Multi-institutional Knowledge-Based (KB) plan prediction benchmark models for whole breast irradiation

Purpose: To train and validate KB prediction models by merging a large multi-institutional cohort of whole breast irradiation (WBI) plans using tangential fields.

Methods: Ten institutions (INST1-INST10, 1481 patients) developed their KB-institutional models for left/right WBI (ten models for right and eight models for left). The transferability of models among centers was assessed based on the overlap of the geometric Principal Component (PC1) of each model when applied to other institutions and/or on the presence of significantly different optimization policies. Centers corresponding to transferable models were asked to join the building of two KB-benchmark models for right/left breast. Dose-volume histogram (DVH) prediction bands (lung/heart) were compared against those of the KB-institutional models.

Results: All models were transferable except INST6 (right breast) and INST1 (left breast). Planning data from 6 institutions for right breast and 5 institutions for left breast (out of 9 and 7 institutions with transferable models, respectively) were combined, totaling data from 850 patients. Prediction bands on the test cohorts (n = 30/25 right/left) showed a large overlap with bands of each institution model: for the right-breast, the KB-benchmark model predicts slightly lower lung D_mean when compared to KB-institution models, except for INST7. Regarding the left-breast, even greater similarity between KB-benchmark and KB-institution model predictions was found.

Conclusions: Multi-institutional KB-benchmark models for WBI were successfully generated. They may be employed by other users, representing the performances reached in a multi-institutional context of experienced centers. KB-benchmark models can also have significant applications for large-scale automatic plan optimization, QA/audit and tutoring/education purposes.