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 (V_30Gy), conformity index (CI), and homogeneity index (HI) of planning target volume (PTV) and the minimum dose (D_100%) and maximum dose (D_max) for the hippocampus, D_max 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.