A Machine Learning Model to Predict Hepatocellular Carcinoma Response to Transcatheter Arterial Chemoembolization

Purpose: To evaluate a fully automated machine learning algorithm that uses pretherapeutic quantitative CT image features and clinical factors to predict hepatocellular carcinoma (HCC) response to transcatheter arterial chemoembolization (TACE). Materials and Methods: Outcome information from 105 patients receiving first-line treatment with TACE was evaluated retrospectively. The primary clinical endpoint was time to progression (TTP) based on follow-up CT radiologic criteria (modified Response Evaluation Criteria in Solid Tumors). A 14-week cutoff was used to classify patients as TACE-susceptible (TTP >= 14 weeks) or TACE-refractory (TTP < 14 weeks). Response to TACE was predicted using a random forest classifier with the Barcelona Clinic Liver Cancer (BCLC) stage and quantitative image features as input, as well as the BCLC stage alone as a control. Results: The model's response prediction accuracy rate was 74.2% (95% confidence interval [CI]: 64%, 82%) using a combination of the BCLC stage plus quantitative image features versus 62.9% (95% CI: 52%, 72%) using the BCLC stage alone. Shape image features of the tumor and background liver were the dominant features correlated to the TTP as selected by the Boruta method and were used to predict the outcome. Conclusion: This preliminary study demonstrated that quantitative image features obtained prior to therapy can improve the accuracy of predicting response of HCC to TACE. This approach is likely to provide useful information for aiding in selection of patients with HCC for TACE. (C) RSNA, 2019