls by dense hyalinized tissue[ 24 ]. In contrast to studies focusing on analyzing the correlation between the changes in perfusion parameters during treatment and long-term efficacy[ 13 , 15 , 16 ], the present study has attempted to predict post-treatment outcomes based on pre-treatment perfusion parameters, which may help to identify poor responders prior to routine treatment, providing an opportunity for early individualized management. It should be noted that in order to expand the sample size, two evaluation criteria were employed in this study: RECIST1.1 and serum AFP combined with abdominal ultrasound. RECIST1.1 with CT or MRI as imaging tool represents the gold standard for evaluating the efficacy of solid tumors and is popularly used in clinical practice. However, ultrasound was preferred to be chosen as the monitoring tool by some of parents due to its non-radiation nature. However, ultrasound is limited by operator proficiency and the measurement accuracy for large tumor is also affected by the display range. Accordingly, we built up another criteria that combined a decrease in serum AFP levels exceeding 90%[ 25 , 26 ] and the results of ultrasound for assessing the efficacy of children without CT findings. In this study, this criteria exhibits a high agreement with the RECIST 1.1, providing evidence of the reliability and feasibility of this assessment method. Our study has several limitations. First, this study is a single-center study with a small sample size, and the above findings are only preliminary results. Second, we did not conduct further multivariate analysis and subgroup analyses of the different chemotherapy regimens due to the small sample size. It would be beneficial to consider expanding the sample size and conducting a multicenter study in the future. Conclusion Quantitative analysis of CEUS and ultrasound characteristic have the potential to predict the efficacy of neoadjuvant chemotherapy for hepatoblastoma. Further investigation of these findings in a larger population is warranted. Declarations Author Contribution C.M.X wrote the main manuscript text, organized and analyze data. W.Y.Q, C.Y.X and J.H organized and collected data. Z.W.Y and W.X.L prepared figures and tables. X.X.Y, H.G.L and Z.L.Y helped supervise the project and conceived the original idea. All authors reviewed the manuscript. 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J Clin Oncol 28:2584–2590. https://doi.org/10.1200/JCO.2009.22.4857 Tables Table 1 Demographic and clinical data of the patients Characteristic All patients (n = 35) Responder group (n=16) Non-responder group (n=19) p value Sex (male) 28 13 15 1.00 Age (month) 15 (8-32) 16 (11-34) 12 (5-32) 0.23 Serum AFP level (ng/mL) 293,654.5 (40,668.5 - 915,793.0) 388,010.5 (62,696.0 - 2,000,000) 289,156.5 (23,960.2-496,142.0) 0.20 PLT（U/L） 576.2±251.3 587.3±55.6 566.8±64.0 0.81 NRL 0.6 (0.3-1.0) 0.7 (0.3-1.6) 0.5 (0.3-1.0) 0.50 PRL 122.0 (71.0-169.2) 129.6 (97.3-166.2) 97.9 (63.6-176.0) 0.40 Tumor number (>1) 3 1 2 1.00 Maximum tumor size (cm) 11.5±2.7 12.3±2.4 10.8±2.8 0.11 Tumor volume (cm 3 ) 417.5 (260.6-596.6) 469.4 (294.8-672.1) 334.0 (238.3-549.7) 0.18 PRETEXT stage 0.37 Stage I 5 4 1 Stage II 18 7 11 Stage III 11 5 6 Stage IV 1 0 1 Distant metastases 3 2 1 0.58 VPEFR+ 14 5 9 0.49 CHIC-HS 0.60 Low risk 18 9 9 Median risk 14 5 9 High risk 3 2 1 Pathological type 0.83 Pure fetal with low mitotic activity 10 5 5 Mixed epithelial 11 5 6 Mixed epithelial and mesenchymal 13 5 8 AFP, alpha-fetoprotein; PLT, platelet; NRL, neutrophil lymphocyte ratio; PRL, platelet lymphocyte ratio; PRETEXT, Pretreatment Extent of Disease; VPEFR+: macrovascular involvement of all hepatic veins (V) or portal bifurcation (P), contiguous extrahepatic tumor (E), multifocal tumor (F), and spontaneous rupture (R); M+: distant metastasis; CHIC, Children’s Hepatic tumors International Collaboration—Hepatoblastoma Stratification system Table 2 Conventional and contrast-enhanced ultrasound characteristics of recruited patients Characteristic All patients (n = 35) Responder group (n=16) Non-responder group (n=19) p value Shape 0.70 Round or oval 9 5 4 Lobular or irregular 26 11 15 Echogenicity 0.70 Homogeneous 8 3 5 Inhomogenous 27 13 14 Boundary 0.72 Well defined 24 10 14 Poorly defined 11 6 5 Calcification 25 3 7 0.29 Liquefactive necrosis 9 7 2 0.049 Enhanced level at arterial phase 0.72 Homogeneous hyperenhancement 10 4 6 Inhomogenous hyperenhancement 25 12 13 whole tumor enhancement time (s) 11 (9-13) 11 (10-13) 11 (9-13) 1.00 Washout to isoenhancement time (s) 20 (17-24) 223 (17-26) 18 (16-23) 0.14 Washout to hypoenhancement time(s) 33 (27-55) 34 (26-48) 33 (27-58) 0.68 Table 3 Consistency of the two evaluation criteria Combined serum AFP levels with abdominal ultrasound RECIST1.1 Total Responder group Non-responder group Responder group 9 0 9 Non-responder group 1 11 12 Total 10 11 21 Table 4 Contrast-enhanced ultrasound quantitative parameters of the two groups (tumor ROI: entire tumor) Parameter Responder group (n=16) Non-responder group (n=19) p value MeanLin ratio 1.7 (1.1-2.5) 0.6 (0.5-1.5) 0.003 PE ratio 1.8 (1.1-3.1) 0.9 (0.7-1.4) 0.005 WiAUC ratio 1.3 (0.7-1.8) 0.6 (0.3-1.2) 0.006 RT ratio 0.6 (0.5-0.8) 0.7 (0.4-0.9) 0.78 mTTI ratio 0.8 (0.3-1.2) 0.9 (0.6-2.0) 0.17 TTP ratio 0.6 (0.5-0.8) 0.6 (0.4-0.8) 0.96 WiR ratio 3.0 (1.7-6.9) 1.6 (1.1-3.5) 0.07 WiPI ratio 1.9 (1.1-3.1) 0.8 (0.7-1.5) 0.004 WoAUC ratio 1.3 (0.9-2.3) 0.5 (0.3-1.1) 0.006 WiWoAUC ratio 1.3 (0.8-2.1) 0.6 (0.3-1.1) 0.003 FT ratio 0.8 (0.5-0.9) 0.6 (0.4-1.1) 0.52 WoR ratio 1.8 (1.3-7.5) 1.5 (1.2-2.5) 0.33 MeanLin, average contrast signal intensity; PE, peak enhancement; RT, rising time; FT, fall time; TTP, time to peak; mTTI, mean transit time; WiAUC, wash-in area under the curve; WiR, wash-in rate; WiPI, wash-in perfusion index; WoAUC, wash-out area under the curve; WoR, wash-out rate; WiWoAUC, wash-in area and wash-out area under the curve Additional Declarations No competing interests reported. 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HB, hepatoblastoma; CEUS, contrast-enhanced ultrasound.