An automated patient-specific segment reduction-based beam angle optimization technique for deep learning...

Deep learning (DL)-based auto-planning has emerged as a powerful tool for optimizing radiotherapy treatment plans, reducing variability, and improving efficiency. However, current approaches often rely on predefined beam angles and arc spans, which may not be optimal for individual patients. Automated beam angle optimization can further enhance plan quality, particularly in early-stage breast cancer radiotherapy, where precise beam configurations are crucial for balancing target coverage and organ-at-risk (OAR) sparing.

This study presents an automated segment reduction-based beam angle optimization technique to improve DL-based auto-planning for radiotherapy in early-stage breast cancer. The method optimizes arc spans for volumetric-modulated-arc-therapy (VMAT) and beam configurations for intensity-modulated-radiation-therapy (IMRT) to improve dose distribution while reducing OAR exposure.

Plans using three different irradiation strategies-partial arc VMAT (PA-VMAT), complex IMRT (C-IMRT), and simple IMRT (S-IMRT)-were generated using two full arcs for dose mimicking of the predicted dose, followed by the segment reduction performed using a stepwise PAMU (Product of segment Area and Monitor Units) thresholding approach to determine optimal arc spans and beam angles. These strategies were compared against the standard continuous partial arc VMAT (CPA-VMAT) technique currently used in our clinical practice. Twenty left-sided breast cancer patients treated under deep inspiration breath-hold (DIBH) conditions were included for evaluation. Plan quality was assessed using dosimetric criteria, conformity indices, dose mimicking index (DMI), and statistical comparisons.

PA-VMAT exhibited superior OAR sparing and the best overall dose mimicking performance, reducing the heart, left lung, and right lung mean doses by 27%, 11%, and 50%, respectively, compared to CPA-VMAT. C-IMRT provided the best target coverage but required higher monitor units, while S-IMRT showed suboptimal dose homogeneity. The automated segment reduction method significantly improved plan efficiency, optimizing beam angles without requiring manual intervention.

This study demonstrates the feasibility of an automated segment reduction-based optimization technique for DL auto-planning in early-stage breast cancer. PA-VMAT emerged as the preferred strategy, balancing plan quality, delivery efficiency, and OAR sparing. The proposed approach enhances treatment planning flexibility and will be incorporated into future clinical practice.