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Multiparametric MRI Radiomic Model for Preoperative Predicting WHO / ISUP Nuclear Grade of Clear Cell Renal Cell Carcinoma
Author(s) -
Li Qiong,
Liu Yujia,
Dong Di,
Bai Xu,
Huang Qingbo,
Guo Aitao,
Ye Huiyi,
Tian Jie,
Wang Haiyi
Publication year - 2020
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.27182
Subject(s) - medicine , receiver operating characteristic , renal cell carcinoma , nuclear medicine , clear cell renal cell carcinoma , lasso (programming language) , radiology , radiomics , logistic regression , pathology , computer science , world wide web
Background Nuclear grade is of importance for treatment selection and prognosis in patients with clear cell renal cell carcinoma (ccRCC). Purpose To develop and validate an MRI‐based radiomic model for preoperative predicting WHO/ISUP nuclear grade in ccRCC. Study Type Retrospective. Population In all, 379 patients with histologically confirmed ccRCC. Training cohort ( n = 252) and validation cohort ( n = 127) were randomly assigned. Field Strength/Sequence Pretreatment 3.0T renal MRI. Imaging sequences were fat‐suppressed T 2 WI, contrast‐enhanced T 1 WI, and diffusion weighted imaging. Assessment Three prediction models were developed using selected radiomic features, radiomic and clinicoradiologic characteristics, and a model containing only clinicoradiologic characteristics. Receiver operating characteristic (ROC) curves and area under the curve (AUC) were used to assess the predictive performance of these models in predicting high‐grade ccRCC. Statistical Tests The least absolute shrinkage and selection operator (LASSO) and minimum redundancy maximum relevance (mRMR) method were used for the selection of radiomic features and clinicoradiologic characteristics, respectively. Multivariable logistic regression analysis was used to develop the radiomic signature of radiomic features and clinicoradiologic model of clinicoradiologic characteristics. Results The radiomic signature showed good performance in discriminating high‐grade (grades 3 and 4) from low‐grade (grades 1 and 2) ccRCC, with sensitivity, specificity, and AUC of 77.3%, 80.0%, and 0.842, respectively, in the validation cohort. The radiomic model, combining radiomic signature and clinicoradiologic characteristics, displayed good predictive ability for high‐grade with sensitivity, specificity, and accuracy of 63.6%, 93.3%, and 88.2%, respectively, in the validation cohort. The radiomic model showed a significantly better performance than the clinicoradiologic model ( P < 0.05). Data Conclusion Multiparametric MRI‐based radiomic model can predict WHO/ISUP grade in patients with ccRCC with satisfying performance, and thus could help the physician to improve treatment decisions. Level of Evidence 3 Technical Efficacy Stage 2