An interpretable ensemble model combining handcrafted radiomics and deep learning for predicting the overall survival of hepatocellular carcinoma patients after stereotactic body radiation therapy

Abstract

Purpose: Hepatocellular carcinoma (HCC) remains a global health concern, marked by increasing incidence rates and poor outcomes. This study seeks to develop a robust predictive model by integrating radiomics and deep learning features with clinical data to predict 2-year survival in HCC patients treated with stereotactic body radiation therapy (SBRT).

Methods: This study analyzed a cohort of 186 HCC patients who underwent SBRT. Radiomics features were extracted from CT scans, complemented by collection of clinical data. Training and validation of machine learning models were conducted using nested cross-validation techniques. Deep learning models, leveraging various convolutional neural networks (CNNs), were employed to effectively integrate both image and clinical data. Post-hoc explainability techniques were applied to elucidate the contribution of imaging data to predictive outcomes.

Results: Handcrafted radiomics features demonstrated moderate predictive performance, with area under the receiver operating characteristic curve (AUC) values ranging from 0.59 to 0.72. Deep learning models, harnessing the fusion of image and clinical data, exhibited improved predictive accuracy, with AUC values ranging from 0.71 to 0.81. Notably, the ensemble model, amalgamating handcrafted radiomics and deep learning features with clinical data, demonstrated the most robust predictive capability, achieving an AUC of 0.86 (95% CI: 0.80-0.93).

Conclusion: The ensemble model represents a significant advancement, providing a comprehensive tool for predicting survival outcomes in HCC patients undergoing SBRT. The inclusion of interpretability methods such as Grad-CAM enhances transparency and understanding of these complex predictive models.

Keywords: Deep learning; Handcrafted features; Hepatocellular carcinoma; Radiomics; Stereotactic body radiation therapy.

Fig. 1

Representation of the workflow of constructing models

Fig. 2

Boxplot of the ROC AUC for handcrafted radiomics and clinical features

Fig. 3

ROC AUC curves for deep learning (DL) based on radiomics, images (Img) +/- clinical features, and ensemble models

Fig. 4

Gradient-Class Activation Maps in the whole liver of a patient using deep learning model (red and green: negative and positive contribution respectively). White contour: gross tumour volume