Conventional pathology workflows rely on two-dimensional, slide-based analysis of thin tissue sections. This approach comes with several key limitations including limited sampling, lack of 3D structural information, and destruction of valuable clinical specimens. There is growing interest in nondestructive 3D pathology to address these shortcomings. Existing work has mainly focused on small-scale proof-of-concept studies, due in part to the difficulty of producing consistent, high-quality 3D pathology datasets across hundreds to thousands of specimens. To facilitate large-scale clinical studies, we present an end-to-end workflow for 3D pathology, with an emphasis on data consistency and quality control.
Margin status directly correlates with patient survival in many types of cancer. To improve the identification of positive margins, we report a rapid surgical guidance workflow including tissue staining, open-top light-sheet (OTLS) microscopy imaging, and post-processing to generate en face histologic images of fresh tissue surfaces. Compared with conventional frozen section analysis (FSA) in which a few vertical sections of tissue are sampled, our technology could comprehensively image large margin surfaces in a non-destructive manner and achieve superior image quality. We provide examples showing that the image quality generated by our rapid surface-histology method approaches that of gold-standard archival histology.
Esophageal adenocarcinoma (EAC), which can arise from Barrett’s esophagus (BE), has a 5-year survival rate of < 20%. Unfortunately, the severe sampling limitations associated with conventional histology may limit the sensitivity for detecting EAC and dysplasia (a precursor lesion to EAC) through regular endoscopic screening of BE patients. We have developed a non-destructive 3D pathology workflow to provide comprehensive evaluation of whole biopsies and a deep learning-based computational triage method that automatically segments potentially neoplastic regions (dysplasia or EAC) to guide pathologist review. A preliminary clinical validation study shows that our AI-assisted 3D workflow enables neoplasia to be identified with higher sensitivity on a per-biopsy level than conventional slide-based 2D histology.
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