基于MDCT图像和临床信息的结直肠癌T分期术前预测模型的开发与验证

Development and validation of a preoperative prediction model for colorectal cancer T-staging based on MDCT images and clinical information.

作者信息

Sa Sha, Li Jing, Li Xiaodong, Li Yongrui, Liu Xiaoming, Wang Defeng, Zhang Huimao, Fu Yu

机构信息

Department of Radiology, The First Hospital of Jilin University, Changchun, China.

College of Electronic Science and Engineering, Jilin University, Changchun, China.

出版信息

Oncotarget. 2017 Jul 21;8(33):55308-55318. doi: 10.18632/oncotarget.19427. eCollection 2017 Aug 15.

DOI:10.18632/oncotarget.19427

PMID:28903421

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5589660/

Abstract

OBJECTIVES

This study aimed to establish and evaluate the efficacy of a prediction model for colorectal cancer T-staging.

RESULTS

T-staging was positively correlated with the level of carcinoembryonic antigen (CEA), expression of carbohydrate antigen 19-9 (CA19-9), wall deformity, blurred outer edges, fat infiltration, infiltration into the surrounding tissue, tumor size and wall thickness. Age, location, enhancement rate and enhancement homogeneity were negatively correlated with T-staging. The predictive results of the model were consistent with the pathological gold standard, and the kappa value was 0.805. The total accuracy of staging improved from 51.04% to 86.98% with the proposed model.

MATERIALS AND METHODS

The clinical, imaging and pathological data of 611 patients with colorectal cancer (419 patients in the training group and 192 patients in the validation group) were collected. A spearman correlation analysis was used to validate the relationship among these factors and pathological T-staging. A prediction model was trained with the random forest algorithm. T staging of the patients in the validation group was predicted by both prediction model and traditional method. The consistency, accuracy, sensitivity, specificity and area under the curve (AUC) were used to compare the efficacy of the two methods.

CONCLUSIONS

The newly established comprehensive model can improve the predictive efficiency of preoperative colorectal cancer T-staging.

摘要

目的

本研究旨在建立并评估结直肠癌T分期预测模型的疗效。

结果

T分期与癌胚抗原（CEA）水平、糖类抗原19-9（CA19-9）表达、肠壁变形、边缘模糊、脂肪浸润、周围组织浸润、肿瘤大小及肠壁厚度呈正相关。年龄、肿瘤位置、强化率及强化均匀性与T分期呈负相关。该模型的预测结果与病理金标准一致，kappa值为0.805。采用所提出的模型，分期的总准确率从51.04%提高到了86.98%。

材料与方法

收集611例结直肠癌患者（训练组419例，验证组192例）的临床、影像及病理资料。采用Spearman相关性分析验证这些因素与病理T分期之间的关系。使用随机森林算法训练预测模型。采用预测模型和传统方法对验证组患者的T分期进行预测。使用一致性、准确性、敏感性、特异性及曲线下面积（AUC）比较两种方法的疗效。