Dataset Information

Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes.

ABSTRACT: Computational methods have made substantial progress in improving the accuracy and throughput of pathology workflows for diagnostic, prognostic, and genomic prediction. Still, lack of interpretability remains a significant barrier to clinical integration. We present an approach for predicting clinically-relevant molecular phenotypes from whole-slide histopathology images using human-interpretable image features (HIFs). Our method leverages >1.6 million annotations from board-certified pathologists across >5700 samples to train deep learning models for cell and tissue classification that can exhaustively map whole-slide images at two and four micron-resolution. Cell- and tissue-type model outputs are combined into 607 HIFs that quantify specific and biologically-relevant characteristics across five cancer types. We demonstrate that these HIFs correlate with well-known markers of the tumor microenvironment and can predict diverse molecular signatures (AUROC 0.601-0.864), including expression of four immune checkpoint proteins and homologous recombination deficiency, with performance comparable to 'black-box' methods. Our HIF-based approach provides a comprehensive, quantitative, and interpretable window into the composition and spatial architecture of the tumor microenvironment.

SUBMITTER: Diao JA

PROVIDER: S-EPMC7955068 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes.

Diao James A JA Wang Jason K JK Chui Wan Fung WF Mountain Victoria V Gullapally Sai Chowdary SC Srinivasan Ramprakash R Mitchell Richard N RN Glass Benjamin B Hoffman Sara S Rao Sudha K SK Maheshwari Chirag C Lahiri Abhik A Prakash Aaditya A McLoughlin Ryan R Kerner Jennifer K JK Resnick Murray B MB Montalto Michael C MC Khosla Aditya A Wapinski Ilan N IN Beck Andrew H AH Elliott Hunter L HL Taylor-Weiner Amaro A

Nature communications 20210312 1

Computational methods have made substantial progress in improving the accuracy and throughput of pathology workflows for diagnostic, prognostic, and genomic prediction. Still, lack of interpretability remains a significant barrier to clinical integration. We present an approach for predicting clinically-relevant molecular phenotypes from whole-slide histopathology images using human-interpretable image features (HIFs). Our method leverages >1.6 million annotations from board-certified pathologis ...[more]

PMID: 33712588

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Project description:PURPOSE:Digital pathology (DP), referring to the digitization of tissue slides, is beginning to change the landscape of clinical diagnostic workflows and has engendered active research within the area of computational pathology. One of the challenges in DP is the presence of artefacts and batch effects, unintentionally introduced during both routine slide preparation (eg, staining, tissue folding) and digitization (eg, blurriness, variations in contrast and hue). Manual review of glass and digital slides is laborious, qualitative, and subject to intra- and inter-reader variability. Therefore, there is a critical need for a reproducible automated approach of precisely localizing artefacts to identify slides that need to be reproduced or regions that should be avoided during computational analysis. METHODS:Here we present HistoQC, a tool for rapidly performing quality control to not only identify and delineate artefacts but also discover cohort-level outliers (eg, slides stained darker or lighter than others in the cohort). This open-source tool employs a combination of image metrics (eg, color histograms, brightness, contrast), features (eg, edge detectors), and supervised classifiers (eg, pen detection) to identify artefact-free regions on digitized slides. These regions and metrics are presented to the user via an interactive graphical user interface, facilitating artefact detection through real-time visualization and filtering. These same metrics afford users the opportunity to explicitly define acceptable tolerances for their workflows. RESULTS:The output of HistoQC on 450 slides from The Cancer Genome Atlas was reviewed by two pathologists and found to be suitable for computational analysis more than 95% of the time. CONCLUSION:These results suggest that HistoQC could provide an automated, quantifiable, quality control process for identifying artefacts and measuring slide quality, in turn helping to improve both the repeatability and robustness of DP workflows.

Dataset Information

Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes.

Publications

Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes.

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