Comparison of 2 methods for prediction of liver dosimetric indices in hepatocellular cancer IMRT planning

Effects of stereotactic body radiotherapy for clinical outcomes of patients with liver metastasis and hepatocellular carcinoma: A retrospective study

This retrospective clinical study described the treatment efficacy and safety of stereotactic body radiotherapy (SBRT) for patients of hepatocellular carcinoma (HCC) and liver metastasis tumors. The therapeutic effect and prognosis of patients with liver cancer treated with stereotactic body radiation therapy (SBRT) at the Fudan University Shanghai Cancer Center (Shanghai, China) between July 2011 and December 2020 were retrospectively analyzed. Overall survival (OS), local control (LC) rates and progression-free survival (PFS) were evaluated using Kaplan-Meier analysis and the log-rank test. Local progression was defined as tumor growth after SBRT on dynamic computed tomography follow-up. Treatment-related toxicities were assessed according to the Common Terminology Criteria for Adverse Events version 4. A total of 36 patients with liver cancer were enrolled in the present study. The prescribed dosages (14 Gy in 3 fractions or 16 Gy in 3 fractions) were applied for SBRT treatments. The median follow-up time was 21.4 months. The median OS time was 20.4 [95% confidence interval (CI): 6.6-34.2] months, and the 2-year OS rates for the total population, HCC group and liver metastasis group were 47.5, 73.3 and 34.2%, respectively. The median PFS time was 17.3 (95% CI: 11.8-22.8) months and the 2-year PFS rates for the total population, HCC group and liver metastasis group were 36.3, 44.0 and 31.4%, respectively. The 2-year LC rates for the total population, HCC group and liver metastasis group were 83.4, 85.7 and 81.6%, respectively. The most common grade IV toxicity for the HCC group was liver function impairment (15.4%), followed by thrombocytopenia (7.7%). There were no grade III/IV radiation pneumonia or digestive discomfort. The present study aimed to explore a safe, effective and non-invasive treatment method for liver tumors. At the same time, the innovation of the present study is to find a safe and effective prescription dose of SBRT in the absence of consensus on guidelines.

Predictive Modeling of Survival and Toxicity in Patients With Hepatocellular Carcinoma After Radiotherapy

PURPOSE

To stratify patients and aid clinical decision making, we developed machine learning models to predict treatment failure and radiation-induced toxicities after radiotherapy (RT) in patients with hepatocellular carcinoma across institutions.

MATERIALS AND METHODS

The models were developed using linear and nonlinear algorithms, predicting survival, nonlocal failure, radiation-induced liver disease, and lymphopenia from baseline patient and treatment parameters. The models were trained on 207 patients from Massachusetts General Hospital. Performance was quantified using Harrell's c-index, area under the curve (AUC), and accuracy in high-risk populations. Models' structures were optimized in a nested cross-validation approach to prevent overfitting. A study analysis plan was registered before external validation using 143 patients from MD Anderson Cancer Center. Clinical utility was assessed using net-benefit analysis.

RESULTS

The survival model stratified high-risk versus low-risk patients well in the external validation cohort (c-index = 0.75), better than existing risk scores. Predictions of 1-year survival and nonlocal failure were excellent (external AUC = 0.74 and 0.80, respectively), especially in the high-risk group (accuracy > 90%). Cause-of-death analysis showed differential modes of treatment failure in these cohorts and indicated that these models could be used to stratify RT patients for liver-sparing treatment regimen or combination approaches with systemic agents. Predictions of liver disease and lymphopenia were good but less robust (external AUC = 0.68 and 0.7, respectively), suggesting the need for more comprehensive consideration of dosimetry and better predictive biomarkers. The liver disease model showed excellent accuracy in the high-risk group (92%) and revealed possible interactions of platelet count with initial liver function.

CONCLUSION

Machine learning approaches can provide reliable outcome predictions in patients with hepatocellular carcinoma after RT in diverse cohorts across institutions. The excellent performance, particularly in high-risk patients, suggests novel strategies for patient stratification and treatment selection.