A clinical‐radiomics model incorporating T2‐weighted and diffusion‐weighted magnetic resonance images predicts the existence of lymphovascular invasion / perineural invasion in patients with colorectal cancer. Issue 9 (21st July 2021)

• Record Type: Journal Article
• Authors: Zhang, Ke; Ren, Yiyue; Xu, Shufeng; Lu, Wei; Xie, Shengnan; Qu, Jiali; Wang, Xiaoyan; Shen, Bo; Pang, Peipei; Cai, Xiujun; Sun, Jihong
• Is Part Of: Medical physics. Volume 48:Issue 9(2021)
• Page Start: 4872
• Page End: 4882
• Publication Date: 2021-07-21
• Subjects: clinical‐radiomics model -- colorectal cancer -- diagnostic performance -- lymphovascular invasion -- perineural invasion; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.15001
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: Lymphovascular invasion (LVI) and perineural invasion (PNI) are independent prognostic factors in patients with colorectal cancer (CRC). In this study, we aimed to develop and validate a preoperative predictive model based on high‐throughput radiomic features and clinical factors for accurate prediction of LVI/PNI in these patients. Methods: Two hundred and sixty‐three patients who underwent colorectal resection for histologically confirmed CRC between 1 February 2011 and 30 June 2020 were retrospectively enrolled. Between 1 February 2011 and 30 September 2018, 213 patients were randomly divided into a training cohort (n = 149) and a validation cohort (n = 64) by a ratio of 7:3. We used a 10000‐iteration bootstrap analysis to estimate the prediction error and confidence interval for two cohorts. The independent test cohort consisted of 50 patients between 1 October 2018 and 30 June 2020. Regions of interest (ROIs) were manually delineated in high‐resolution T2‐weighted and diffusion‐weighted images using ITK‐SNAP software on each CRC tumor slice. In total, 3356 radiomic features were extracted from each ROI. Next, we used the maximum relevance minimum redundancy and least absolute shrinkage and selection operator algorithms to select the strongest of these features to establish a clinical‐radiomics model for predicting LVI/PNI. Receiver‐operating characteristic and calibration curves were then plotted to evaluate the predictive performance of the model in the training, validation, and independent test cohorts. Results: A multiparametric clinical‐radiomics model combining MRI‐reported extramural vascular invasion (EMVI) status and a Radiomics score for the LVI/PNI estimation was established. This model had significant predictive power in the training cohort (area under the curve [AUC] 0.91; 95% confidence interval [CI]: 0.85–0.97), validation cohort (AUC: 0.88; 95% CI: 0.79–89), and independent test cohorts (AUC 0.83, 95% CI 0.72–0.95). The model performed well in the independent test cohort with sensitivity of 0.818, specificity of 0.714, and accuracy of 0.760. Calibration curve and decision curve analysis demonstrated clinical benefits. Conclusion: Multiparametric clinical‐radiomics models can accurately predict LVI/PNI in patients with CRC. Our model has predictive ability that should improve preoperative diagnostic performance and allow more individualized treatment decisions.