Prediction of nuclear grade of renal clear cell carcinoma based on MRI texture analysis in combination with imaging features
10.3760/cma.j.cn112149-20200712-00912
- VernacularTitle:基于MRI纹理分析预测肾透明细胞癌核分级
- Author:
Yu ZHANG
;
Xinyuan CHEN
;
Ning XU
;
Dairong CAO
;
Qunlin CHEN
- From:
Chinese Journal of Radiology
2021;55(1):53-58
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the application value of MRI texture analysis in combination with imaging features to predict the WHO/International Society of Urological Pathology (ISUP) nuclear grading in pre-operative patients with clear cell renal carcinoma (ccRCC).Methods:MRI images of 78 patients diagnosed as ccRCC by surgical pathology from July 2016 to July 2020 in First Affiliated Hospital of Fujian Medical University were retrospectively analyzed. According to the WHO/ISUP grading system, the patients were divided into low grade group (49 cases, grade Ⅰ in 2 cases and grade Ⅱ in 47 cases) and high grade group (29 cases, grade Ⅲ in 25 cases and grade Ⅳ in 4 cases), and then were assigned to training set ( n= 63) and validation set ( n=15) in a ratio of 7∶3 using random indicator method. MRI radiological features were evaluated and MRI imaging texture features were extracted. The largest-diameter slice of lesion on cross-sectional images was selected and ROIs were drawn on T 2WI and corticomedullary phase (CMP) images, respectively. Quantitative texture analysis software MaZda was used to extract texture features, including gray-scale histogram, co-occurrence matrix, run-length matrix, gradient, autoregressive model and wavelet transform. The extracted texture features were preliminarily selected by the combination of Fisher, probability of classification errorand average correlation coefficient, and interaction information, and then the reduced texture parameters or imaging features were tested by the independent sample t test, Mann-Whitney U test or χ 2 test. Parameters with statistically significant differences were used to construct a multi-factors binary logistic regression model and the ROC curve was used to analyze its effectiveness in predicting high grade ccRCC. Results:In training set, there were significant differences intumor length, shape and margin, enhancement degree of CMP, vein thrombosis and 47 texture features between the low and high grade ccRCC groups. In the training set, 7 multi-factors binary logistic regression model were constructed, including radiological features model (M1), T 2WI texture features model (M2), CMP image texture features model (M3) and combination radiological features of T 2WI texture features model (M4), combination radiological features of CMP images texture features model (M5), combination T 2WI texture features of CMP images texture features model (M6) and combination of all features model (M7). The area under ROC curve of M7 in predicting nuclear grading of ccRCC was the largest, which were 0.901 (95% CI 0.828-0.974) and 0.820 (95% CI 0.564-0.974) in the training set and validation set, respectively. Conclusion:MRI texture analysis combined with imaging features is hopeful to be an effective preoperative noninvasive method in predicting WHO/ISUP grading of ccRCC.
