Assuring AI/ML-readiness of digital pathology in diverse existing and emerging multi-omic datasets through quality control workflows

Sage Bionetworks, Seattle WA

Investigators

Abstract

Abstract In an era of multi-omics, histology remains an essential approach for basic, translational, and clinical research providing valuable, low-cost, and non-destructive information about tissue morphology. The adoption of whole slide imaging (WSI) and digital pathology (DP) has led to large clinical and research repositories being instantiated for computational data mining of image-based biomarkers associated with genotype, diagnosis, prognosis, and therapy response. Importantly, data quality plays a critical role in the usage of these WSI, especially when employing artificial intelligence (AI) and machine learning (ML) methods. Artifacts and batch effects may arise at many points in the process from biopsy to digitization, and while several tools to detect them have been developed, consistent application and reporting are lacking, with none being routinely applied in public repositories. This leaves a unique opportunity to immediately provide added value to existing and future NIH- supported datasets. This proposal sees a collaboration between Sage Bionetworks, experts in FAIR data sharing and Team Science, and Dr. Andrew Janowczyk, a leader in automated quality control (QC) of WSI who has spearheaded the development of an open-source DP QC tool, HistoQC. We propose to enhance the AI/ML readiness of existing and future DP data by providing transparent, reproducible, reporting of detected imaging artifacts and batch effects within NIH-sponsored datasets in an automated fashion via the extension of our existing QC workflows. Implementing transparent reporting of DP data quality will enable researchers to exclude artifacts from their training sets in a consistent cross-investigator manner. Our work will provide greater trust in dataset reuse and experimental reproducibility while also easing AI/ML model creation and enhancing their performance. We will build on strong preliminary data and prototypes, demonstrating both significantly improved cross-reader QC reproducibility and technical feasibility, with three specific aims. Aim 1 sees this enrichment process will be applied to WSI from NIH-supported public datasets, including TCGA and GTEx, and for NIH/NCI Division of Cancer Biology research programs supported by the Multi-Consortia Coordinating (MC2) Center parent grant. Aim 2 employs the lessons learned from the enhancement of raw DP data to be AI/ML ready in Aim 1 to deploy a scalable workflow for QC of all incoming DP data from MC2-supported programs, providing continual prospective data enrichment to assure AI/ML readiness. Lastly, Aim 3 demonstrates enhanced AI/ML readiness of DP data subjected to our automated QC processes using a prototypical self-supervised tissue classification task. Our deliverables include (a) 5000 WSI annotated by our QC workflow and enhanced into AI/ML ready datasets; (b) workflows to enable processing of incoming datasets for AI-readiness, (c) a failure rate of identifying poor quality slides is <1%; and (d) our QC comparative AI/ML demonstration yields an improvement of >10% performance in terms of tissue classification performance as a result of our data enhancements.