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5988488/v1/968cb14ce6162f5977d0232f.png"},{"id":76284274,"identity":"984e301c-02f5-4969-9380-dea1189e32dd","added_by":"auto","created_at":"2025-02-14 10:54:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3474948,"visible":true,"origin":"","legend":"\u003cp\u003eA 15 months old boy with HB received C5VD regimen. The patient was evaluated as responder according to RECIST 1.1 criteria after 2 courses of chemotherapy. (a) Target lesion before treatment in B-mode ultrasound. (b) Target lesion and the surrounding liver parenchyma in CEUS mode. (c) Yellow and green curves represent the time-intensity curves of the target lesion and the surrounding liver parenchyma, respectively. It can be observed intuitively that MeanLin, PE, WiAUC, WiR, WoAUC, WiWoAUC of the tumor were significantly bigger than those of the surrounding liver parenchyma. C5VD, cisplatin, 5-flourouracil, vincristine, and doxorubicin; CEUS, contrast-enhanced ultrasound\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5988488/v1/5237450a6f4d39b32d34391f.png"},{"id":76284270,"identity":"7f2be261-9ebf-4832-936f-0e1ba467fa62","added_by":"auto","created_at":"2025-02-14 10:54:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3678885,"visible":true,"origin":"","legend":"\u003cp\u003eA 9 months old girl with HB received C5VD regimen. The patient was evaluated as non-responder according to RECIST 1.1 criteria after 2 courses of chemotherapy. (a) Target lesion before treatment in B-mode ultrasound. (b) Target lesion and the surrounding liver parenchyma in CEUS mode. (c) Yellow and green curves represent the time-intensity curves of the target lesion and the surrounding liver parenchyma, respectively. Compared with the time-intensity curves in Fig 2, it can be observed intuitively that MeanLin, PE, WiAUC, WiR, WoAUC, WiWoAUC of the tumors were more similar to those of the surrounding liver parenchyma. C5VD, cisplatin, 5-flourouracil, vincristine, and doxorubicin; CEUS, contrast-enhanced ultrasound\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5988488/v1/7d0ea6a21bbcece50418f24b.png"},{"id":93547897,"identity":"1b02e566-d06e-4336-8c81-1838aeb98dec","added_by":"auto","created_at":"2025-10-15 04:02:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8896731,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5988488/v1/cd7be3a3-75d9-4b21-85ae-d7580205baae.pdf"},{"id":76284268,"identity":"28252c71-0124-4c91-9aac-6474cc86d516","added_by":"auto","created_at":"2025-02-14 10:54:49","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":12704,"visible":true,"origin":"","legend":"","description":"","filename":"ARTABLES1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5988488/v1/0c909c4cbafb558921a6d099.docx"},{"id":76285115,"identity":"17640706-0e44-4ce3-b3c8-edbbeef36973","added_by":"auto","created_at":"2025-02-14 11:02:49","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":12780,"visible":true,"origin":"","legend":"","description":"","filename":"ARTABLES2.docx","url":"https://assets-eu.researchsquare.com/files/rs-5988488/v1/8294c800f45465ca8867c8d1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Quantitative analysis of tumor perfusion via contrast-enhanced ultrasound to predict the neoadjuvant chemotherapy efficacy for children with hepatoblastoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHepatoblastoma (HB), which accounts for approximately 1% of all childhood cancers, is the most common malignant liver tumor in children under 5 years old [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Neoadjuvant chemotherapy followed by surgical resection is the cornerstone of treatment for hepatoblastoma[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Approximately two-thirds of patients with HB are not eligible for surgical resection at diagnosis and require neoadjuvant chemotherapy to downstage and to achieve complete tumor resection[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, some of them are not sensitive to neoadjuvant chemotherapy[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. For these patients, neoadjuvant chemotherapy not only prolongs the course but also reduces the tolerance of surgery. Interventional therapy, targeted drugs, and immunotherapy are their options[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Therefore, predicting the response of neoadjuvant chemotherapy may help choose appropriate treatment strategy for patients with newly diagnosed HB.\u003c/p\u003e \u003cp\u003eSeveral studies have reported some genetic phenotypes and transcriptomic signature of hepatoblastoma might correlate with chemotherapy resistance response[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Nevertheless, molecular testing is expensive and time-consuming. Currently, some researches have investigated the value of contrast-enhanced computed tomography radiomics in predicting the response of neoadjuvant chemotherapy of HB and attained preliminary results[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. It seems that the vascular perfusion of HB before neoadjuvant chemotherapy contains the useful information that can reflect the response of neoadjuvant chemotherapy. However, radiomics faces several challenges that need to be addressed, including but not limited to the explainability of the models, the reproducibility of the quantitative imaging features, and the sensitivity to variations in image acquisition and reconstruction parameters[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eContrast enhanced-ultrasound (CEUS) is a safe, convenient and easily repeatable imaging technique that can reflect real-time blood perfusion of lesions. Quantitative analysis of CEUS is a technique for obtaining quantitative parameters of tissue or tumor perfusion during CEUS by using specific models and functions, the use of which enables early diagnosis of tumors and early dynamic assessment of the efficacy of anti-tumor therapy[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Quantitative analysis of CEUS has been used to predict the response of chemotherapy in various types of solid tumors[\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, there is a lack of studies related to predicting the efficacy of neoadjuvant chemotherapy for hepatoblastoma in children by quantitative analysis of CEUS.