Abstract 6178: Automating tumor purity assessment: Advanced AI-driven segmentation and classification of nuclei in H&E stained slides
Authors: Wagner Nikolaus R. , Kikuchi Kaito , Fukuda Kristen , Spasic Nikola , Littlechild Stacy , Todorovic Sinisa , Stojnev Dragana , Jovanovic Masa , Weston Claire 
Published: March 2024

Abstract
Background
Tumor purity, the proportion of cancerous cells within a tumor mass, is a pivotal factor in oncological research and clinical practice and plays a significant role in the molecular testing workflow, particularly when selecting samples for genomic analysis. Accurate assessment of tumor purity is crucial for understanding tumor behavior, guiding treatment decisions, and predicting patient outcomes. Traditional methods of analyzing hematoxylin and eosin (H&E) stained slides for tumor purity are predominantly pathologist manual readouts. This process is both time-consuming and prone to inter- and intra-observer variability, leading to inconsistencies in diagnosis and treatment planning. Here, we introduce an automated AI-based product designed to streamline tumor purity analysis. The RevealAI-HE digital assay leverages state-of-the-art AI algorithms to automate the segmentation and classification of nuclei in H&E slides, enhancing the accuracy, consistency, and speed of tumor purity assessment. RevealAI-HE aims to assist pathologists in decision-making, support oncology research, streamline molecular testing workflows, and improve personalized care.
Methods
H&E stained solid tumor slides were imaged using 3D-Histech Pannoramic 1000 and 250 scanners. From this dataset, 104 regions of interest (ROIs) were manually selected to cover a wide range of H&E stain intensities in 14 different indications. A nuclear segmentation model based on StarDist-H&E was deployed on these ROIs, and a pathology expert manually assigned one of three classifications to each nuclei: tumor, immune, and stroma. The resulting ground-truth dataset of over 26,000 classified nuclei was used for training a classifier model based on a ResNet architecture. For model evaluation, the nuclei dataset was randomly split into training (80%) and testing (20%) the model, and the training data was batched and randomized for each training epoch to ensure robustness and generalizability. The nuclear segmentation, nucleus classifier, and tumor purity statistics models were integrated into an end-to-end digital assay (RevealAI-HE) using Reveal Biosciences’ proprietary digital assay development pipeline. RevealAI-HE is integrated into Reveal Biosciences’ flagship whole slide image management system, ImageDx.
Results
The RevealAI-HE digital assay’s nucleus classification model achieved a high accuracy across all 14 indications. Compared to manual methods, the AI system significantly reduced the time required for tumor purity analysis. Furthermore, the AI model exhibited excellent consistency in its analyses, addressing the issue of variability seen in manual assessments. These results indicate that our AI product, in many aspects, surpasses the current gold standard of tumor purity analysis (pathologist readout), offering a more efficient, accurate, and reproducible approach.
Citation Format: Nikolaus R. Wagner, Kaito Kikuchi, Kristen Fukuda, Nikola Spasic, Stacy Littlechild, Sinisa Todorovic, Dragana Stojnev, Masa Jovanovic, Claire Weston. Automating tumor purity assessment: Advanced AI-driven segmentation and classification of nuclei in H&E stained slides [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6178.