IntroductionWe introduce a standardized necrosis rate-percent reduction in enhancing tumor diameter normalized by baseline tumor diameter-with a threshold of ≥30%. This endpoint is derived from the mRECIST partial response criteria but is normalized to mitigate tumor size-dependent bias. A clinical-radiomics model was developed to assess necrosis in hepatocellular carcinoma (HCC) patients treated with transarterial chemoembolization (TACE).MethodsRetrospectively, 95 HCC patients undergoing TACE were included. Radiomics features were selected via LASSO regression, and clinical variables via logistic regression. Separate radiomics and clinical models were developed, and a combined model was constructed using multivariable logistic regression. The cohort was randomly split into training (70%) and validation (30%) sets, with all preprocessing, feature selection, and model training confined to the training set to prevent data leakage. Model performance was evaluated using discrimination (AUC), calibration, clinical utility (decision curve analysis), and a nomogram.ResultsFrom 1,316 extracted radiomics features, six were retained for Rad-score calculation. Key clinical predictors included hepatitis group, standardized viable tumor ratio, and vascular invasion. The integrated model achieved AUCs of 0.865 (95% CI: 0.768-0.961) in training and 0.853 (95% CI: 0.716-0.990) in validation (n=29), outperforming the clinical model (AUCs: 0.808 (95% CI: 0.695-0.922) and 0.666 (95% CI: 0.465-0.866), respectively). Decision curve analysis and calibration plots confirmed the combined model's superior performance.ConclusionThe radiomics-clinical nomogram, based on a standardized necrosis rate, may enable early prediction of TACE response, offering potential insights for therapeutic decision-making, risk stratification, and liver transplantation management. External validation is warranted before clinical application.
Keywords: hepatocellular carcinoma (HCC), necrosis, predictive model, radiomics, transarterial chemoembolization (TACE)
Technology in cancer research & treatment
Journal Article
English
41999190
Guideline Central and select third party use “cookies” on this website to enhance the user experience.
This technology helps us gather statistical and analytical information to optimize the relevant content for you.
The user also has the option to opt-out which may have an effect on the browsing experience.