Systematic radiomics analysis based on multiparameter MRI to preoperatively predict the expression of Ki67 and histological grade in patients with bladder cancer.

Objectives: Bladder cancer is among the most prevalent urothelial malignancies. Radiomics-based preoperative prediction of Ki67 and histological grade will facilitate clinical decision-making.

Methods: This retrospective study recruited 283 bladder cancer patients between 2012 and 2021. Multiparameter MRI sequences included: T1WI, T2WI, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) imaging. The radiomics features of intratumoral and peritumoral regions were extracted simultaneously. Max-Relevance and Min-Redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) algorithms were employed to select the features. Six machine learning-based classifiers were adopted to construct the radiomics models, and the best was chosen for the model construction.

Results: The mRMR and LASSO algorithms were more suitable for Ki67 and histological grade, respectively. Additionally, Ki67 had a higher proportion of intratumoral features, while peritumoral features accounted for a greater proportion of the histological grade. Random forests performed the best in predicting both pathological outcomes. Consequently, the multiparameter MRI (MP-MRI) models achieved area under the curve (AUC) values of 0.977 and 0.852 for Ki67 in training and test sets, respectively, and 0.972 and 0.710 for the histological grade.

Conclusion: Radiomics holds the potential to predict multiple pathological outcomes of bladder cancer preoperatively and are expected to provide clinical decision-making guidance. Furthermore, our work inspired the process of radiomics research.

Advances In Knowledge: This study demonstrated that different feature selection techniques, segmentation regions, classifiers, and MRI sequences will affect the performance of the model. We systematically demonstrated that radiomics can predict histological grade and Ki67.