Survival Prediction and Treatment Decisions in Hepatocellular Carcinoma: A Deep Learning-Based Radiomics Approach.

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Abstract

Deep learning radiomics (DLRadiomics) can capture a wide range of tumor and lesion characteristics, providing valuable insights into biological behavior, pathophysiological status, and patient prognosis. This study integrated clinical data with deep learning-derived features into a machine learning survival model to assess the effectiveness of hepatectomy and transarterial chemoembolization (TACE) treatments in hepatocellular carcinoma (HCC) patients. This study included pathologically confirmed HCC patients who received either hepatectomy or TACE between January 2013 and December 2022. We utilized three deep learning-based algorithms (ResNet50, ResNet18, and DenseNet121) with contrast-enhanced computed tomography (CT) images to predict the overall survival time. Deep learning features were extracted from these predictive models. Furthermore, a combined survival model was developed by incorporating clinical factors with the deep learning features for two treatment regimens separately. The areas under the curves (AUC) of the receiver operating characteristic (ROC) curves were used to assess the discrimination of the model at different time points. Additionally, nomograms were constructed to predict patient prognosis undergoing different treatment regimens, and their survival risk was evaluated using the Kaplan-Meier analysis. This study recruited 409 HCC patients who received either hepatectomy (n = 278, 57 [49-66]; 239 men) or TACE (n = 131, 62 [51-69.5]; 111 men). ResNet50 achieved the highest AUC of 0.866 (95% confidence interval (CI): 0.815-0.917) in the training set and 0.793 (95% CI: 0.675-0.912) in the testing cohort. Overall, six models were constructed to assess overall survival for hepatectomy and TACE treatments, with the combined models exhibiting superior discriminative performance. The C-index for the combined hepatectomy model was 0.836 (95% CI: 0.776-0.897) in the training cohort and 0.861 (95% CI: 0.755-0.967) in the testing cohort. The C-index for the combined TACE model was 0.840 (95% CI: 0.792-0.888) in the training cohort and 0.834 (95% CI: 0.759-0.910) in the testing cohort. Two nomograms were created to help clinicians in selecting a treatment method by examining the difference scores between treatments. The machine learning models can potentially predict differences in outcomes between hepatectomy and TACE. Furthermore, prognostic models using deep learning-based features can effectively predict survival risk in HCC patients.

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