Rodrigues Ana Carolina, de Almeida José Guilherme, Rodrigues Nuno, Moreno Raquel, Castro Verde Ana Sofia, Mascarenhas Gaivão Ana, Bilreiro Carlos, Santiago Inês, Ip Joana, Belião Sara, Silva Sara, Domingues Inês, Tsiknakis Manolis, Marias Konstantinos, Regge Daniele, Papanikolaou Nikolaos
Champalimaud Research, Champalimaud Foundation, Computational Clinical Imaging, Av. Brasília, Doca de Pedrouços, Lisboa, Lisbon, PT 1400-038, Portugal.
Faculty of Medicine, University of Porto, Porto, Portugal.
Radiol Imaging Cancer. 2025 Sep;7(5):e240507. doi: 10.1148/rycan.240507.
Purpose To develop and prospectively validate a clinical and radiologic model to predict clinically significant prostate cancer (csPCa) using biparametric MRI (bpMRI). Materials and Methods Retrospective data (acquired before March 31, 2022) from 12 medical centers were collected. Radiomic features were extracted from the whole prostate gland using segmentations generated by an automatic deep learning algorithm. A model incorporating bpMRI radiomics, age, prostate-specific antigens, the Prostate Imaging Reporting and Data System (PI-RADS), and the prostate zone lesion location was trained. A retrospective validation set and prospective data (acquired after March 31, 2022) were used to compare PI-RADS scoring (area under the receiver operating characteristic curve [AUC] and specificity at PI-RADS >3). Sensitivity analyses for sequence (T2-weighted, apparent diffusion coefficient, diffusion-weighted imaging) and scanner vendor (GE, Philips, Siemens) were performed, in addition to fairness analyses for relevant categories. Results The retrospective dataset for model development included 7157 male patients (mean age, 64.78 years; 3342 [46.7%] with csPCa), and the prospective dataset for model validation included 1629 patients (mean age, 66.19 years; 592 [36.3%] with csPCa). The multimodal model outperformed PI-RADS in the retrospective (AUC, 0.88 vs 0.80, = .005; specificity of 71% vs 58%, = .002) and prospective validation sets (AUC, 0.91 vs 0.85, < .001; specificity of 77% vs 66%, < .001), leading to 22.7% fewer biopsies compared with PI-RADS. Sensitivity analyses showed the importance of multiple sequences and vendors in achieving model generalization, as using specific sequences or vendors alone led to worse performance. Fairness analysis showed generalizability across different categories but highlighted increased sensitivity with higher PI-RADS and reduced performance in one medical center. Conclusion A multimodal model provided a temporally generalizable predictor of csPCa that outperformed PI-RADS. Algorithm Development, Machine Learning, Model Validation, Model Training, Genital/Reproductive, Neoplasms-Primary, Oncology, Comparative Studies, Technology Assessment © RSNA, 2025.
目的 开发并前瞻性验证一种临床和放射学模型，以利用双参数磁共振成像（bpMRI）预测临床显著前列腺癌（csPCa）。材料与方法 收集了12个医学中心的回顾性数据（2022年3月31日前获取）。使用自动深度学习算法生成的分割结果从整个前列腺中提取影像组学特征。训练了一个包含bpMRI影像组学、年龄、前列腺特异性抗原、前列腺影像报告和数据系统（PI-RADS）以及前列腺区域病变位置的模型。使用回顾性验证集和前瞻性数据（2022年3月31日后获取）比较PI-RADS评分（受试者操作特征曲线下面积[AUC]以及PI-RADS>3时的特异性）。除了对相关类别进行公平性分析外，还对序列（T2加权、表观扩散系数、扩散加权成像）和扫描仪供应商（GE、飞利浦、西门子）进行了敏感性分析。结果 用于模型开发的回顾性数据集包括7157例男性患者（平均年龄64.78岁；3342例[46.7%]患有csPCa），用于模型验证的前瞻性数据集包括1629例患者（平均年龄66.19岁；592例[36.3%]患有csPCa）。在回顾性验证集（AUC，0.88对0.80，P = .005；特异性71%对58%，P = .002）和前瞻性验证集中，多模态模型的表现均优于PI-RADS（AUC，0.91对0.85，P < .001；特异性77%对66%，P < .001），与PI-RADS相比，活检次数减少了22.7%。敏感性分析表明多个序列和供应商对于实现模型泛化的重要性，因为单独使用特定序列或供应商会导致性能更差。公平性分析表明该模型在不同类别中具有可推广性，但突出显示了PI-RADS越高敏感性越高，以及在一个医学中心性能有所下降。结论 多模态模型提供了一个在时间上具有可推广性的csPCa预测指标，其表现优于PI-RADS。算法开发、机器学习、模型验证、模型训练、生殖系统、原发性肿瘤、肿瘤学、对比研究、技术评估 © RSNA，2025年
