Abstract 1449: Label free virtual HE from 3D holotomography enables reliable nuclear and microanatomical readouts in thick cancer tissues.

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Abstract Conventional H&E staining is destructive, 2D-limited, and incompatible with longitudinal or scarce-sample studies. Holotomography (HT) enables label-free 3D refractive index (RI) imaging of thick tissue, but pixel-accurate supervision for virtual staining is unattainable due to sectioning-induced deformation. Building on our recent demonstration of 3D virtual H&E for colon and gastric cancer tissues up to 50 µm thick, we developed a framework that learns hematoxylin- and eosin-like contrast directly from HT volumes using weakly aligned chemical H&E supervision. The method produces stable, interpretable nuclear and stromal features, enabling nuclei-centric quantitative readouts in a fully label-free workflow.FFPE breast cancer tissues were imaged with 3D HT to obtain quantitative RI volumes at subcellular resolution. A diffusion-based image-to-image model was trained with weak HT–H&E alignment and a nucleus-aware auxiliary loss applied only during training. Inference requires HT alone, maintaining a consumable-free, non-destructive pipeline. Fidelity was evaluated by (i) nuclear morphology agreement, (ii) patch- and field-level realism, (iii) smooth z-slice consistency, and (iv) standardized nuclei quantification. Cross-tissue generalizability was assessed using previously validated colon and gastric datasets spanning 10–50 µm thickness.The virtual H&E preserved basophilic nuclear contrast and eosinophilic stromal features, accurately reconstructing glandular, stromal, and muscular boundaries even in thick sections. Nuclear contours remained continuous across axial planes without flicker artifacts. Whole-field nuclear counts showed 97.8% concordance with chemical H&E, and nuclear area/shape metrics showed no significant deviation. Appearance error was notably lower than prior virtual-staining approaches, and expert reviewers confirmed faithful chromatin texture, nucleoli visibility, and nuclear crowding patterns essential for cancer grading. The method remained robust across tissue thicknesses (4–50 µm), organ types, and institutional sources.Virtual H&E generated directly from label-free HT enables high-fidelity, nuclei-driven readouts without staining, sectioning artifacts, or tissue loss. This supports rapid, consumable-free tissue assessment and reproducible quantification of nuclei-centric biomarkers. Its compatibility with thick tissue volumes positions the technology for drug-response profiling, mechanism-of-action studies, tumor microenvironment analysis, and high-content phenotypic screening. Ongoing multi-site validation and development of 3D diffusion models for whole-slide inference aim to establish label-free 3D virtual histopathology as a scalable alternative to conventional H&E for research and translational oncology. Citation Format: Juyeon Park, Su-Jin Shin, Geon Kim, Dongjoo Kim, Hyungjoo Cho, Daewoong Ahn, Ji Eun Heo, Jean R. Clemenceau, Dongmin Ryu, Isabel Barnfather, Minji Kim, Inyeop Jang, Ji-Youn Sung, Jeong Hwan Park, Sanggeun Oh, Hyun-Seok Min, Sumin Lee, Kwang Suk Lee, Nam Hoon Cho, Tae Hyun Hwang, YongKeun Park. Label free virtual HE from 3D holotomography enables reliable nuclear and microanatomical readouts in thick cancer tissues [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1449.

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