TIA Centre Seminar Series: Dr. Andrew Song
Full Title: AI-driven efficient patient prognosis based on 3D pathology samples
Abstract: Human tissue forms 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, with minimal success in translation to clinical practice; manual and computational evaluations of such large 3D data have 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 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 superior prediction performance to 2D traditional single-slice-based prognostication, 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, MAMBA provides a general and efficient framework for 3D pathology for clinical decision support and can help to reveal novel 3D morphological biomarkers for prognosis and therapeutic response.