\u003c/p\u003e \u003cp\u003eThis study was aimed to explore the value of quantitative analysis of CEUS images of hepatoblastoma in predicting the efficacy of neoadjuvant chemotherapy for children with hepatoblastoma.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003ePatient selection\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the institutional review board of our central. Prior to underwent CEUS examination, informed parental consent was obtained for each patient. Patients with HB admitted to our hospital from January 2017 to July 2023 were retrospectively collected. The inclusion criteria were as follows: (1) initially diagnosed as hepatoblastoma with histopathological confirmation; (2) had enhanced CT and CEUS examination before neoadjuvant chemotherapy within 1\u0026nbsp;week; and (3) received neoadjuvant chemotherapy based on the CCCG-HB-2016 protocol[17] ;The exclusion criteria were as follows: (1) incomplete clinical data before and after neoadjuvant chemotherapy; and (2) poor CEUS image quality (evaluated by experienced radiologist) or unavailable dynamic\u0026nbsp;CEUS images (Fig.\u0026nbsp;1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical staging and risk-stratified staging\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe clinical stage of the tumor was evaluated using the Pretreatment Extent of Disease (PRETEXT) system by a radiologist (C.Y.X., with 7 years of experience in pediatric radiology) based on CT imaging findings before chemotherapy[18]. The risk-stratified of the tumor was evaluate using the Children\u0026rsquo;s Hepatic tumors International Collaboration\u0026mdash;Hepatoblastoma Stratification system (CHIC-HS)[19].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEfficacy evaluation criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;To monitor the chemotherapy response, the follow-up imaging examination was performed after two cycles of neoadjuvant chemotherapy. In cases where patients received contrast-enhanced computed tomography examination after neoadjuvant chemotherapy, the Response Evaluation Criteria in Solid Tumors 1.1 (RECIST 1.1) was used preferentially to evaluate the effect of chemotherapy[20]. With RECIST 1.1 criteria, a 70% decline detected in the maximum diameters of the target lesions without progression of other lesions and appearance of new lesions was considered responders. In patients who did not undergo contrast-enhanced computed tomography after chemotherapy but had an ultrasound examination, the efficacy was evaluated by combining serum alpha-fetoprotein (AFP) levels and ultrasound examination. A 70% decline in the maximum diameters of the target lesions with a 90% decline in serum AFP levels were considered responders.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContrast‑enhanced ultrasound technique\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn Aplio 500 (Canon, Tokyo, Japan) equipped with a 1.0-6.0 MHz curvilinear transducer, an Aplio 900 (Canon, Tokyo, Japan) equipped with a 1.0-6.0 MHz curvilinear transducer and an Aixplorer (Supersonic, Provence, France) equipped with a 1.0-6.0 MHz curvilinear transducer SC6-1 were used to perform conventional ultrasound and CEUS examinations by a radiologist (Z.L.Y, with 10 years of experience in liver CEUS). Conventional ultrasound examination was performed before CEUS. If the patient presents with multiple lesions, the largest tumor was selected as the target lesion. The ultrasound imaging characteristics of the target lesion were recorded, including morphology, boundary, internal echo, and calcification. Subsequently, the section that displayed the maximum view of the tumor as well as a part of normal liver parenchyma was selected to perform CEUS. Then, the contrast-specific imaging mode was activated with a mechanical index \u0026lt;0.1. Following the recommendation of the Food and Drug Administration, the dose of contrast agent (SonoVue; Bracco, Milan, Italy) was 0.03 ml/ kg, up to a maximum of 2.4 mL. A bolus injection of the appropriate dose of contrast agent was administered through a vascular catheter needle placed in a superficial vein, followed by a 5 mL flush of 0.9% saline. A timer was started immediately after the injection. The target lesions were continuously observed for the first 60s, and then intermittently observed every 60 seconds until 5 minutes later. images and videos were recorded for further analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImage analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQuantification of dynamic sequences was performed by a radiologist (C.M.X., with 3 years of experience in liver CEUS) using perfusion quantification software (VueBox, Bracco). There are two types of regions of interest (ROI) for target lesions: one covering the entire lesion and the other covering the active area with enhancement on CEUS. The ROI of the surrounding liver parenchyma was also delineated and should avoid larger vessels. The movement of lesion caused by respiration were compensated by the automatic respiratory motion compensation function. The following perfusion parameters of the target lesion and surrounding liver parenchyma were obtained: average contrast signal intensity (MeanLin), peak enhancement (PE), rising time (RT), fall time (FT), time to peak (TTP), mean transit time (mTT), wash-in area under the curve (WiAUC), wash-in rate (WiR), wash-in perfusion index (WiPI), wash-out area under the curve (WoAUC), wash-out rate (WoR) and WiAUC+WoAUC (WiWoAUC). WiPI was a ratio of WiAUC to RT. At the same time, the ratio of the lesion to the surrounding liver parenchyma was calculated: MeanLin ratio, PE ratio, RT ratio, FT ratio, TTP ratio, mTT ratio, WiAUC ratio, WiR ratio, WiPI ratio, WoAUC ratio, WoR ratio and WiWoAUC ratio. Quantification of target lesions was performed in duplicate at different time points at least 1 week apart by the same radiologist (C.M.X.) to evaluate intra-observer reproducibility.