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

作者：Xuhui Fan, Hongwei Yu, Ni Xie, Guihua Chen, Tiewen Li, Jingwen Chen, Meijuan He, Hao Liu, Han Wang, Xiaorui Yin · 发表于：British Journal of Radiology · 年份：2023 · DOI：10.1259/bjr.20221086 · 被引用次数：14 · 研究领域：Bladder and Urothelial Cancer Treatments、Radiomics and Machine Learning in Medical Imaging、Prostate Cancer Diagnosis and Treatment

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 cli...