Multimodal MRI radiomics for preoperative predicting Fuhrman nuclear grade of clear cell renal cell carcinoma
10.3760/cma.j.cn112149-20220516-00439
- VernacularTitle:多模态MRI影像组学术前预测肾透明细胞癌Fuhrman核分级
- Author:
Zhaoyu XING
1
;
Liwen SHEN
;
Liang PAN
;
Jun SUN
;
Jie CHEN
;
Nan SHEN
;
Shengnan YU
;
Wei XING
;
Longjiang ZHANG
Author Information
1. 苏州大学附属第三医院泌尿外科，常州 213003
- Keywords:
Carcinoma, renal cell;
Magnetic resonance imaging;
Radiomics;
Fuhrman nuclear grade
- From:
Chinese Journal of Radiology
2022;56(7):785-791
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the value of multimodal MRI radiomics in the preoperative prediction of Fuhrman nuclear grade of clear cell renal cell carcinoma (ccRCC).Methods:A total of 129 patients with ccRCC confirmed by pathology from April 2011 to April 2021 in Third Affiliated Hospital of Soochow University were collected, and the imaging and clinicopathological data were retrospectively analyzed. All patients were divided into training set ( n=90) and validation set ( n=39) at the ratio of 7∶3 using random indicator method. According to the postoperative pathological results, Fuhrman grades Ⅰ and Ⅱ were included in the low grade group (96 cases, 65 cases in the training set and 31 cases in the validation set), and Fuhrman grades Ⅲ and Ⅳ were included in the high grade group (33 cases, 25 cases in the training set and 8 cases in the validation set). Two radiologists manually delineated regions of interest (ROI) on T 1WI, T 2WI, Dixon-water, Dixon-fat, susceptibility weighted imaging (SWI), blood oxygen level dependent (BOLD) images, and 396 texture features were extracted from each ROI. In the training set, intra-class correlation coefficient, Mann-Whitney U test, minimum redundancy maximum relevance and least absolute shrinkage and selection operator method were used to reduce the dimension of features to obtain the best texture features. The logistic regression was used to develop the multimodal radiomics model, and the receiver operating characteristic (ROC) curve was used to evaluate the effectiveness of the model in identifying high and low-grade ccRCC in training set and validation set. Results:Four SWI, one T 2WI and one BOLD texture features were selected for modeling. The areas under the ROC curve (95%CI) of the multimodal radiomics model for identifying high and low grade ccRCC in the training and validation sets were 0.859 (0.770-0.923) and 0.883 (0.740-0.964), with the specificity at 95.4% and 87.1%, the sensitivity at 68.0% and 87.5%, the accuracy at 87.8% and 87.2%, respectively. Conclusion:The multimodal MRI radiomics model based on T 2WI, SWI and BOLD images has high effectiveness in preoperative predicting Fuhrman nuclear grade of ccRCC.