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe enhancement level (hyper/iso/hypoenhancement), extent of liquefaction necrosis, the time to achieve full tumor enhancement, the time to washout to equal enhancement, and the time to washout to low enhancement were also reviewed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analyses\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStatistical analysis was performed using SPSS Version 26.0 (IBM, Armonk, NY, USA). Continuous variables were first analyzed for normality of distribution using the Kolmogorov-Smirnov test of normality. Continuous variables that conformed to the normal distribution are expressed as the mean \u0026plusmn; standard deviation and those that did not are expressed as the median (interquartile range). Categorical variables are expressed as medians and ranges. Categorical variables were compared using Fisher\u0026rsquo;s exact test. Continuous variables were compared using the Mann-Whitney test. The Mann\u0026minus;Whitney non-parametric U-test was used to compare differences between responders and non-responders. Kappa test was used to evaluate the consistency of two efficacy evaluation indicators. The Intra-class correlation coefficient (ICC) was used to assess the reproducibility of intra-observer perfusion parameters. All differences with \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 were considered significant.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePatient and lesion characteristics\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA total of 35 cases were included in the study. After two cycles of neoadjuvant chemotherapy, 27 patients were evaluated according to RECIST1.1 criteria, and 8 were evaluated according to the criteria that combined serum AFP levels with abdominal ultrasound. According to these criteria, 16 patients were considered to be responders and 19 patients were considered to be non-responders. Demographic and clinical data of the study population are outlined in Table 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The median age of patients was 15 months. Twenty-eight (80.0%) were male. Serum AFP level at diagnosis were higher than normal in all patients and the median AFP at diagnosis was 293,655ng/mL. The average maximum diameter of all target lesions was 11.5\u0026plusmn;2.7 cm, 3 (5.7%) patients had multifocal lesions, and 3 (5.7%) manifested distant metastases. There was no significant difference in clinical characteristics between the responder and non-responder groups. Conventional and contrast-enhanced ultrasound characteristics of HB in the study population are outlined in Table 2. The proportion of lesions with liquefaction necrosis was significantly higher in the responder group than in the non-responder group (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsistency of the two evaluation criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOf the 35 patients, 21 patients had the results of serum AFP level, chest and abdominal enhanced CT examination, and liver ultrasound after 2 courses of chemotherapy. The Kappa value of the two efficacy evaluation criteria for the 21 patients was 0.904 (Table 3). Only one patient showed a difference in the evaluation results. The AFP baseline level of this patient was 309.9 ng/mL. And he was assessed as responder by RECIST 1.1, but non-responder by the other criterion due to a decrease in serum AFP was less than 90% after two cycles.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReproducibility of perfusion parameters\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIntra-observer reproducibility of perfusion parameters is outlined in Table S1. For amplitude parameters, the ICC range for correlations between Intra-observer quantification was 0.803\u0026minus;0.999. For time parameters, the ICC range for correlations between Intra-observer quantification was 0.620\u0026minus;0.936.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation of CEUS quantitative parameters with tumor response\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 4 and Table S2 show the results of perfusion parameters between the responder group and the non-responder group in the case of the two tumor ROIs, respectively. The results were similar for both tumor ROIs. The amplitude parameters ratio including MeanLin ratio, PE ratio, WiAUC ratio, WiPI ratio, WoAUC ratio, and WiWoAUC ratio of the responder group were significantly higher than those of the non-responder group (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). And when the tumor ROI only included tumor active region, the WiR ratio of the responder group was significantly higher than that of the non-responder group (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). Differences between the patients in the responder and non-responder groups are illustrated in Figures 2 and 3.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study collected 35 patients with hepatoblastoma requiring neoadjuvant chemotherapy and analyzed the relationship between pre-chemotherapy CEUS perfusion parameters and chemotherapy efficacy after 2 courses. Our finding shows that amplitude parameters ratio in the responder group, including MeanLin ratio, PE ratio, WiAUC ratio, WiPI ratio, WoAUC ratio, and WiWoAUC ratio, were significantly higher than those in the non-responder group. Moreover, in case of ROI of tumor was the active area, the WiR ratio of the responder group was significantly higher than that of the non-responder group. All these findings indicates that quantitative analysis of CEUS might have the potential to predict the efficacy of neoadjuvant chemotherapy for hepatoblastoma and facilitate the precise treatment of hepatoblastoma.