Purpose: The Ki67 index and the Gleason grade group (GGG) are vital prognostic indicators of prostate cancer (PCa). This study investigated the value of biparametric magnetic resonance imaging (bpMRI) radiomics feature-based machine learning (ML) models in predicting the Ki67 index and GGG of PCa. Methods: A total of 122 patients with pathologically proven PCa who had undergone preoperative MRI were retrospectively included. Radiomics features were extracted from T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps. Then, recursive feature elimination (RFE) was applied to remove redundant features. ML models for predicting Ki67 expression and GGG were constructed based on bpMRI and different algorithms, including logistic regression (LR), support vector machine (SVM), random forest (RF), and K-nearest neighbor (KNN). The performances of different models were evaluated with receiver operating characteristic (ROC) analysis. In addition, a joint analysis of Ki67 expression and GGG was performed by assessing their Spearman correlation and calculating the diagnostic accuracy for both indices. Results: The ML model based on LR and ADC + T2 (LR_ADC + T2, AUC = 0.8882) performed best in predicting Ki67 expression, and ADC_wavelet-LHH_firstorder_Maximum had the highest feature weighting. The SVM_DWI + T2 (AUC = 0.9248) performed best in predicting GGG, and DWI_wavelet HLL_glcm_SumAverage had the highest feature weighting. The Ki67 and GGG exhibited a weak positive correlation (r= 0.382,p< 0.001), and LR_ADC + DWI had the highest diagnostic accuracy in predicting both (0.6230). Conclusion: The proposed ML models are suitable for predicting both Ki67 expression and GGG in PCa. This algorithm could be used to identify indolent or invasive PCa with a noninvasive, repeatable, and accurate diagnostic method.
目的：Ki67指数与格里森分级组（GGG）是前列腺癌（PCa）的重要预后指标。本研究探讨了基于双参数磁共振成像（bpMRI）影像组学特征的机器学习（ML）模型在预测PCa Ki67指数及GGG中的价值。方法：回顾性纳入122例经病理证实且术前接受MRI检查的PCa患者。从T2加权成像（T2WI）、扩散加权成像（DWI）及表观扩散系数（ADC）图中提取影像组学特征，并采用递归特征消除法（RFE）剔除冗余特征。基于bpMRI及不同算法（包括逻辑回归（LR）、支持向量机（SVM）、随机森林（RF）和K近邻（KNN））构建预测Ki67表达与GGG的ML模型。通过受试者工作特征（ROC）分析评估不同模型的性能。此外，通过评估Spearman相关性及计算两项指标的诊断准确率，对Ki67表达与GGG进行联合分析。结果：基于LR及ADC+T2的ML模型（LR_ADC+T2，AUC=0.8882）在预测Ki67表达中表现最佳，其中ADC_wavelet-LHH_firstorder_Maximum的特征权重最高。SVM_DWI+T2模型（AUC=0.9248）在预测GGG中表现最优，DWI_wavelet HLL_glcm_SumAverage的特征权重最高。Ki67与GGG呈弱正相关（r=0.382，p<0.001），而LR_ADC+DWI模型对两者的联合诊断准确率最高（0.6230）。结论：本研究提出的ML模型适用于预测PCa的Ki67表达与GGG。该算法可通过无创、可重复且准确的诊断方法，用于鉴别惰性与侵袭性PCa。
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