Khosravi Pegah, Saikali Shady, Alipour Abolfazl, Mohammadi Saber, Boger Maxwell, Diallo Dalanda M, Smith Christopher J, Moschovas Marcio C, Hajirasouliha Iman, Hung Andrew J, Venkataraman Srirama S, Patel Vipul
Department of Biological Sciences, New York City College of Technology (City Tech), City University of New York (CUNY), Brooklyn, NY 11201, United States.
PhD Programs in Biology and Computer Science, The Graduate Center, City University of New York (CUNY), New York, NY 10016, United States.
Biol Methods Protoc. 2025 Apr 26;10(1):bpaf032. doi: 10.1093/biomethods/bpaf032. eCollection 2025.
Preoperative identification of extracapsular extension (ECE) in prostate cancer (PCa) is crucial for effective treatment planning, as ECE presence significantly increases the risk of positive surgical margins and early biochemical recurrence following radical prostatectomy. AutoRadAI, an innovative artificial intelligence (AI) framework, was developed to address this clinical challenge while demonstrating broader potential for diverse medical imaging applications. The framework integrates T2-weighted MRI data with histopathology annotations, leveraging a dual convolutional neural network (multi-CNN) architecture. AutoRadAI comprises two key components: ProSliceFinder, which isolates prostate-relevant MRI slices, and ExCapNet, which evaluates ECE likelihood at the patient level. The system was trained and validated on a dataset of 1001 patients (510 ECE-positive, 491 ECE-negative cases). ProSliceFinder achieved an area under the ROC curve (AUC) of 0.92 (95% confidence interval [CI]: 0.89-0.94) for slice classification, while ExCapNet demonstrated robust performance with an AUC of 0.88 (95% CI: 0.83-0.92) for patient-level ECE detection. Additionally, AutoRadAI's modular design ensures scalability and adaptability for applications beyond ECE detection. Validated through a user-friendly web-based interface for seamless clinical integration, AutoRadAI highlights the potential of AI-driven solutions in precision oncology. This framework improves diagnostic accuracy and streamlines preoperative staging, offering transformative applications in PCa management and beyond.
术前识别前列腺癌（PCa）的包膜外扩展（ECE）对于有效的治疗规划至关重要，因为存在ECE会显著增加根治性前列腺切除术后手术切缘阳性和早期生化复发的风险。AutoRadAI是一种创新的人工智能（AI）框架，旨在应对这一临床挑战，同时展示其在多种医学成像应用中的更广泛潜力。该框架将T2加权MRI数据与组织病理学注释相结合，利用双卷积神经网络（multi-CNN）架构。AutoRadAI包括两个关键组件：ProSliceFinder，用于分离与前列腺相关的MRI切片；ExCapNet，用于在患者层面评估ECE可能性。该系统在一个包含1001例患者（510例ECE阳性、491例ECE阴性病例）的数据集上进行了训练和验证。ProSliceFinder在切片分类方面的ROC曲线下面积（AUC）为0.92（95%置信区间[CI]：0.89 - 0.94），而ExCapNet在患者层面的ECE检测中表现出色，AUC为0.88（95%CI：0.83 - 0.92）。此外，AutoRadAI的模块化设计确保了其在ECE检测之外的应用中的可扩展性和适应性。通过用户友好的基于网络的界面进行验证以实现无缝临床整合，AutoRadAI突出了人工智能驱动的解决方案在精准肿瘤学中的潜力。该框架提高了诊断准确性并简化了术前分期，在PCa管理及其他领域提供了变革性应用。