\u003c/p\u003e \u003cp\u003eOn CEUS, perfusion parameters can be obtained by continuously measuring the change in contrast agents concentration in ROI over time using quantitative analysis software. In the present study, the responder group exhibited greater amplitude parameters. Studies have found that the greater the amplitude parameter such as PE, the higher the angiogenesis and the richer the blood supply[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The proportion of lesions with liquefaction necrosis was also significantly higher in the responder group. The rapid growth of tumor may result in the local liquefaction necrosis due to relative nutrient deficiency. Consequently, the presence of liquefaction necrosis in untreated tumors may indicate that the tumors are more active and have richer blood supply. Based on these findings, it can be postulated that hepatoblastoma with a high angiogenesis and rich blood supply would respond better to neoadjuvant chemotherapy.\u003c/p\u003e \u003cp\u003ePrevious studies employed other imaging modalities to assess the efficacy of targeted therapy in hepatocellular carcinoma had similar findings. Chen et al. analyzed MRI blood flow parameters of patients received systemic therapy and found that patients with high peak intensity before treatment had longer overall survival than those with low peak intensity[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Ippolito et al. analyzed CT perfusion parameters of patients treated with sorafenib and found that compared with progressor group, the non-progressor group had significantly higher hepatic perfusion, TTP, and arterial perfusion at baseline[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Investigators explained that a higher vascularization of lesions may be responsible for their higher response rate to antiangiogenic treatment. Although patients in present study did not receive antiangiogenic treatment, the cytotoxicity of chemotherapeutic agents play an important role in destructing tumor vessels. Saxena et al. found evident vascular changes in HB after chemotherapy, including mild intimal proliferation, complete occlusion of the vessels by myxomatous tissue, and thickening of vessel walls by dense hyalinized tissue[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn contrast to studies focusing on analyzing the correlation between the changes in perfusion parameters during treatment and long-term efficacy[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], the present study has attempted to predict post-treatment outcomes based on pre-treatment perfusion parameters, which may help to identify poor responders prior to routine treatment, providing an opportunity for early individualized management.\u003c/p\u003e \u003cp\u003eIt should be noted that in order to expand the sample size, two evaluation criteria were employed in this study: RECIST1.1 and serum AFP combined with abdominal ultrasound. RECIST1.1 with CT or MRI as imaging tool represents the gold standard for evaluating the efficacy of solid tumors and is popularly used in clinical practice. However, ultrasound was preferred to be chosen as the monitoring tool by some of parents due to its non-radiation nature. However, ultrasound is limited by operator proficiency and the measurement accuracy for large tumor is also affected by the display range. Accordingly, we built up another criteria that combined a decrease in serum AFP levels exceeding 90%[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and the results of ultrasound for assessing the efficacy of children without CT findings. In this study, this criteria exhibits a high agreement with the RECIST 1.1, providing evidence of the reliability and feasibility of this assessment method.\u003c/p\u003e \u003cp\u003eOur study has several limitations. First, this study is a single-center study with a small sample size, and the above findings are only preliminary results. Second, we did not conduct further multivariate analysis and subgroup analyses of the different chemotherapy regimens due to the small sample size. It would be beneficial to consider expanding the sample size and conducting a multicenter study in the future.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eQuantitative analysis of CEUS and ultrasound characteristic have the potential to predict the efficacy of neoadjuvant chemotherapy for hepatoblastoma. Further investigation of these findings in a larger population is warranted.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eC.M.X wrote the main manuscript text, organized and analyze data. W.Y.Q, C.Y.X and J.H organized and collected data. Z.W.Y and W.X.L prepared figures and tables. X.X.Y, H.G.L and Z.L.Y helped supervise the project and conceived the original idea. All authors reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChung EM, Lattin GE, Cube R, et al (2011) From the archives of the AFIP: Pediatric liver masses: radiologic-pathologic correlation. Part 2. Malignant tumors. Radiographics 31:483\u0026ndash;507. https://doi.org/10.1148/rg.312105201\u003c/li\u003e\n\u003cli\u003eWu PV, Rangaswami A (2022) Current Approaches in Hepatoblastoma-New Biological Insights to Inform Therapy. Curr Oncol Rep 24:1209\u0026ndash;1218. https://doi.org/10.1007/s11912-022-01230-2\u003c/li\u003e\n\u003cli\u003eMeyers RL, Tiao G, de Ville de Goyet J, et al (2014) Hepatoblastoma state of the art: pre-treatment extent of disease, surgical resection guidelines and the role of liver transplantation. Curr Opin Pediatr 26:29\u0026ndash;36. https://doi.org/10.1097/MOP.0000000000000042\u003c/li\u003e\n\u003cli\u003eMarin JJG, Cives-Losada C, Asensio M, et al (2019) Mechanisms of Anticancer Drug Resistance in Hepatoblastoma. Cancers (Basel) 11:407. https://doi.org/10.3390/cancers11030407\u003c/li\u003e\n\u003cli\u003eCao Y, Wu S, Tang H (2024) An update on diagnosis and treatment of hepatoblastoma. Biosci Trends 17:445\u0026ndash;457. https://doi.org/10.5582/bst.2023.01311\u003c/li\u003e\n\u003cli\u003eClaver\u0026iacute;a-Cabello A, Herranz JM, Latasa MU, et al (2023) Identification and experimental validation of druggable epigenetic targets in hepatoblastoma. J Hepatol 79:989\u0026ndash;1005. https://doi.org/10.1016/j.jhep.2023.05.031\u003c/li\u003e\n\u003cli\u003eSong H, Bucher S, Rosenberg K, et al (2022) Single-cell analysis of hepatoblastoma identifies tumor signatures that predict chemotherapy susceptibility using patient-specific tumor spheroids. Nat Commun 