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Individualized Prediction of Perineural Invasion in Colorectal Cancer: Development and Validation of A Radiomics Prediction Model
Jul 10, 2018Author:
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 Title: Individualized Prediction of Perineural Invasion in Colorectal Cancer: Development and Validation of A Radiomics Prediction Model  

Authors: Huang, YQ; He, L; Dong, D; Yang, CY; Liang, CS; Chen, X; Ma, ZL; Huang, XM; Yao, S; Liang, CH; Tian, J; Liu, ZY  

Author Full Names: Huang, Yanqi; He, Lan; Dong, Di; Yang, Caiyun; Liang, Cuishan; Chen, Xin; Ma, Zelan; Huang, Xiaomei; Yao, Su; Liang, Changhong; Tian, Jie; Liu, Zaiyi  

Source: CHINESE JOURNAL OF CANCER RESEARCH, 30 (1):40-+; 10.21147/j.issn.1000-9604.2018.01.05 FEB 2018  

Language: English  

Abstract: Objective: To develop and validate a radiomics prediction model for individualized prediction of perineural invasion (PNI) in colorectal cancer (CRC). Methods: After computed tomography (CT) radiomics features extraction, a radiomics signature was constructed in derivation cohort (346 CRC patients). A prediction model was developed to integrate the radiomics signature and clinical candidate predictors [age, sex, tumor location, and carcinoembryonic antigen (CEA) level]. Apparent prediction performance was assessed. After internal validation, independent temporal validation (separate from the cohort used to build the model) was then conducted in 217 CRC patients. The final model was converted to an easy-to-use nomogram. Results: The developed radiomics nomogram that integrated the radiomics signature and CEA level showed good calibration and discrimination performance [Harrell's concordance index (c-index): 0.817; 95% confidence interval (95% CI): 0.811-0.823]. Application of the nomogram in validation cohort gave a comparable calibration and discrimination (c-index: 0.803; 95% CI: 0.794-0.812). Conclusions: Integrating the radiomics signature and CEA level into a radiomics prediction model enables easy and effective risk assessment of PNI in CRC. This stratification of patients according to their PNI status may provide a basis for individualized auxiliary treatment.  

ISSN: 1000-9604  

eISSN: 1993-0631  

IDS Number: FX9QN  

Unique ID: WOS:000426439800005