Improving Adjuvant Liver-Directed Treatment Recommendations for Unresectable Hepatocellular Carcinoma: An Artificial Intelligence-Based Decision-Making Tool

PURPOSE

Liver-directed therapy after transarterial chemoembolization (TACE) can lead to improvement in survival for selected patients with unresectable hepatocellular carcinoma (HCC). However, there is uncertainty in the appropriate application and modality of therapy in current clinical practice guidelines. The aim of this study was to develop a proof-of-concept, machine learning (ML) model for treatment recommendation in patients previously treated with TACE and select patients who might benefit from additional treatment with combination stereotactic body radiotherapy (SBRT) or radiofrequency ablation (RFA).

METHODS

This retrospective observational study was based on data from an urban, academic hospital system selecting for patients diagnosed with stage I-III HCC from January 1, 2008, to December 31, 2018, treated with TACE, followed by adjuvant RFA, SBRT, or no additional liver-directed modality. A feedforward, ML ensemble model provided a treatment recommendation on the basis of pairwise assessments evaluating each potential treatment option and estimated benefit in survival.

RESULTS

Two hundred thirty-seven patients met inclusion criteria, of whom 54 (23%) and 49 (21%) received combination of TACE and SBRT or TACE and RFA, respectively. The ML model suggested a different consolidative modality in 32.7% of cases among patients who had previously received combination treatment. Patients treated in concordance with model recommendations had significant improvement in progression-free survival (hazard ratio 0.5; P = .007). The most important features for model prediction were cause of cirrhosis, stage of disease, and albumin-bilirubin grade (a measure of liver function).

CONCLUSION

In this proof-of-concept study, an ensemble ML model was able to provide treatment recommendations for HCC who had undergone prior TACE. Additional treatment in line with model recommendations was associated with significant improvement in progression-free survival, suggesting a potential benefit for ML-guided medical decision making.

Retrospective analysis of stereotactic body radiation therapy efficacy over radiofrequency ablation for hepatocellular carcinoma

BACKGROUND AND PURPOSE

To evaluate the efficacy of stereotactic body radiotherapy (SBRT) and radiofrequency ablation (RFA) for hepatocellular carcinoma (HCC).

METHODS AND MATERIALS

Patients treated for HCC between 2012 and 2016 were reviewed. Among these, 668 patients who underwent RFA of 736 tumors and 105 patients who underwent SBRT of 114 tumors were included. Using propensity score matching (PSM) to adjust for clinical factors, 95 tumors were selected from each treatment arm. Freedom from local progression (the primary endpoint, FFLP) was compared before and after adjustment with PSM.

RESULTS

At baseline, SBRT-treated tumors were more advanced, larger (median, 2.4 vs. 1.6 cm), and more frequently located in the subphrenic region than RFA-treated tumors (P < .001). The median follow-up was 21.5 (interquartile range, 11.2-36.7) months. Before PSM, the 2-year FFLP rates were 76.3% for the SBRT group and 70.2% for the RFA groups, respectively. After PSM, the 2-year FFLP rates were 74.9% for the SBRT group and 64.9% for the RFA group, respectively. The local control rates were not significantly different. The Cox proportional hazards model revealed the treatment modality as an independent predictor of local recurrence favoring SBRT in the entire cohort and in the PSM model. Elevated tumor markers, tumor location (subphrenic region), and tumor size (>2.0 cm) were also independent predictors of local progression.

CONCLUSION

SBRT appears to be an effective alternative treatment for HCC when RFA is not feasible due to tumor location or size.