13:4878. https://doi.org/10.1038/s41467-022-32473-z\u003c/li\u003e\n\u003cli\u003eHirsch TZ, Pilet J, Morcrette G, et al (2021) Integrated Genomic Analysis Identifies Driver Genes and Cisplatin-Resistant Progenitor Phenotype in Pediatric Liver Cancer. Cancer Discov 11:2524\u0026ndash;2543. https://doi.org/10.1158/2159-8290.CD-20-1809\u003c/li\u003e\n\u003cli\u003eChen Y, Froelich MF, Tharmaseelan H, et al (2024) Computed tomography imaging phenotypes of hepatoblastoma identified from radiomics signatures are associated with the efficacy of neoadjuvant chemotherapy. Pediatr Radiol 54:58\u0026ndash;67. https://doi.org/10.1007/s00247-023-05793-5\u003c/li\u003e\n\u003cli\u003eYang Y, Wang H, Si J, et al (2024) Predicting response of hepatoblastoma primary lesions to neoadjuvant chemotherapy through contrast-enhanced computed tomography radiomics. J Cancer Res Clin Oncol 150:223. https://doi.org/10.1007/s00432-024-05746-x\u003c/li\u003e\n\u003cli\u003eIbrahim A, Primakov S, Beuque M, et al (2021) Radiomics for precision medicine: Current challenges, future prospects, and the proposal of a new framework. Methods 188:20\u0026ndash;29. https://doi.org/10.1016/j.ymeth.2020.05.022\u003c/li\u003e\n\u003cli\u003eLassau N, Chami L, Benatsou B, et al (2007) Dynamic contrast-enhanced ultrasonography (DCE-US) with quantification of tumor perfusion: a new diagnostic tool to evaluate the early effects of antiangiogenic treatment. Eur Radiol 17 Suppl 6:F89-98. https://doi.org/10.1007/s10406-007-0233-6\u003c/li\u003e\n\u003cli\u003eMcCarville MB, Coleman JL, Guo J, et al (2016) Use of Quantitative Dynamic Contrast-Enhanced Ultrasound to Assess Response to Antiangiogenic Therapy in Children and Adolescents With Solid Malignancies: A Pilot Study. AJR Am J Roentgenol 206:933\u0026ndash;939. https://doi.org/10.2214/AJR.15.15789\u003c/li\u003e\n\u003cli\u003eAmioka A, Masumoto N, Gouda N, et al (2016) Ability of contrast-enhanced ultrasonography to determine clinical responses of breast cancer to neoadjuvant chemotherapy. Jpn J Clin Oncol 46:303\u0026ndash;309. https://doi.org/10.1093/jjco/hyv215\u003c/li\u003e\n\u003cli\u003eLassau N, Koscielny S, Albiges L, et al (2010) Metastatic renal cell carcinoma treated with sunitinib: early evaluation of treatment response using dynamic contrast-enhanced ultrasonography. Clin Cancer Res 16:1216\u0026ndash;1225. https://doi.org/10.1158/1078-0432.CCR-09-2175\u003c/li\u003e\n\u003cli\u003eLassau N, Koscielny S, Chami L, et al (2011) Advanced hepatocellular carcinoma: early evaluation of response to bevacizumab therapy at dynamic contrast-enhanced US with quantification--preliminary results. Radiology 258:291\u0026ndash;300. https://doi.org/10.1148/radiol.10091870\u003c/li\u003e\n\u003cli\u003eCommittee CA-CAP, Group CMAPO (2017) Expert Consensus for Multidisciplinary Management of Hepatoblastoma(CCCG-HB-2016). Chinese Journal of Pediatric Surgery 38:733\u0026ndash;739. https://doi.org/10.3760/cma.j.issn.0253-3006.2017.10.003\u003c/li\u003e\n\u003cli\u003eTowbin AJ, Meyers RL, Woodley H, et al (2018) 2017 PRETEXT: radiologic staging system for primary hepatic malignancies of childhood revised for the Paediatric Hepatic International Tumour Trial (PHITT). Pediatr Radiol 48:536\u0026ndash;554. https://doi.org/10.1007/s00247-018-4078-z\u003c/li\u003e\n\u003cli\u003eMeyers RL, Maibach R, Hiyama E, et al (2017) Risk-stratified staging in paediatric hepatoblastoma: a unified analysis from the Children\u0026rsquo;s Hepatic tumors International Collaboration. Lancet Oncol 18:122\u0026ndash;131. https://doi.org/10.1016/S1470-2045(16)30598-8\u003c/li\u003e\n\u003cli\u003eEisenhauer EA, Therasse P, Bogaerts J, et al (2009) New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 45:228\u0026ndash;247. https://doi.org/10.1016/j.ejca.2008.10.026\u003c/li\u003e\n\u003cli\u003eWang Z, Tang J, An L, et al (2007) Contrast-enhanced ultrasonography for assessment of tumor vascularity in hepatocellular carcinoma. J Ultrasound Med 26:757\u0026ndash;762. https://doi.org/10.7863/jum.2007.26.6.757\u003c/li\u003e\n\u003cli\u003eChen B-B, Hsu C-Y, Yu C-W, et al (2017) Dynamic Contrast-enhanced MR Imaging of Advanced Hepatocellular Carcinoma: Comparison with the Liver Parenchyma and Correlation with the Survival of Patients Receiving Systemic Therapy. Radiology 283:923. https://doi.org/10.1148/radiol.2017174012\u003c/li\u003e\n\u003cli\u003eIppolito D, Querques G, Pecorelli A, et al (2019) Diagnostic Value of Quantitative Perfusion Computed Tomography Technique in the Assessment of Tumor Response to Sorafenib in Patients With Advanced Hepatocellular Carcinoma. J Comput Assist Tomogr 43:206\u0026ndash;213. https://doi.org/10.1097/RCT.0000000000000807\u003c/li\u003e\n\u003cli\u003eSaxena R, Leake JL, Shafford EA, et al (1993) Chemotherapy effects on hepatoblastoma. A histological study. Am J Surg Pathol 17:1266\u0026ndash;1271. https://doi.org/10.1097/00000478-199312000-00008\u003c/li\u003e\n\u003cli\u003eE H, T H, K W, et al (2016) Resectability and tumor response after preoperative chemotherapy in hepatoblastoma treated by the Japanese Study Group for Pediatric Liver Tumor (JPLT)-2 protocol. Journal of pediatric surgery 51:. https://doi.org/10.1016/j.jpedsurg.2016.09.038\u003c/li\u003e\n\u003cli\u003eZs\u0026iacute;ros J, Maibach R, Shafford E, et al (2010) Successful treatment of childhood high-risk hepatoblastoma with dose-intensive multiagent chemotherapy and surgery: final results of the SIOPEL-3HR study. J Clin Oncol 28:2584\u0026ndash;2590. https://doi.org/10.1200/JCO.2009.22.4857\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 Demographic and clinical data of the patients\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eAll patients (n = 35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eResponder group (n=16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eNon-responder group (n=19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eSex (male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e28\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eAge (month)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e15 (8-32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e16 (11-34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e12 (5-32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eSerum AFP level (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e293,654.5 (40,668.5 - 915,793.