Criteria-calibration approaches to deep learning-based cervical cancer radiation treatment auto-planning.

ABSTRACT: Knowledge-Based Planning (KBP) pipelines, which integrate machine learning-based models to predict dose distribution, have gained popularity in clinical radiation therapy. However, for patients with specific requirements, the trained models may struggle to rapidly adjust to guide the automatic planning process. Therefore, the aim of this study was to calibrate the dose prediction model to improve the quality and accuracy of automatic planning for cervical cancer radiation therapy. We retrospectively collected a routine cervical cancer dataset (200 cases) to conduct the KBP pipelines for automatically generating radiation planning, and a small number of ovarian-protection and myelosuppressive datasets (21 cases) to calibrate and evaluate the dose prediction model. A total of three criteria-

SUBMITTER: Liang Y 

PROVIDER: S-EPMC12369083 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature