End-to-end automated radiotherapy planning using transfer learning to overcome data scarcity.

Lu Jiang¹, Emily Hirata¹, Evan Porter¹, Di Xu¹, Jiayi Du¹, Wensha Yang¹, Qihui Lyu¹, Minsong Cao¹, Ke Sheng¹

¹Department of Radiation Oncology, UCSF, San Francisco, California, USA.

Abstract

PURPOSE: To develop and benchmark 3D dose prediction algorithms for volumetric modulated arc therapy for head-and-neck (HN) and pancreatic cancer treatments, and to demonstrate the clinical feasibility of converting the predicted doses into deliverable plans. METHODS AND MATERIALS: Three deep learning models were pretrained on 3232 public plans and fine-tuned on institutional cohorts (256 HN and…

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