{"title":"多期mri衍生的Delta放射组学与临床特征相结合,用于预测肝细胞癌热消融后的生存","authors":"Junpeng Luo , Hao Xin , Yandan Wang , Xiang He","doi":"10.1016/j.ejrad.2025.112368","DOIUrl":null,"url":null,"abstract":"<div><h3>Rationale and objectives</h3><div>To evaluate the predictive value of a delta radiomic score (DRS)derived from multiphase contrast-enhanced Magnetic Resonance Imaging (MRI), in combination with clinical characteristics, forecasting overall survival (OS) and recurrence-free survival (RFS) in patients with hepatocellular carcinoma (HCC) after thermal ablation.</div></div><div><h3>Materials and Methods</h3><div>This retrospective study enrolled 415 patients with HCC from two centers who underwent multiphase contrast-enhanced magnetic resonance imaging and percutaneous thermal ablation. Patients in Institution 1 (n = 315) were randomly assigned to the training cohorts (n = 220) and internal tests (n = 95), while 100 patients in Institution 2 formed the external validation cohort. Delta radiomic features were extracted by comparing contrast-enhanced phases with noncontract images, followed by variance filtering, correlation analysis, random forest ranking, and Cox regression to derive the DRS. The DRS was combined with clinical characteristics, including tumor burden score (TBS), Barcelona Clinic Liver Cancer (BCLC), and China Liver Cancer Staging (CNLC), to build Random Survival Forest (RSF) models. The performance of the model was evaluated using the concordance index (C-index), the area under the curve (AUC), the calibration curves and the analysis of decision curves (DCA).</div></div><div><h3>Results</h3><div>The RSF models that integrate DRS with clinical characteristics demonstrated favorable predictive performance for both OS and RFS. For OS predictions at 36 and 60 months, AUCs ranged from 0.71 to 0.83 in cohorts, with corresponding C indices between 0.69 and 0.83. For the prediction of short-term RFS at 6, 12, and 24 months, the AUC ranged from 0.71 to 0.91, with C-indices between 0.68 and 0.71. Calibration and DCA analysis confirmed the robustness and clinical utility of the models in internal and external validations.</div></div><div><h3>Conclusions</h3><div>Delta radiomics derived from multiphase MRI effectively captured tumor dynamics and heterogeneity. When integrated with clinical characteristics, the resulting models allowed an accurate prediction of both recurrence and long-term survival, providing a practical tool for risk stratification and individualized post-ablation management in HCC.</div></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"192 ","pages":"Article 112368"},"PeriodicalIF":3.3000,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multiphase MRI-Derived Delta radiomics integrated with clinical features for survival prediction in hepatocellular carcinoma after thermal ablation\",\"authors\":\"Junpeng Luo , Hao Xin , Yandan Wang , Xiang He\",\"doi\":\"10.1016/j.ejrad.2025.112368\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Rationale and objectives</h3><div>To evaluate the predictive value of a delta radiomic score (DRS)derived from multiphase contrast-enhanced Magnetic Resonance Imaging (MRI), in combination with clinical characteristics, forecasting overall survival (OS) and recurrence-free survival (RFS) in patients with hepatocellular carcinoma (HCC) after thermal ablation.</div></div><div><h3>Materials and Methods</h3><div>This retrospective study enrolled 415 patients with HCC from two centers who underwent multiphase contrast-enhanced magnetic resonance imaging and percutaneous thermal ablation. Patients in Institution 1 (n = 315) were randomly assigned to the training cohorts (n = 220) and internal tests (n = 95), while 100 patients in Institution 2 formed the external validation cohort. Delta radiomic features were extracted by comparing contrast-enhanced phases with noncontract images, followed by variance filtering, correlation analysis, random forest ranking, and Cox regression to derive the DRS. The DRS was combined with clinical characteristics, including tumor burden score (TBS), Barcelona Clinic Liver Cancer (BCLC), and China Liver Cancer Staging (CNLC), to build Random Survival Forest (RSF) models. The performance of the model was evaluated using the concordance index (C-index), the area under the curve (AUC), the calibration curves and the analysis of decision curves (DCA).