文献类型：期刊文献

英文题名：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

作者：Zhang, Ke[1,2];Ren, Yiyue[3,4];Xu, Shufeng[2,5];Lu, Wei[2,6];Xie, Shengnan[2];Qu, Jiali[2];Wang, Xiaoyan[2];Shen, Bo[2,7];Pang, Peipei[8];Cai, Xiujun[3,4];Sun, Jihong[1,2]

机构：[1]Shaoxing Univ, Sch Med, Shaoxing, Peoples R China;[2]Zhejiang Univ, Sir Run Run Shaw Hosp, Sch Med, Dept Radiol, Hangzhou, Peoples R China;[3]Zhejiang Univ, Sir Run Run Shaw Hosp, Sch Med, Dept Gen Surg, Hangzhou, Peoples R China;[4]Zhejiang Univ, Key Lab Endoscop Tech Res Zhejiang Prov, Hangzhou, Peoples R China;[5]Wenzhou Med Univ, Peoples Hosp Quzhou, Dept Radiol, Quzhou Hosp, Quzhou, Peoples R China;[6]Univ Chinese Acad Sci, Hwa Mei Hosp, Dept Radiol, Ningbo, Peoples R China;[7]Zhejiang Univ, Affiliated Cent Hosp Huzhou Univ, Huzhou Cent Hosp, Affiliated Huzhou Hosp,Dept Radiol,Sch Med, Huzhou, Peoples R China;[8]GE Healthcare, Dept Pharmaceut Diag, Hangzhou, Peoples R China

年份：2021

卷号：48

期号：9

起止页码：4872

外文期刊名：MEDICAL PHYSICS

收录：SCI-EXPANDED(收录号：WOS:000675122000001)、、EI(收录号：20213010674617)、Scopus(收录号：2-s2.0-85110977120)、WOS

基金：This study was supported by the National Natural Science Foundation of China (81871403), Key Research and Development Program of Zhejiang Province (2019C03014), and the Fundamental Research Funds for the Central Universities.

语种：英文

外文关键词：clinical-radiomics model; colorectal cancer; diagnostic performance; lymphovascular invasion; perineural invasion

外文摘要：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.