CT-based Radiomics Signature to Discriminate High-grade From Low-grade Colorectal Adenocarcinoma

Publication date: Available online 2 March 2018
Source:Academic Radiology
Author(s): Xiaomei Huang, Zixuan Cheng, Yanqi Huang, Cuishan Liang, Lan He, Zelan Ma, Xin Chen, Xiaomei Wu, Yexing Li, Changhong Liang, Zaiyi Liu
Rationale and ObjectivesTo develop and validate a computed tomography-based radiomics signature for preoperatively discriminating high-grade from low-grade colorectal adenocarcinoma (CRAC).Materials and MethodsThis retrospective study was approved by our institutional review board, and the informed consent requirement was waived. This study enrolled 366 patients with CRAC (training dataset: n = 222, validation dataset: n = 144) from January 2008 to August 2015. A radiomics signature was developed with the least absolute shrinkage and selection operator method in training dataset. Mann-Whitney U test was applied to explore the correlation between radiomics signature and histologic grade. The discriminative power of radiomics signature was investigated with the receiver operating characteristics curve. An independent validation dataset was used to confirm the predictive performance. We further performed a stratified analysis to validate the predictive performance of radiomics signature in colon adenocarcinoma and rectal adenocarcinoma.ResultsThe radiomics signature demonstrated discriminative performance for high-grade and low-grade CRAC, with an area under the curve of 0.812 (95% confidence interval [CI]: 0.749–0.874) in training dataset and 0.735 (95%CI: 0.644–0.826) in validation dataset. Stratified analysis demonstrated that radiomics signature also showed distinguishing ability for histologic grade in both colon adenocarcinoma and rectal adenocarcinoma, with area under the curve of 0.725 (95%CI: 0.653–0.797) and 0.895 (95%CI: 0.838–0.952), respectively.ConclusionsWe developed and validated a radiomics signature as a complementary tool to differentiate high-grade from low-grade CRAC preoperatively, which may make contribution to personalized treatment.



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