Developing an AI-assisted planning pipeline for hippocampal avoidance whole brain radiotherapy

Background and purpose Hippocampal avoidance whole brain radiotherapy (HA-WBRT) is effective for controlling disease and preserving neuro-cognitive function for brain metastases. However, contouring and planning of HA-WBRT is complex and time-consuming. We designed and evaluated a pipeline using deep learning tools for a fully automated treatment planning workflow to generate HA-WBRT radiotherapy plans. Materials and methods We retrospectively collected 50 adult patients who received HA-WBRT. Using RTOG- 0933 clinical trial protocol guidelines, all organs-at-risk (OARs) and the clinical target volume (CTV) were contoured by experienced radiation oncologists. A deep-learning segmentation model was designed and trained. Next, we developed a volumetric-modulated arc therapy (VMAT) auto-planning algorithm for 30 Gy in 10 fractions. Automated segmentations were evaluated using the Dice similarity coefficient (DSC) and 95th-percentile Hausdorff distance (95 % HD). Auto-plans were evaluated by the percentage of PTV volume that receives 30 Gy (V30Gy), conformity index (CI), and homogeneity index (HI) of planning target volume (PTV) and the minimum dose (D100%) and maximum dose (Dmax) for the hippocampus, Dmax for the lens, eyes, optic nerve, brain stem, and chiasm. Results We developed a deep-learning segmentation model and an auto-planning script. For the 10 cases in the independent test set, the overall average DSC and 95 % HD of contours were greater than 0.8 and less than 7 mm, respectively. All auto-plans met the RTOG- 0933 criteria. The HA-WBRT plan automatically created time was about 10 min. Conclusions An artificial intelligence (AI)-assisted pipeline using deep learning tools can rapidly and accurately generate clinically acceptable HA-WBRT plans with minimal manual intervention and increase efficiency of this treatment for brain metastases.