Prostate Cancer Risk Stratification via Nondestructive 3D Pathology with Deep Learning-Assisted Gland Analysis.
Prostate Cancer Risk Stratification via Nondestructive 3D Pathology with Deep Learning-Assisted Gland Analysis.
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DOI：
10.1158/0008-5472.can-21-2843
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发表时间：
2022-01-15
期刊：
影响因子：
11.2
通讯作者：
Liu JTC
中科院分区：
文献类型：
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作者：
Xie W;Reder NP;Koyuncu C;Leo P;Hawley S;Huang H;Mao C;Postupna N;Kang S;Serafin R;Gao G;Han Q;Bishop KW;Barner LA;Fu P;Wright JL;Keene CD;Vaughan JC;Janowczyk A;Glaser AK;Madabhushi A;True LD;Liu JTC
用于对整个前列腺活检进行深度学习辅助计算3D组织学分析的端到端管道表明，无损3D病理学有可能实现前列腺癌患者的上级预后分层。前列腺癌治疗计划在很大程度上取决于芯针活检的检查。前列腺的显微结构构成病理学家进行预后分级的基础。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，这导致了患者的治疗不足和过度。为了改善风险评估和治疗决策，我们开发了一种工作流程，用于对整个前列腺活检进行无损3D病理学和计算分析，该活检用标准苏木精和伊红（H&E）染色的快速廉价荧光类似物进行标记。这种分析是基于可解释的腺体功能，并促进了三维图像增强辅助分割（ITAS3D）的发展。ITAS3D是一种可推广的基于深度学习的策略，使组织微观结构能够以无注释和客观（基于生物标记物）的方式进行体积分割，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个存档的根治性乳腺癌切除术标本中提取的300个离体活检组织进行了成像，其中118个活检组织含有癌症。根据临床生化复发结果，对于低至中危前列腺癌患者的风险分层，癌症活检中的3D腺体特征上级于相应的2D特征。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。用于对整个前列腺活检进行深度学习辅助计算3D组织学分析的端到端管道表明，无损3D病理学有可能实现前列腺癌患者的上级预后分层。
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. 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. 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.