微信公众号

官网二维码
中国癌症防治杂志

中国癌症防治杂志 ›› 2025, Vol. 17 ›› Issue (5): 568-575.doi: 10.3969/j.issn.1674-5671.2025.05.07

• 论著 • 上一篇    下一篇

基于CT影像组学预测局部晚期直肠癌新辅助放化疗后的放射性直肠炎发生风险

  

  1. 南京医科大学公共卫生学院；浙江省肿瘤医院/中国科学院杭州医学研究所；荷兰马斯特里赫特大学医学中心GROW肿瘤学院放射肿瘤学系
  • 出版日期:2025-10-25 发布日期:2025-12-03
  • 通讯作者: 朱骥 E-mail:zhuji@zjcc.org.cn
  • 基金资助:
    国家自然科学基金面上项目（82574019）；国家自然科学基金青年项目（82303672）；国家卫生健康委员会科研基金（省部共建）重大项目（WKJ?ZJ?2305）；嘉兴市重点研发计划项目（2024BZ20004）

Prediction risk of radiation proctitis after neoadjuvant chemoradiotherapy in locally advanced rectal cancer using CT radiomics 

  • Online:2025-10-25 Published:2025-12-03

PDF (PC)

1

赞

摘要： 目的 构建基于计算机断层扫描（computed tomography，CT）影像组学的机器学习模型，预测局部晚期直肠癌患者（locally advanced rectal cancer, LARC）新辅助放化疗（neoadjuvant chemoradiotherapy，nCRT）后放射性直肠炎的发生风险。方法 回顾性纳入2021年12月至2024年12月于浙江省肿瘤医院接受nCRT的LARC患者，作为训练集（n=326）;前瞻性纳入2022年8月至2024年8月于浙江省肿瘤医院接受nCRT的LARC患者作为验证集（n=104）。采集治疗前CT手动勾画直肠系膜区感兴趣区域，分别采用Pyradiomics与预训练的ResNet18提取手工特征与深度学习特征。通过单因素Logistic回归、Spearman相关性分析、最小绝对收缩和选择算子回归、递归特征消除进行特征筛选，分别采用Logistic回归、支持向量机、轻量级梯度提升机和极度梯度提升构建手工特征独立模型（Rad⁃model）、深度学习特征独立模型（DL⁃model）以及两者的联合模型（DLRad⁃model）。采用受试者工作特征曲线下面积（area under the curve,AUC）等评估模型的预测性能。结果 训练集与验证集2级及以上放射性直肠炎发生率差异无统计学意义（36.81% vs 25.96%， P=0.056）。基于极度梯度提升构建的DL⁃model与DLRad⁃model的AUC在训练集中分别为0.901（95%CI：0.874~0.927）与0.918（95%CI：0.893~0.940），在验证集中分别为0.747（95%CI：0.644~0.845）与0.729（95%CI：0.620~0.829），均高于Rad⁃model的AUC（均P<0.05），但DL⁃model与DLRad⁃model的AUC比较差异无统计学意义（P>0.05）。结论 深度学习特征在预测LARC患者nCRT后的放射性直肠炎发生风险方面具有优势，可为临床风险分层提供有效工具。

关键词: 局部晚期直肠癌, 新辅助放化疗, 影像组学, 深度学习, 放射性直肠炎

Abstract: Objective To develop a machine learning model based on computed tomography (CT) radiomics for predicting the risk of radiation proctitis in patients with locally advanced rectal cancer (LARC) after neoadjuvant chemoradiotherapy (nCRT). Methods Patients with LARC underwent nCRT at Zhejiang Cancer Hospital from December 2021 to December 2024 were retrospectively enrolled as the training set (n=326), while patients underwent nCRT from August 2022 to August 2024 were prospectively enrolled as the validation set (n=104). The region of interest in mesorectal region was manually delineated on pre⁃treatment CT images. Handcrafted features and deep learning features were extracted using Pyradiomics and a pre⁃trained ResNet18 model, respectively. Feature selection was performed using univariable Logistic regression, Spearman correlation analysis, least absolute shrinkage and selection operator regression, and recursive feature elimination. Logistic regression, support vector machine, light gradient boosting machine, and eXtreme gradient boosting were employed to develop independent radiomics models （Rad⁃model）, independent models based on deep learning features （DL⁃model）, and the combined model integrating both feature sets （DLRad⁃model）. The predictive performance of the models was evaluated using the area under the curve (AUC) of receiver operating characteristic. Results The incidence of grade 2 or higher radiation proctitis did not differ significantly between the training set and the validation set (36.81% vs 25.96%, P=0.056). Both the DL⁃model and the DLRad⁃model constructed based on eXtreme gradient boosting achieved AUC of 0.901 (95%CI: 0.874-0.927) and 0.918 (95%CI: 0.893-0.940), respectively, in the training set. In the validation set, these models achieved AUC of 0.747 (95%CI: 0.644-0.845) and 0.729 (95%CI: 0.620-0.829), respectively. Both of which were significantly higher than that of the Rad⁃model (all P<0.05). However,  the difference in AUC between the DL⁃model and the DLRad⁃model was not statistically significant (P>0.05). Conclusions Deep learning features demonstrate advantages in predicting the risk of radiation proctitis after nCRT in patients with LARC, and providing an effective tool for clinical risk stratification.

Key words: Locally advanced rectal cancer, Neoadjuvant chemoradiotherapy, Radiomics, Deep learning, Radiation proctitis

中图分类号: 

  •