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REVIEW article

Front. Oncol.
Sec. Radiation Oncology
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1507592
This article is part of the Research Topic Optimizing Radiotherapy for Cervical Cancer Efficacy Toxicity and Brachytherapy Integration View all articles

Artificial Intelligence in High-Dose-Rate Brachytherapy Treatment Planning for Cervical Cancer: A Review

Provisionally accepted
Junyue Shi Junyue Shi 1Jun Chen Jun Chen 2Gaokui He Gaokui He 1Qinghe Peng Qinghe Peng 3*
  • 1 Department of Nuclear Technology Application, China Institute of Atomic Energy, Beijing, Beijing, China
  • 2 Department of Radiation Oncology, Foresea Life Insurance Guangzhou General Hospital, Guangzhou, Guangdong Province, China
  • 3 Sun Yat-sen University Cancer Center (SYSUCC), Guangzhou, China

The final, formatted version of the article will be published soon.

    Cervical cancer remains a significant global health concern, characterized by high morbidity and mortality rates. High-dose-rate brachytherapy (HDR-BT) is a critical component of cervical cancer treatment, requiring precise and efficient treatment planning. However, the process is labor-intensive, heavily reliant on operator expertise, and prone to variability due to factors such as applicator shifts and organ filling changes. Recent advancements in artificial intelligence (AI), particularly in medical image processing, offer significant potential for automating and standardizing treatment planning in HDR-BT. This review examines the progress and challenge of AI applications in HDR-BT treatment planning, focusing on automatic segmentation, applicator reconstruction, dose calculation, and plan optimization. By addressing current limitations and exploring future directions, this paper aims to guide the integration of AI into clinical practice, ultimately improving treatment accuracy, reducing preparation time, and enhancing patient outcomes.

    Keywords: Artificial intelligence (AI), cervical cancer, high-dose-rate brachytherapy (HDR-BT), treatment planning, deep learning (DL)

    Received: 08 Oct 2024; Accepted: 10 Jan 2025.

    Copyright: © 2025 Shi, Chen, He and Peng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Qinghe Peng, Sun Yat-sen University Cancer Center (SYSUCC), Guangzhou, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.