Research Papers:

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

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

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Oncotarget. 2017; 8:55308-55318. https://doi.org/10.18632/oncotarget.19427

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Sha Sa1, Jing Li1, Xiaodong Li1, Yongrui Li1, Xiaoming Liu2, Defeng Wang3,4, Huimao Zhang1 and Yu Fu1

1Department of Radiology, The First Hospital of Jilin University, Changchun, China

2College of Electronic Science and Engineering, Jilin University, Changchun, China

3Research Center for Medical Image Computing, Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

4Union Medical Imaging Research Institute, Shenzhen, China

Correspondence to:

Huimao Zhang, email: [email protected]

Yu Fu, email: [email protected]

Keywords: colorectal cancer, T-staging, model, development and validation, random forest

Received: March 29, 2017     Accepted: July 12, 2017     Published: July 21, 2017


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.

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