arXiv 2307.149072023-07-27eess.IVcs.CVq-bio.QM

用于高效分析3D病理样本的弱监督AI

Weakly Supervised AI for Efficient Analysis of 3D Pathology Samples

Harvard Medical School（哈佛医学院）；Massachusetts General Hospital（麻省总医院）；Broad Institute of Harvard and MIT（哈佛与麻省理工学院博德研究所）；Dana-Farber Cancer Institute（丹娜法伯癌症研究院）；Harvard-MIT Division of Health Sciences and Technology（哈佛-麻省理工学院健康科学与技术分部）；Massachusetts Institute of Technology（麻省理工学院）；University of Washington（华盛顿大学）；Johns Hopkins Hospital（约翰霍普金斯医院）；The Ohio State University（俄亥俄州立大学）

Andrew H. Song, Mane Williams, Drew F. K. Williamson, Guillaume Jaume, Andrew Zhang, Bowen Chen, Robert Serafin, Jonathan T. C. Liu, Alex Baras, Anil V. Parwani, Faisal Mahmood

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AI总结：

提出弱监督深度学习平台MAMBA，通过处理多模态3D组织图像预测患者预后，实验表明其基于3D块的方法优于2D切片，有效降低采样偏差并提升预后性能。

摘要：

人体组织及其组成的细胞形成了一个本质上三维（3D）的微环境。然而，病理诊断中的标准护理方法涉及选取少量二维（2D）切片进行显微镜下评估，这存在采样偏差和误诊的风险。目前已开发出多种捕获3D组织形态的方法，但它们在临床实践中的转化仍然很少；对如此庞大的3D数据进行手动和计算评估迄今为止是不切实际的，且/或无法提供患者层面的临床见解。在此，我们提出了模态不可知的体积块分析多实例学习（MAMBA），这是一个基于深度学习的平台，用于处理来自不同成像模态的3D组织图像并预测患者预后。使用开放拓扑光片显微镜或微计算机断层扫描对存档的前列腺癌标本进行成像，并通过MAMBA利用生成的3D数据集训练基于5年生化复发结果的风险分层网络。采用基于3D块的方法，MAMBA实现了0.86和0.74的受试者操作特征曲线下面积（AUC），优于基于2D传统单切片的预后预测（AUC为0.79和0.57），表明3D形态特征具有更优的预后预测能力。进一步分析表明，纳入更大的组织体积可提高预后性能，并减轻采样偏差带来的风险预测变异性，这说明了捕获更大范围异质性3D形态的价值。随着研究人员和临床医生对3D空间生物学和病理学技术的快速发展和采用，MAMBA为临床决策支持的3D弱监督学习提供了一个通用且高效的框架，并有助于揭示用于预后和治疗反应的新型3D形态生物标志物。

英文摘要：

Human tissue and its constituent cells form a microenvironment that is fundamentally three-dimensional (3D). However, the standard-of-care in pathologic diagnosis involves selecting a few two-dimensional (2D) sections for microscopic evaluation, risking sampling bias and misdiagnosis. Diverse methods for capturing 3D tissue morphologies have been developed, but they have yet had little translation to clinical practice; manual and computational evaluations of such large 3D data have so far been impractical and/or unable to provide patient-level clinical insights. Here we present Modality-Agnostic Multiple instance learning for volumetric Block Analysis (MAMBA), a deep-learning-based platform for processing 3D tissue images from diverse imaging modalities and predicting patient outcomes. Archived prostate cancer specimens were imaged with open-top light-sheet microscopy or microcomputed tomography and the resulting 3D datasets were used to train risk-stratification networks based on 5-year biochemical recurrence outcomes via MAMBA. With the 3D block-based approach, MAMBA achieves an area under the receiver operating characteristic curve (AUC) of 0.86 and 0.74, superior to 2D traditional single-slice-based prognostication (AUC of 0.79 and 0.57), suggesting superior prognostication with 3D morphological features. Further analyses reveal that the incorporation of greater tissue volume improves prognostic performance and mitigates risk prediction variability from sampling bias, suggesting the value of capturing larger extents of heterogeneous 3D morphology. With the rapid growth and adoption of 3D spatial biology and pathology techniques by researchers and clinicians, MAMBA provides a general and efficient framework for 3D weakly supervised learning for clinical decision support and can help to reveal novel 3D morphological biomarkers for prognosis and therapeutic response.

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