  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.
