Abstract 2469: Enhancing pathology workflows with chemical imaging and AI-driven generative models

Abstract Histopathology has been a cornerstone of biomedical tissue assessment for decades, involving a labor-intensive and multi-step process that includes biopsy, gross examination, sampling, and subsequent tissue processing into snap-frozen or formalin-fixed paraffin-embedded (FFPE) blocks. These blocks are then sectioned, stained, and examined microscopically to diagnose and grade tissues. Despite its effectiveness, the traditional workflow is time-consuming, resource-intensive, and reliant on chemical dyes and expert interpretation. Recently, innovative chemical imaging techniques, such as Stimulated Raman Scattering (SRS) microscopy, have begun to redefine this field by enabling direct measurement of the molecular composition of tissues, thereby bypassing conventional tissue processing and staining protocols. This study demonstrates the integration of SRS microscopy into pathology workflows to rapidly acquire high-resolution chemical data from minimally processed fresh-frozen tissues. Unlike traditional methods that rely on thin-sectioned slides, this approach collects data from intact thick tissues using "optical sectioning, " enabling the generation of three-dimensional datasets across multiple planes. These chemical images are processed through a generative adversarial network (GAN) pipeline to produce virtual hematoxylin and eosin (H&E) images that are visually and diagnostically comparable to traditionally stained specimens. Furthermore, the generative model is extended to simulate the morphology and staining patterns of FFPE-processed tissues, offering a versatile tool for creating archival-quality images. This method significantly enhances diagnostic capabilities by preserving lipids and small molecules that are often lost during conventional processing. Within minutes, it generates high-quality stained images that demonstrated high inter-pathologist agreement in tissue grading, proving diagnostically reliable for clinical use. We evaluate our approach through a comprehensive survey of pathologists, assessing its diagnostic utility and reliability. Our results demonstrate that the generated images are comparable in quality to real histological images, showing no discernible inferiority in diagnostic utility. In prostate pathology, the integration of SRS microscopy and AI significantly streamlines traditional workflows, reducing complexity and processing time while preserving diagnostic precision. Citation Format: Kianoush Falahkheirkhah, Sudipta Mukherjee, John Cheville, Rohit Bhargava. Enhancing pathology workflows with chemical imaging and AI-driven generative models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2469.