Mukherjee, Sudipta S.

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.

Histopathology has remained a cornerstone for biomedical tissue assessment for over a century, with a resource-intensive workflow involving biopsy or excision, gross examination, sampling, tissue processing to snap frozen or formalin-fixed paraffin-embedded blocks, sectioning, staining, optical imaging, and microscopic assessment. Emerging chemical imaging approaches, including stimulated Raman scattering (SRS) microscopy, can directly measure inherent molecular composition in tissue (thereby dispensing with the need for tissue processing, sectioning, and using dyes) and can use artificial intelligence (AI) algorithms to provide high-quality images. Here we show the integration of SRS microscopy in a pathology workflow to rapidly record chemical information from minimally processed fresh-frozen prostate tissue. Instead of using thin sections, we record data from intact thick tissues and use optical sectioning to generate images from multiple planes. We use a deep learning–based processing pipeline to generate virtual hematoxylin and eosin images. Next, we extend the computational method to generate archival-quality images in minutes, which are equivalent to those obtained from hours/days-long formalin-fixed, paraffin-embedded processing. We assessed the quality of images from the perspective of enabling pathologists to make decisions, demonstrating that the virtual stained image quality was diagnostically useful and the interpathologist agreement on prostate cancer grade was not impacted. Finally, because this method does not wash away lipids and small molecules, we assessed the utility of lipid chemical composition in determining grade. Together, the combination of chemical imaging and AI provides novel capabilities for rapid assessments in pathology by reducing the complexity and burden of current workflows. Significance: Archival-quality (formalin-fixed paraffin-embedded), thin-section diagnostic images are obtained from thick-cut, fresh-frozen prostate tissues without dyes or stains to expedite cancer histopathology by combining SRS microscopy and machine learning.