Application of BOLD-MRI-based radiomics in differentiating malignant from benign renal tumors

Blood oxygen level dependent magnetic resonance imaging (BOLD-MRI) is a kind of non-invasive MRI technology which reflects the tissue blood oxyen levels. This stuy aims to explore the value of radiomics based on BOLD-MRI in differentiating malignant from benign renal tumors.

A total of 141 patients with renal tumors confirmed by pathology were retrospectively analyzed. Seventy-four men and sixty-seven women, aged 26-78 years, with a median age of 56, were included. In all patients, 118 with malignant tumors and 23 with benign tumors were confirmed. All the patients underwent renal T₁ weighted imaging (T₁WI), T₂ weighted imaging (T₂WI), and BOLD-MRI scan within 2 weeks before surgery. The patients were randomly assigned into a training group (benign, n=17; malignant, n=83) and a test group (benign, n=6; malignant, n=35). Two radiologists (A and B), who were blind to the pathological results, delineated the regions of interest (ROI) on the maximum axial slices of the tumors. Radiologist B delineated the ROI again at an interval of one month. The intra-class correlation coefficient (ICC) was used to evaluate inter-observer and intra-observer repeatability and ICC>0.75 represented as a good consistency. All the T₂* Mapping images and the related ROI files were loaded into the Artificial Intelligence Kit software. A total of 396 texture features, which were calculated based on morphology, histogram, gray level co-occurrence matrix, gray-scale run length matrix, gray-scale area size matrix and gray-scale dependent matrix, were extracted from each ROI. The lowest redundancy and the highest correlation were filtered using minimum redundancy maximum relevance (mRMR) algorithm. Then least absolute shrinkage and selection operator (LASSO) algorithm was used to screened out the most predictive features. Multivariate logistic regression was performed to develop the prediction model after feature selection. The radiomics signature score (Radscore) of each case was calculated. The Wilcoxon test was used to compare the difference in the Radscore between benign and malignant renal tumors in the training and test groups. The diagnostic performance of the model in differentiating malignant from benign renal tumors was evaluated with receiver operating characteristic (ROC) curve and leave group out cross validation. The clinical application value of the model was evaluated by decision curve analysis (DCA).

There was significant difference in the age between the patients with benign and those with malignant tumors (t=4.383, P<0.001). There were no significant differences in gender composition and in the largest tumor diameter between the 2 groups (χ²=3.452, P=0.063; t=1.432, P=0.154). The ICC values of all the texture features for the inter-observer repeatability were ranged from 0.71 to 0.87, and the ICC values for the intra-observer repeatability were ranged from 0.76 to 0.91. Thirty features with the lowest redundancy and the highest correlation were screened out. The most predictive 12 features were filtered out. The Radscores of malignant tumors in the training and test groups were higher than those of benign tumors (P<0.001 and P=0.006, respectively). The areas under the ROC curve of the model developed by multivariable logistic regression for differentiating malignant from benign renal tumors in the training and test groups were 0.881 and 0.706, with the accuracy at 82.93% and 79.00%, the sensitivity at 82.86% and 77.11%, and the specificities at 83.33% and 88.24%, respectively. The results of decision curve analysis showed that the net benefit of the radiomics model was higher than that of "all malignant" or "all benign" when the threshold was higher than 0.3.

BOLD-MRI-based radiomics can be a reliable non-invasive approach for differentiating renal malignant tumors from benign tumors.