Prediction of Fuhrman grade of renal clear cell carcinoma by multimodal MRI radiomics: a retrospective study

AIM

To explore the value of multimodal magnetic resonance imaging (MRI) radiomics combined with traditional radiologist-defined semantic characteristics and conventional (cMRI) and functional MRI (fMRI) texture features in predicting Fuhrman grade of clear cell renal cell carcinoma (ccRCC).

MATERIALS AND METHODS

The data of 89 patients with histopathologically proven ccRCC (low-grade, 54; high-grade, 35) were collected. Texture features were extracted from cMRI (T1- and T2-weighted imaging) and fMRI (Dixon-MRI; blood-oxygen-level dependent [BOLD]-MRI; and susceptibility-weighted imaging [SWI]) images, and the traditional characteristics (TC) were evaluated. Logistic regression analysis was performed to develop models based on TC, cMRI, and fMRI texture features for grading. Receiver operating characteristic (ROC) curve analysis and leave-group-out cross-validation (LGOCV) were performed to test the reliability of combined models.

RESULTS

Two T2-weighted imaging-based, two Dixon_W-based, one Dixon_F-based, one BOLD-based, and three SWI-based texture features, and three TC were extracted for feature selection. TC, cMRI, fMRI, cMRI+fMRI, cMRI+TC, fMRI+TC, and cMRI+fMRI+TC models were constructed. The AUC of the cMRI+fMRI+TC model for differentiating high- from low-grade ccRCC was 0.74, with 81.42% accuracy, 75.93% sensitivity, and 91.43% specificity. The fMRI+TC model exhibited a performance similar to that of the cMRI+fMRI+TC model (p>0.05). The areas under the curve (AUCs) of the fMRI+TC and cMRI+fMRI+TC models were significantly higher than those of the other five models (all p<0.05). For the cMRI+fMRI+TC model, the mean accuracy was 85.40% after 100 LGOCV for the test sets.

CONCLUSION

Multimodal MRI radiomics combined with TC, cMRI, and fMRI texture features may be a reliable quantitative approach for differentiating high-grade ccRCC from low-grade ccRCC.