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Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer
IF = 32.956
Yan-Qi Huang 1, Chang-Hong Liang 1, Lan He 1, Jie Tian 1, Cui-Shan Liang 1, Xin Chen 1, Ze-Lan Ma 1, Zai-Yi Liu 2
Affiliations collapse
Affiliations
1 Yan-qi Huang, Chang-hong Liang, Lan He, Cui-shan Liang, Ze-lan Ma, and Zai-yi Liu, Guangdong General Hospital, Guangdong Academy of Medical Sciences; Yan-qi Huang, Cui-shan Liang, and Ze-lan Ma, Southern Medical University; Lan He, School of Medicine, South China University of Technology; Xin Chen, Affiliated Guangzhou First People’s Hospital, Guangzhou Medical University, Guangzhou; and Jie Tian, Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, People’s Republic of China.
2 Yan-qi Huang, Chang-hong Liang, Lan He, Cui-shan Liang, Ze-lan Ma, and Zai-yi Liu, Guangdong General Hospital, Guangdong Academy of Medical Sciences; Yan-qi Huang, Cui-shan Liang, and Ze-lan Ma, Southern Medical University; Lan He, School of Medicine, South China University of Technology; Xin Chen, Affiliated Guangzhou First People’s Hospital, Guangzhou Medical University, Guangzhou; and Jie Tian, Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, People’s Republic of China.
PMID: 27138577 DOI: 10.1200/JCO.2015.65.9128
Abstract
Purpose: To develop and validate a radiomics nomogram for preoperative prediction of lymph node (LN) metastasis in patients with colorectal cancer (CRC).
Patients and methods: The prediction model was developed in a primary cohort that consisted of 326 patients with clinicopathologically confirmed CRC, and data was gathered from January 2007 to April 2010. Radiomic features were extracted from portal venous-phase computed tomography (CT) of CRC. Lasso regression model was used for data dimension reduction, feature selection, and radiomics signature building. Multivariable logistic regression analysis was used to develop the predicting model, we incorporated the radiomics signature, CT-reported LN status, and independent clinicopathologic risk factors, and this was presented with a radiomics nomogram. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. Internal validation was assessed. An independent validation cohort contained 200 consecutive patients from May 2010 to December 2011.
Results: The radiomics signature, which consisted of 24 selected features, was significantly associated with LN status (P < .001 for both primary and validation cohorts). Predictors contained in the individualized prediction nomogram included the radiomics signature, CT-reported LN status, and carcinoembryonic antigen level. Addition of histologic grade to the nomogram failed to show incremental prognostic value. The model showed good discrimination, with a C-index of 0.736 (C-index, 0.759 and 0.766 through internal validation), and good calibration. Application of the nomogram in the validation cohort still gave good discrimination (C-index, 0.778 [95% CI, 0.769 to 0.787]) and good calibration. Decision curve analysis demonstrated that the radiomics nomogram was clinically useful.
Conclusion: This study presents a radiomics nomogram that incorporates the radiomics signature, CT-reported LN status, and clinical risk factors, which can be conveniently used to facilitate the preoperative individualized prediction of LN metastasis in patients with CRC.
