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. Keywords: CT, Chemoembolization, Liver © RSNA, 2019 See also commentary by Chapiro and Duncan in this issue.