Prostate cancer is a dominant health concern calling for advanced diagnostic tools. Utilizing digital pathology and artificial intelligence, this study explores the potential of 11 deep neural network architectures for automated Gleason grading in prostate carcinoma focusing on comparing traditional and recent architectures. A standardized image classification pipeline, based on the AUCMEDI framework, facilitated robust evaluation using an in-house dataset consisting of 34,264 annotated tissue tiles. The results indicated varying sensitivity across architectures, with ConvNeXt demonstrating the strongest performance. Notably, newer architectures achieved superior performance, even though with challenges in differentiating closely related Gleason grades. The ConvNeXt model was capable of learning a balance between complexity and generalizability. Overall, this study lays the groundwork for enhanced Gleason grading systems, potentially improving diagnostic efficiency for prostate cancer.

翻译：前列腺癌是一项主要的健康问题，亟需先进的诊断工具。本研究利用数字病理学和人工智能技术，探讨了11种深度神经网络架构在前列腺癌自动Gleason分级中的潜力，重点比较了传统架构与最新架构。基于AUCMEDI框架构建的标准化图像分类流程，利用包含34,264张已标注组织切片的内部数据集进行了稳健评估。结果表明，不同架构的敏感性存在差异，其中ConvNeXt表现出最强的性能。值得注意的是，尽管在区分紧密相关的Gleason分级方面存在挑战，但较新的架构仍实现了更优越的性能。ConvNeXt模型能够学习在复杂性与泛化能力之间取得平衡。总体而言，本研究为改进Gleason分级系统奠定了基础，有望提升前列腺癌的诊断效率。