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ORIGINAL RESEARCH article
Front. Oncol.
Sec. Radiation Oncology
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1534740
This article is part of the Research Topic Recent Advances in Radiation Oncology for the Management of Thoracic Malignancies View all 4 articles
Artificial Intelligence in Automatic Image Segmentation System for Exploring Recurrence Patterns in Small Cell Carcinoma of the Lung
Provisionally accepted
Jie Shen 1,2,3*
Jing Shen 1,2,3
Shaobin Wang 4 Hui Guan 1,2,3 Mingyi Di 5 Zhikai Liu 1,2,5
Qi Chen 6 Mei Li 1,3
Ke Hu 1,2
Fuquan ZHANG 1,3- 1 Chinese Academy of Medical Sciences and Peking Union Medical College, Dongcheng, China
- 2 Peking Union Medical College Hospital (CAMS), Beijing, Beijing Municipality, China
- 3 Department of Radiation Oncology, Peking Union Medical College Hospital, Department of Medical Oncology, Peking Union Medical College Hospital (CAMS), Beijing, Beijing, China
- 4 Department of Biomedical Engineering,School of Medicine, School of Medicine, Tsinghua University, Beijing, Beijing Municipality, China
- 5 Peking university first hospital, Beijing, China
- 6 MedMind Technology Co., Ltd. Beijing, China, Beijing, China
The integration of artificial intelligence (AI) in automatic image segmentation systems enhances clinical target volume (CTV) evaluation for small cell lung cancer (SCLC). This study analyzed data from 180 SCLC patients (2010–2021) treated with curative radiotherapy, utilizing AI-driven segmentation and recursive feature elimination to model recurrence. Tumor size (≥5cm) independently impacted local control (HR=1.635, p=0.028), with 3-year recurrence rates of 61.1% vs. 86.7% (p=0.004). Recurrence predominantly occurred in regions 10R, 10L, 4R, and 7 (67.65% of cases). A random forest model incorporating 110 clinical variables achieved 77% accuracy in predicting recurrence. AI-based CTV delineation identified initial tumor regions (GTV/GTVnd) as critical recurrence zones, offering a clinically viable tool for optimizing radiotherapy targeting and patient outcomes.
Keywords: Small Cell Lung Cancer, Clinical target volume (CTV), artificial intelligence, Local recurrence, Prediction model
Received: 26 Nov 2024; Accepted: 04 Apr 2025.
Copyright: © 2025 Shen, Shen, Wang, Guan, Di, Liu, Chen, Li, Hu and ZHANG. 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:
Jie Shen, Chinese Academy of Medical Sciences and Peking Union Medical College, Dongcheng, China
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