Predicting the Initial Treatment Response to Transarterial Chemoembolization in Intermediate-Stage Hepatocellular Carcinoma by the Integration of Radiomics and Deep Learning

Abstract

ObjectivesWe aimed to develop radiology-based models for the preoperative prediction of the initial treatment response to transarterial chemoembolization (TACE) in patients with hepatocellular carcinoma (HCC) since the integration of radiomics and deep learning (DL) has not been reported for TACE.MethodsThree hundred and ten intermediate-stage HCC patients who underwent TACE were recruited from three independent medical centers. Based on computed tomography (CT) images, recursive feature elimination (RFE) was used to select the most useful radiomics features. Five radiomics conventional machine learning (cML) models and a DL model were used for training and validation. Mutual correlations between each model were analyzed. The accuracies of integrating clinical variables, cML, and DL models were then evaluated.ResultsGood predictive accuracies were showed across the two cohorts in the five cML models, especially the random forest algorithm (AUC = 0.967 and 0.964, respectively). DL showed high accuracies in the training and validation cohorts (AUC = 0.981 and 0.972, respectively). Significant mutual correlations were revealed between tumor size and the five cML models and DL model (each P < 0.001). The highest accuracies were achieved by integrating DL and the random forest algorithm in the training and validation cohorts (AUC = 0.995 and 0.994, respectively).ConclusionThe radiomics cML models and DL model showed notable accuracy for predicting the initial response to TACE treatment. Moreover, the integrated model could serve as a novel and accurate method for prediction in intermediate-stage HCC.

Imaging in interventional oncology, the better you see, the better you treat

“…For example, multiple high-quality studies reached good performance in predicting outcome before transarterial chemoembolizations (TACE) in hepatocellular carcinoma (HCC). [83][84][85][86][87][88] Transarterial radioembolization (TARE) has been investigated less extensively compared to TACE. Only some pilot studies have suggested radiomics features might be associated with outcome after TARE for HCC, liver metastases and intrahepatic cholangiocarcinoma.…”

Deep learning based radiomics for gastrointestinal cancer diagnosis and treatment: A minireview

“…Its performance was assessed using confusion matrices and receiver operating characteristic curves, and the model attained an AUC over 0.90 for all four classes in both validation sets, and accuracies of 85.1% and 82.8% for validation sets 1 and 2, respectively. In the next year, they combined conventional radiomics and DL to build a new CECT-based DLR model that served to predict the initial treatment response to TACE of HCC patients preoperatively[ 46 ]. Different from their prior work, they designed their own CNN for feature extraction and prediction, and the DL model was integrated with five radiomics models built with different classic machine learning algorithms or tumor size feature to build integrated models for efficacy comparison.…”

Dynamic radiomics for predicting the efficacy of antiangiogenic therapy in colorectal liver metastases

“…Although some PET and MRI based radiomics studies have achieved remarkable results in the field of metastatic colorectal cancer ( 12 , 13 ), CT based imaging criteria are still the preferred criteria for evaluation of tumor drug response in clinical trials so far. CT-based radiomics has been shown to help predict therapy response and outcome in multiple cancers, including CRC ( 14 – 16 ). Ligero et al.…”