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e388,010.5 (62,696.0 - 2,000,000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e289,156.5 (23,960.2-496,142.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003ePLT（U/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e576.2\u0026plusmn;251.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e587.3\u0026plusmn;55.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e566.8\u0026plusmn;64.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eNRL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.6 (0.3-1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.7 (0.3-1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.5 (0.3-1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003ePRL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e122.0 (71.0-169.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e129.6 (97.3-166.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e97.9 (63.6-176.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eTumor number (\u0026gt;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eMaximum tumor size (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e11.5\u0026plusmn;2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e12.3\u0026plusmn;2.4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e10.8\u0026plusmn;2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eTumor volume (cm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e417.5 (260.6-596.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e469.4 (294.8-672.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e334.0 (238.3-549.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003ePRETEXT stage\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp; Stage I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp; Stage II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp; Stage III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp; Stage IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eDistant metastases\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eVPEFR+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eCHIC-HS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp; Low risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp; Median risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp; High risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003ePathological\u0026nbsp;type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp; Pure fetal with low mitotic activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp; Mixed epithelial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp; Mixed epithelial and mesenchymal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAFP, alpha-fetoprotein; PLT, platelet; NRL, neutrophil lymphocyte ratio; PRL, platelet lymphocyte ratio; PRETEXT, Pretreatment Extent of Disease; VPEFR+: macrovascular involvement of all hepatic veins (V) or portal bifurcation (P), contiguous extrahepatic tumor (E), multifocal tumor (F), and spontaneous rupture (R); M+: distant metastasis; CHIC, Children\u0026rsquo;s Hepatic tumors International Collaboration\u0026mdash;Hepatoblastoma Stratification system\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2 Conventional and contrast-enhanced ultrasound characteristics of recruited patients\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAll patients (n = 35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eResponder group (n=16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNon-responder group (n=19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eShape\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRound\u0026nbsp;or\u0026nbsp;oval\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLobular or\u0026nbsp;irregular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEchogenicity\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHomogeneous\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInhomogenous\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBoundary\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWell defined\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePoorly defined\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCalcification\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLiquefactive necrosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEnhanced level at arterial phase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHomogeneous hyperenhancement\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eInhomogenous hyperenhancement\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ewhole tumor enhancement time (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11 (9-13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11 (10-13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11 (9-13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eWashout to isoenhancement time (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20 (17-24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e223 (17-26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18 (16-23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eWashout to hypoenhancement time(s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e33 (27-55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e34 (26-48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e33 (27-58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 3 Consistency of the two evaluation criteria\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eCombined serum AFP levels with abdominal ultrasound\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003eRECIST1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 24px;\"\u003e\n \u003cp\u003eTotal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eResponder group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eNon-responder group\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eResponder