Research Papers:
The development and validation of a CTbased radiomics signature for the preoperative discrimination of stage III and stage IIIIV colorectal cancer
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Cuishan Liang1,2,*, Yanqi Huang1,2,*, Lan He1,3, Xin Chen4, Zelan Ma1,2, Di Dong5, Jie Tian5, Changhong Liang1, Zaiyi Liu1
1Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
2Graduate College, Southern Medical University, Guangzhou, 510515, China
3School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510006, China
4Department of Radiology, The Affiliated Guangzhou First People’ Hospital, Guangzhou Medical University, Guangzhou, 510180, China
5Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, 100190, China
*These authors have contributed equally to this work
Correspondence to:
Zaiyi Liu, email: zyliu@163.com
Changhong Liang, email: cjr.lchh@vip.163.com
Keywords: colorectal cancer, computed tomography, radiomics signature, predictor, stage
Received: December 25, 2015 Accepted: April 2, 2016 Published: April 22, 2016
ABSTRACT
Objectives: To investigative the predictive ability of radiomics signature for preoperative staging (I-IIvs.III-IV) of primary colorectal cancer (CRC).
Methods: This study consisted of 494 consecutive patients (training dataset: n=286; validation cohort, n=208) with stage I–IV CRC. A radiomics signature was generated using LASSO logistic regression model. Association between radiomics signature and CRC staging was explored. The classification performance of the radiomics signature was explored with respect to the receiver operating characteristics(ROC) curve.
Results: The 16-feature-based radiomics signature was an independent predictor for staging of CRC, which could successfully categorize CRC into stage I-II and III-IV (p <0.0001) in training and validation dataset. The median of radiomics signature of stage III-IV was higher than stage I-II in the training and validation dataset. As for the classification performance of the radiomics signature in CRC staging, the AUC was 0.792(95%CI:0.741-0.853) with sensitivity of 0.629 and specificity of 0.874. The signature in the validation dataset obtained an AUC of 0.708(95%CI:0.698-0.718) with sensitivity of 0.611 and specificity of 0.680.
Conclusions: A radiomics signature was developed and validated to be a significant predictor for discrimination of stage I-II from III-IV CRC, which may serve as a complementary tool for the preoperative tumor staging in CRC.