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基于放射组学特征的列线图预测 II 期和 III 期结肠癌无病生存。

Radiomic signature-based nomogram to predict disease-free survival in stage II and III colon cancer.

机构信息

Department of Radiology, Peking University People's Hospital, 11 Xizhimen South St., Beijing 100044, China.

Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, School of Computer Science and Technology, Guizhou University, 2708 Huaxi Avenue South St., Guiyang 550025, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.

出版信息

Eur J Radiol. 2020 Oct;131:109205. doi: 10.1016/j.ejrad.2020.109205. Epub 2020 Aug 19.

Abstract

PURPOSE

To develop a radiomic nomogram to predict disease-free survival (DFS) in patients with colon cancer.

METHODS

We retrospectively identified 302 patients with stage III colon cancer and 269 patients with stage II colon cancer who had undergone multidetector computed tomography (MDCT) and radical resection between January 2009 and December 2015. Patients were divided into a training cohort (n = 322) and an external validation cohort (n = 249). Radiomic features were extracted from MDCT images, and a radiomic signature was built as to predict DFS. A radiomic nomogram integrating the radiomic signature and clinicopathologic characteristics was developed using multivariable logistic regression. The nomogram was evaluated with regard to calibration, discrimination, and clinical utility.

RESULTS

The radiomic signature was an independent prognostic factor for DFS in the training cohort (HR = 1.102; 95 % CI: 1.052-1.156; P < 0.001) and the external validation cohort (HR = 1.157; 95 % CI: 1.030-1.301; P = 0.014). The radiomic signature-based nomogram was more effective at predicting DFS than either the TNM staging system or a clinicopathologic nomogram. The C-indices of the radiomic nomogram and TNM staging system were 0.780 (95 % CI: 0.734-0.847) and 0.738 (0.687-0.784) respectively. The radiomic signature-based nomogram demonstrated good fitness (shown by calibration curves) and clinical usefulness (shown by decision curve analysis).

CONCLUSION

A radiomic signature derived from MDCT images can effectively predict DFS in patients with stage II and III colon cancer and could be used as a supplement for risk stratification.

摘要

目的

开发一种基于放射组学的列线图,以预测结肠癌患者的无病生存(DFS)。

方法

我们回顾性地纳入了 2009 年 1 月至 2015 年 12 月期间接受多排螺旋 CT(MDCT)和根治性切除术的 302 例 III 期结肠癌患者和 269 例 II 期结肠癌患者。将患者分为训练队列(n=322)和外部验证队列(n=249)。从 MDCT 图像中提取放射组学特征,并构建放射组学特征以预测 DFS。使用多变量逻辑回归构建整合放射组学特征和临床病理特征的放射组学列线图。通过校准、区分和临床实用性评估该列线图。

结果

在训练队列(HR=1.102;95%CI:1.052-1.156;P<0.001)和外部验证队列(HR=1.157;95%CI:1.030-1.301;P=0.014)中,放射组学特征是 DFS 的独立预后因素。基于放射组学特征的列线图在预测 DFS 方面比 TNM 分期系统或临床病理列线图更有效。放射组学列线图和 TNM 分期系统的 C 指数分别为 0.780(95%CI:0.734-0.847)和 0.738(0.687-0.784)。基于放射组学特征的列线图具有良好的拟合度(通过校准曲线显示)和临床实用性(通过决策曲线分析显示)。

结论

从 MDCT 图像中提取的放射组学特征可有效预测 II 期和 III 期结肠癌患者的 DFS,可作为风险分层的补充。

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