Background: Currently, prostate cancer (PCa) prebiopsy medical image diagnosis mainly relies on mpMRI and PI-RADS scores. However, PI-RADS has its limitations, such as inter- and intra-radiologist variability and the potential for imperceptible features. The primary objective of this study is to evaluate the effectiveness of a machine learning model based on radiomics analysis of MRI T2-weighted (T2w) images for predicting PCa in prebiopsy cases. Method: A retrospective analysis was conducted using 820 lesions (363 cases, 457 controls) from The Cancer Imaging Archive (TCIA) Database for model development and validation. An additional 83 lesions (30 cases, 53 controls) from Hong Kong Queen Mary Hospital were used for independent external validation. The MRI T2w images were preprocessed, and radiomic features were extracted. Feature selection was performed using Cross Validation Least Angle Regression (CV-LARS). Using three different machine learning algorithms, a total of 18 prediction models and 3 shape control models were developed. The performance of the models, including the area under the curve (AUC) and diagnostic values such as sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were compared to the PI-RADS scoring system for both internal and external validation. Results: All the models showed significant differences compared to the shape control model (allp< 0.001, except SVM model PI-RADS+2 Featuresp= 0.004, SVM model PI-RADS+3 Featuresp= 0.002). In internal validation, the best model, based on the LR algorithm, incorporated 3 radiomic features (AUC = 0.838, sensitivity = 76.85%, specificity = 77.36%). In external validation, the LR (3 features) model outperformed PI-RADS in predictive value with AUC 0.870 vs. 0.658, sensitivity 56.67% vs. 46.67%, specificity 92.45% vs. 84.91%, PPV 80.95% vs. 63.64%, and NPV 79.03% vs. 73.77%. Conclusions: The machine learning model based on radiomics analysis of MRI T2w images, along with simulated biopsy, provides additional diagnostic value to the PI-RADS scoring system in predicting PCa.
背景：目前前列腺癌（PCa）活检前的医学影像诊断主要依赖于多参数磁共振成像（mpMRI）和PI-RADS评分系统。然而，PI-RADS存在一定局限性，例如放射科医师间及医师自身判读的差异性，以及可能遗漏某些细微特征。本研究的主要目的是评估基于MRI T2加权（T2w）影像组学分析的机器学习模型在预测活检前前列腺癌病例中的有效性。方法：研究回顾性分析了来自癌症影像档案（TCIA）数据库的820个病灶（363例阳性，457例阴性）用于模型开发与验证，并采用香港玛丽医院的83个病灶（30例阳性，53例阴性）进行独立外部验证。对MRI T2w图像进行预处理并提取影像组学特征，使用交叉验证最小角回归（CV-LARS）进行特征选择。通过三种不同机器学习算法共构建了18个预测模型和3个形态学对照模型。在内部与外部验证中，将模型性能（包括曲线下面积（AUC）及敏感性、特异性、阳性预测值（PPV）和阴性预测值（NPV）等诊断指标）与PI-RADS评分系统进行比较。结果：所有模型与形态学对照模型均存在显著差异（除SVM模型PI-RADS+2特征p=0.004、SVM模型PI-RADS+3特征p=0.002外，其余均p<0.001）。在内部验证中，基于逻辑回归（LR）算法整合3个影像组学特征的最佳模型表现最优（AUC=0.838，敏感性=76.85%，特异性=77.36%）。在外部验证中，LR（3特征）模型的预测价值全面优于PI-RADS系统：AUC（0.870 vs. 0.658）、敏感性（56.67% vs. 46.67%）、特异性（92.45% vs. 84.91%）、PPV（80.95% vs. 63.64%）和NPV（79.03% vs. 73.77%）。结论：基于MRI T2w影像组学分析的机器学习模型结合模拟活检，能为PI-RADS评分系统预测前列腺癌提供额外的诊断价值。
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