Prostate cancer treatment planning is largely dependent upon examination of core-needle biopsies. The microscopic architecture of the prostate glands forms the basis for prognostic grading by pathologists. Interpretation of these convoluted three-dimensional (3D) glandular structures via visual inspection of a limited number of two-dimensional (2D) histology sections is often unreliable, which contributes to the under- and overtreatment of patients. To improve risk assessment and treatment decisions, we have developed a workflow for nondestructive 3D pathology and computational analysis of whole prostate biopsies labeled with a rapid and inexpensive fluorescent analogue of standard hematoxylin and eosin (H&E) staining. This analysis is based on interpretable glandular features and is facilitated by the development of image translation–assisted segmentation in 3D (ITAS3D). ITAS3D is a generalizable deep learning–based strategy that enables tissue microstructures to be volumetrically segmented in an annotation-free and objective (biomarker-based) manner without requiring immunolabeling. As a preliminary demonstration of the translational value of a computational 3D versus a computational 2D pathology approach, we imaged 300 ex vivo biopsies extracted from 50 archived radical prostatectomy specimens, of which, 118 biopsies contained cancer. The 3D glandular features in cancer biopsies were superior to corresponding 2D features for risk stratification of patients with low- to intermediate-risk prostate cancer based on their clinical biochemical recurrence outcomes. The results of this study support the use of computational 3D pathology for guiding the clinical management of prostate cancer. Significance: An end-to-end pipeline for deep learning–assisted computational 3D histology analysis of whole prostate biopsies shows that nondestructive 3D pathology has the potential to enable superior prognostic stratification of patients with prostate cancer.

中文翻译：

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通过非破坏性 3D 病理学和深度学习辅助腺体分析进行前列腺癌风险分层

前列腺癌的治疗计划很大程度上取决于芯针活检的检查。前列腺的微观结构构成了病理学家预后分级的基础。通过目视检查有限数量的二维 (2D) 组织学切片来解释这些复杂的三维 (3D) 腺体结构通常是不可靠的，这会导致患者治疗不足和过度。为了改进风险评估和治疗决策，我们开发了一个工作流程，用于对整个前列腺活检进行无损 3D 病理学和计算分析，并用快速且廉价的标准苏木精和伊红 (H&E) 染色的荧光类似物进行标记。该分析基于可解释的腺体特征，并通过 3D 图像翻译辅助分割 (ITAS3D) 的发展得到促进。ITAS3D 是一种基于深度学习的通用策略，能够以无注释和客观（基于生物标记）的方式对组织微观结构进行体积分割，而无需免疫标记。作为计算 3D 与计算 2D 病理学方法的转化价值的初步证明，我们对从 50 个存档的根治性前列腺切除术标本中提取的 300 个离体活检样本进行了成像，其中 118 个活检样本含有癌症。癌症活检中的 3D 腺体特征优于相应的 2D 特征，可根据临床生化复发结果对低至中风险前列腺癌患者进行风险分层。这项研究的结果支持使用计算 3D 病理学来指导前列腺癌的临床管理。意义：用于全前列腺活检的深度学习辅助计算 3D 组织学分析的端到端流程表明，无损 3D 病理学有可能对前列腺癌患者进行更好的预后分层。