Application and progress of image segmentation in radiotherapy for rectal cancer
10.3760/cma.j.cn113030-20230830-00080
- VernacularTitle:图像分割在直肠癌放疗领域的应用及进展
- Author:
Yuanling TANG
1
;
Xin WANG
Author Information
1. 四川大学华西医院腹部肿瘤病房，成都 610041
- Keywords:
Rectal neoplasms;
Radiotherapy;
Artificial intelligence;
Image segmentation;
Efficacy prediction
- From:
Chinese Journal of Radiation Oncology
2024;33(9):859-863
- CountryChina
- Language:Chinese
-
Abstract:
With the rapid development of artificial intelligence technology, the research and application of image segmentation technology in the field of radiotherapy for rectal cancer have captivated increasing attention. Neoadjuvant chemoradiotherapy followed by radical surgery is the standard treatment for patients with locally advanced rectal cancer. Manual delineation of radiotherapy targets and organs at risk is a time-consuming and laborious task. Developing an automatic delineation model of radiotherapy targets using artificial intelligence can significantly improve the efficiency and robustness of target delineation. In addition, combined with radiomics methods, based on computed tomography (CT), magnetic resonance imaging (MRI), extracting radiation features from rectal tumor can build a model for efficacy evaluation and prediction of neoadjuvant therapy, which can help clinicians formulate individualized treatment regimens. Segmenting the region of interest (ROI) and extracting radiation features is a key step in model construction. This article will review the application of image segmentation in the field of radiotherapy for rectal cancer, aiming to explore the importance of image segmentation in radiotherapy for rectal cancer and future research directions.
