Data_Sheet_2_CT-Based Radiomics Signature: A Potential Biomarker for Predicting Postoperative Recurrence Risk in Stage II Colorectal Cancer.DOCX (11.18 kB)
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Data_Sheet_2_CT-Based Radiomics Signature: A Potential Biomarker for Predicting Postoperative Recurrence Risk in Stage II Colorectal Cancer.DOCX

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posted on 19.03.2021, 05:32 authored by Shuxuan Fan, Xiaonan Cui, Chunli Liu, Xubin Li, Lei Zheng, Qian Song, Jin Qi, Wenjuan Ma, Zhaoxiang Ye

Objective: To evaluate whether a radiomics signature could improve stratification of postoperative risk and prediction of chemotherapy benefit in stage II colorectal cancer (CRC) patients.

Material and Methods: This retrospective study enrolled 299 stage II CRC patients from January 2010 to December 2015. Based on preoperative portal venous-phase CT scans, radiomics features were generated and selected to build a radiomics score (Rad-score) using the Least Absolute Shrinkage and Selection Operator (LASSO) method. The minority group was balanced by the synthetic minority over-sampling technique (SMOTE). Predictive models were built with the Rad-score and clinicopathological factors, and the area under the curve (AUC) was used to evaluate their performance. A nomogram was also constructed for predicting 3-year disease-free survival (DFS). The performance of the nomogram was assessed with a concordance index (C-index) and calibration plots.

Results: Overall, 114 features were selected to construct the Rad-score, which was significantly associated with the 3-year DFS. Multivariate analysis demonstrated that the Rad-score, CA724 level, mismatch repair status, and perineural invasion were independent predictors of recurrence. Results showed that the Rad-score can classify patients into high-risk and low-risk groups in the training cohort (AUC 0.886) and the validation cohort (AUC 0.874). On this basis, a nomogram that integrated the Rad-score and clinical variables demonstrated superior performance (AUC 0.954, 0.906) than the clinical model alone (AUC 0.765, 0.705) in the training and validation cohorts, respectively. The C-index of the nomogram was 0.872, and the performance was acceptable.

Conclusion: Our radiomics-based model can reliably predict recurrence risk in stage II CRC patients and potentially provide complementary prognostic value to the traditional clinicopathological risk factors for better identification of patients who are most likely to benefit from adjuvant therapy. The proposed nomogram promises to be an effective tool for personalized postoperative surveillance for stage II CRC patients.

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