Adequate detection of the histopathological extraprostatic extension (EPE) of prostate cancer (PCa) remains a challenge using conventional radiomics on 3 Tesla multiparametric magnetic resonance imaging (3T mpMRI). This study focuses on the assessment of artificial intelligence (AI)-driven models with innovative MRI radiomics in predicting EPE of prostate cancer (PCa) at a lesion-specific level. With a dataset encompassing 994 lesions from 794 PCa patients who underwent robot-assisted radical prostatectomy (RARP) at two Dutch hospitals, the study establishes and validates three classification models. The models were validated on an internal validation cohort of 162 lesions and an external validation cohort of 189 lesions in terms of discrimination, calibration, net benefit, and comparison to radiology reporting. Notably, the achieved AUCs ranged from 0.86 to 0.91 at the lesion-specific level, demonstrating the superior accuracy of the random forest model over conventional radiological reporting. At the external test cohort, the random forest model was the best-calibrated model and demonstrated a significantly higher accuracy compared to radiological reporting (83% vs. 67%,p= 0.02). In conclusion, an AI-powered model that includes both existing and novel MRI radiomics improves the detection of lesion-specific EPE in prostate cancer.
利用3特斯拉多参数磁共振成像（3T mpMRI）的传统影像组学技术，准确检测前列腺癌（PCa）的组织病理学前列腺外侵犯（EPE）仍具挑战性。本研究聚焦于评估人工智能（AI）驱动模型结合创新MRI影像组学在病灶层面预测前列腺癌EPE的能力。研究基于荷兰两家医院794例接受机器人辅助根治性前列腺切除术（RARP）患者共994个病灶的数据集，构建并验证了三种分类模型。模型在包含162个病灶的内部验证队列和189个病灶的外部验证队列中，从区分度、校准度、净获益及与影像学报告对比等方面进行了验证。值得注意的是，在病灶层面获得的曲线下面积（AUC）范围为0.86至0.91，证明随机森林模型相较于传统影像学报告具有更优的准确性。在外部测试队列中，随机森林模型校准度最佳，其准确率显著高于影像学报告（83% vs. 67%，p=0.02）。综上所述，融合现有及新型MRI影像组学特征的AI模型能够提升前列腺癌病灶特异性EPE的检测效能。
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