Weakly Supervised AI for Efficient Analysis of 3D Pathology Samples
Andrew H. Song, Mane Williams, Drew F. K. Williamson, Guillaume Jaume, J. Andrew Zhang, Bowen Chen, Robert Serafin, Jonathan Liu, Alex S. Baras, Anil Vasdev Parwani, Faisal Mahmood · PubMed · 2023
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 that there is value in capturing larger extents of spatially 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.