Artificial Intelligence-Facilitated Online Adaptive Proton Therapy Using Pencil Beam Scanning Proton Therapy

Kavli Affiliate: Jie Shan

| First 5 Authors: Hongying Feng, Jie Shan, Carlos E. Vargas, Sameer R. Keole, Jean-Claude M. Rwigema

| Summary:

We propose an oAPT workflow that incorporates all these functionalities and
validate its clinical implementation feasibility with prostate patients.
AI-based auto-segmentation tool AccuContourTM (Manteia, Xiamen, China) was
seamlessly integrated into oAPT. Initial spot arrangement tool on the vCT for
re-optimization was implemented using raytracing. An LET-based biological
effect evaluation tool was developed to assess the overlap region of high dose
and high LET in selected OARs. Eleven prostate cancer patients were
retrospectively selected to verify the efficacy and efficiency of the proposed
oAPT workflow. The time cost of each component in the workflow was recorded for
analysis. The verification plan showed significant degradation of the CTV
coverage and rectum and bladder sparing due to the interfractional anatomical
changes. Re-optimization on the vCT resulted in great improvement of the plan
quality. No overlap regions of high dose and high LET distributions were
observed in bladder or rectum in re-plans. 3D Gamma analyses in PSQA confirmed
the accuracy of the re-plan doses before delivery (Gamma passing rate =
99.57%), and after delivery (98.59%). The robustness of the re-plans passed all
clinical requirements. The average time for the complete execution of the
workflow was 9.12minutes, excluding manual intervention time. The
AI-facilitated oAPT workflow was demonstrated to be both efficient and
effective by generating a re-plan that significantly improved the plan quality
in prostate cancer treated with PBSPT.

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