</div></div><div><h3>Results</h3><div>The RSF models that integrate DRS with clinical characteristics demonstrated favorable predictive performance for both OS and RFS. For OS predictions at 36 and 60 months, AUCs ranged from 0.71 to 0.83 in cohorts, with corresponding C indices between 0.69 and 0.83. For the prediction of short-term RFS at 6, 12, and 24 months, the AUC ranged from 0.71 to 0.91, with C-indices between 0.68 and 0.71. Calibration and DCA analysis confirmed the robustness and clinical utility of the models in internal and external validations.</div></div><div><h3>Conclusions</h3><div>Delta radiomics derived from multiphase MRI effectively captured tumor dynamics and heterogeneity. When integrated with clinical characteristics, the resulting models allowed an accurate prediction of both recurrence and long-term survival, providing a practical tool for risk stratification and individualized post-ablation management in HCC.</div></div>\",\"PeriodicalId\":12063,\"journal\":{\"name\":\"European Journal of Radiology\",\"volume\":\"192 \",\"pages\":\"Article 112368\"},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2025-08-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"European Journal of Radiology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0720048X25004541\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Journal of Radiology","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0720048X25004541","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
Multiphase MRI-Derived Delta radiomics integrated with clinical features for survival prediction in hepatocellular carcinoma after thermal ablation
Rationale and objectives
To evaluate the predictive value of a delta radiomic score (DRS)derived from multiphase contrast-enhanced Magnetic Resonance Imaging (MRI), in combination with clinical characteristics, forecasting overall survival (OS) and recurrence-free survival (RFS) in patients with hepatocellular carcinoma (HCC) after thermal ablation.
Materials and Methods
This retrospective study enrolled 415 patients with HCC from two centers who underwent multiphase contrast-enhanced magnetic resonance imaging and percutaneous thermal ablation. Patients in Institution 1 (n = 315) were randomly assigned to the training cohorts (n = 220) and internal tests (n = 95), while 100 patients in Institution 2 formed the external validation cohort. Delta radiomic features were extracted by comparing contrast-enhanced phases with noncontract images, followed by variance filtering, correlation analysis, random forest ranking, and Cox regression to derive the DRS. The DRS was combined with clinical characteristics, including tumor burden score (TBS), Barcelona Clinic Liver Cancer (BCLC), and China Liver Cancer Staging (CNLC), to build Random Survival Forest (RSF) models. The performance of the model was evaluated using the concordance index (C-index), the area under the curve (AUC), the calibration curves and the analysis of decision curves (DCA).
Results
The RSF models that integrate DRS with clinical characteristics demonstrated favorable predictive performance for both OS and RFS. For OS predictions at 36 and 60 months, AUCs ranged from 0.71 to 0.83 in cohorts, with corresponding C indices between 0.69 and 0.83. For the prediction of short-term RFS at 6, 12, and 24 months, the AUC ranged from 0.71 to 0.91, with C-indices between 0.68 and 0.71. Calibration and DCA analysis confirmed the robustness and clinical utility of the models in internal and external validations.
Conclusions
Delta radiomics derived from multiphase MRI effectively captured tumor dynamics and heterogeneity. When integrated with clinical characteristics, the resulting models allowed an accurate prediction of both recurrence and long-term survival, providing a practical tool for risk stratification and individualized post-ablation management in HCC.
期刊介绍:
European Journal of Radiology is an international journal which aims to communicate to its readers, state-of-the-art information on imaging developments in the form of high quality original research articles and timely reviews on current developments in the field.
Its audience includes clinicians at all levels of training including radiology trainees, newly qualified imaging specialists and the experienced radiologist. Its aim is to inform efficient, appropriate and evidence-based imaging practice to the benefit of patients worldwide.