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• W2336302003 abstract "// Cuishan Liang 1, 2, * , Yanqi Huang 1, 2, * , Lan He 1, 3 , Xin Chen 4 , Zelan Ma 1, 2 , Di Dong 5 , Jie Tian 5 , Changhong Liang 1 , Zaiyi Liu 1 1 Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China 2 Graduate College, Southern Medical University, Guangzhou, 510515, China 3 School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510006, China 4 Department of Radiology, The Affiliated Guangzhou First People’ Hospital, Guangzhou Medical University, Guangzhou, 510180, China 5 Key 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-II vs. 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." @default.
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• W2336302003 date "2016-04-22" @default.
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• W2336302003 title "The development and validation of a CT-based radiomics signature for the preoperative discrimination of stage I-II and stage III-IV colorectal cancer" @default.
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• W2336302003 doi "https://doi.org/10.18632/oncotarget.8919" @default.
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