group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eNon-responder group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eTotal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 4 Contrast-enhanced ultrasound quantitative parameters of the two groups (tumor ROI: entire tumor)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eParameter\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eResponder group (n=16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNon-responder group (n=19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMeanLin ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.7 (1.1-2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.6 (0.5-1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePE ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.8 (1.1-3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.9 (0.7-1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eWiAUC ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.3 (0.7-1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.6 (0.3-1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eRT ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.6 (0.5-0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.7 (0.4-0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003emTTI ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.8 (0.3-1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.9 (0.6-2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eTTP ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.6 (0.5-0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.6 (0.4-0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eWiR ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.0 (1.7-6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.6 (1.1-3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eWiPI ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.9 (1.1-3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.8 (0.7-1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eWoAUC ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.3 (0.9-2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.5 (0.3-1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eWiWoAUC ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.3 (0.8-2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.6 (0.3-1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eFT ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.8 (0.5-0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.6 (0.4-1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eWoR ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.8 (1.3-7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.5 (1.2-2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eMeanLin, average contrast signal intensity; PE, peak enhancement; RT, rising time; FT, fall time; TTP, time to peak; mTTI, mean transit time; WiAUC, wash-in area under the curve; WiR, wash-in rate; WiPI, wash-in perfusion index; WoAUC, wash-out area under the curve; WoR, wash-out rate; WiWoAUC, wash-in area and wash-out area under the curve\u003c/p\u003e\n"}],"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":"Hepatoblastoma, Pediatrics, Contrast-enhanced ultrasound, Quantitative Analysis","lastPublishedDoi":"10.21203/rs.3.rs-5988488/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5988488/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePredicting the neoadjuvant chemotherapy efficacy may help choose appropriate treatment strategy of hepatoblastoma. Quantitative analysis of contrast-enhanced ultrasound (CEUS) has been used to predict the chemotherapy efficacy in various types of tumors, but its value in hepatoblastoma has not been fully evaluated.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo explore the value of quantitative analysis of CEUS in predicting the neoadjuvant chemotherapy efficacy for hepatoblastoma.\u003c/p\u003e\u003ch2\u003eMaterials and methods\u003c/h2\u003e \u003cp\u003eThirty-five hepatoblastoma patients who require neoadjuvant chemotherapy and underwent liver CEUS in our hospital from January 2017 to July 2023 were enrolled. A CEUS examination was performed at baseline, and perfusion parameters were obtained via perfusion quantification software. Patients were classified into responder group and non-responder group after 2 courses. The differences in quantitative parameters between two groups were compared.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe MeanLin ratio (1.7 vs. 0.6 and 1.7 vs. 0.8), PE ratio (1.8 vs. 0.9 and 1.9 vs. 0.9), WiAUC ratio (1.3 vs. 0.6 and 1.3 vs. 0.6), WiPI ratio (1.9 vs. 0.8 and 2.0 vs. 0.9), WoAUC ratio (1.3 vs. 0.5 and 1.3 vs. 0.7), and WiWoAUC ratio (1.3 vs. 0.6 and 1.3 vs. 0.7) in the response group were significantly higher than those in the non-response group (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); when tumor\u0026rsquo;s region of interest was the active area, the WiR ratio (3.1 vs. 1.4) of the responder group was significantly higher (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04). The proportion of lesions with liquefaction necrosis before chemotherapy was significantly higher in the response group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eQuantitative analysis of CEUS may have the potential to predict the neoadjuvant chemotherapy efficacy for hepatoblastoma.\u003c/p\u003e","manuscriptTitle":"Quantitative analysis of tumor perfusion via contrast-enhanced ultrasound to predict the neoadjuvant chemotherapy efficacy for children with hepatoblastoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-14 10:54:44","doi":"10.21203/rs.3.rs-5988488/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":"baf8e855-2612-4388-b3d1-12829f2acbe1","owner":[],"postedDate":"February 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-15T03:54:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-14 10:54:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5988488","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5988488","identity":"rs-5988488","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}