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.

翻译：人体组织及其组成细胞形成的微环境本质上是三维的。然而，病理诊断的金标准仍是选取少量二维切片进行显微镜评估，这存在采样偏差和误诊风险。尽管已开发出多种获取三维组织形态的方法，但这些方法尚未有效转化为临床实践——对如此庞大的三维数据进行人工或计算评估迄今仍不切实际，且/或无法提供患者层面的临床见解。本文提出模态无关的体块分析多实例学习（MAMBA）平台——一种基于深度学习的框架，可处理来自不同成像模态的三维组织图像并预测患者预后。我们采用开顶式光片显微镜或微型计算机断层扫描对存档的前列腺癌样本进行成像，并利用所得三维数据集通过MAMBA训练基于5年生化学复发结局的风险分层网络。基于三维体块的方法中，MAMBA的受试者工作特征曲线下面积（AUC）分别达0.86和0.74，优于传统基于单张二维切片的预后模型（AUC为0.79和0.57），表明三维形态特征具有更优的预后能力。进一步分析揭示，纳入更大组织体积可提升预后性能并降低采样偏差带来的风险预测变异，这印证了捕获更大范围异质性三维形态的价值。随着三维空间生物学和病理学技术被研究人员和临床医生快速推广采用，MAMBA为三维弱监督学习提供了通用且高效的临床决策支持框架，并有助于揭示新型三维形态生物标志物用于预后及疗效评估。