Description: Histomorphological Phenotype Learning (HPL) was developed as a self-supervised methodology to discriminatory features in microscopy images to aid in cancer diagnosis and management. The methodology partitions whole slide images (WSIs) into meaningful Histomorphological Phenotype Clusters (HPCs) that can be used to define and quantify morphological phenotypes which recur within and between cases. HPL employs the following approach: (1) WSI pre-processing, (2) self-supervised learning of tissue tiles, (3) tissue tile representation clustering into Histomorphological Phenotype Clusters (HPCs), and (4) HPC characterization. This process has been applied to images from 276 patients at NYU Langone and a multi-cancer cohort of patients from The Cancer Genome Atlas (TCGA) to identify and study histomorphological phenotypes associated with lung adenocarcinoma (LUAD). HPL was able to identify LUAD types and growth patterns that are comparable to the World Health Organization (WHO) classification, correlate with patient survival (mean concordance-index for recurrence-free survival= 0.74), and align with transcriptomic measures of immunophenotype.

Subject of Study

Subject Domain

Cancer

Chronic Disease

Population Age

Adult (19 years - 64 years)

Senior (65 years - 79 years)

Aged (80 years and over)

Keywords

Computer-Assisted Diagnosis

Computer-Assisted Image Interpretation

Deep Learning

Diagnosis

Diagnostic Imaging

Diagnostic Techniques and Procedures

Gene Expression Profiling

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Restrictions: Free to All

Author Approval Required
Instructions: Source data for manuscript figures are included with the publication in Nature Communications. Code and data (i.e., pre-trained LUAD/LUSC model checkpoints, multi-cancer model checkpoints; tile vector representations; HPC configurations; whole slide image and patient vector representations; and jupyter notebook) are available through the GitHub and Zenodo repositories. The project utilized slide images for 10 cancer types (IDs: TCGA-LUAD, TCGA-LUSC, TCGA-BLCA, TCGA-BRCA, TCGA-CESC, TCGA-COAD, TCGA-PRAD, TCGA-SKCM, TCGA-STAD) from the Genomic Data Commons (GDC). Requests for data from the NYU patient cohort can be submitted to the corresponding author and will be subject to a data transfer agreement.; Access via GitHub
Code and data

Access via Zenodo
Code and data
Accession #: 10718821

Access via Nature Communications
Source data

Access via Genomic Data Commons
Data portal
Accession #: TCGA-LUAD, TCGA-LUSC, TCGA-BLCA, TCGA-BRCA, TCGA-CESC, TCGA-COAD, TCGA-PRAD, TCGA-SKCM, TCGA-STAD

Associated Publications

Claudio Quiros A, Coudray N, Yeaton A, Yang X, Liu B, Le H, Chiriboga L, Karimkhan A, Narula N, Moore DA, Park CY, Pass H, Moreira AL, Le Quesne J, Tsirigos A, Yuan K. Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unannotated pathology slides. Nat Commun. 2024 Jun 11;15(1):4596.

Coudray N, Ocampo PS, Sakellaropoulos T, Narula N, Snuderl M, Fenyö D, Moreira AL, Razavian N, Tsirigos A. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat Med. 2018 Oct;24(10):1559-1567.

Moreira AL, Ocampo PSS, Xia Y, Zhong H, Russell PA, Minami Y, Cooper WA, Yoshida A, Bubendorf L, Papotti M, Pelosi G, Lopez-Rios F, Kunitoki K, Ferrari-Light D, Sholl LM, Beasley MB, Borczuk A, Botling J, Brambilla E, Chen G, Chou TY, Chung JH, Dacic S, Jain D, Hirsch FR, Hwang D, Lantuejoul S, Lin D, Longshore JW, Motoi N, Noguchi M, Poleri C, Rekhtman N, Tsao MS, Thunnissen E, Travis WD, Yatabe Y, Roden AC, Daigneault JB, Wistuba II, Kerr KM, Pass H, Nicholson AG, Mino-Kenudson M. A Grading System for Inv

Data Type

Biospecimens

Imaging

Software Used

DeepPATH

Study Type

Observational

Grant Support: P30 CA016087/NCI NIH

EP/R018634/1/Engineering and Physical Sciences Research Council (EPSRC)

BB/V016067/1/Engineering and Physical Sciences Research Council (EPSRC)

2019-06360/Swedish Research Council

NYU Data Catalog

Mapping the Landscape of Histomorphological Cancer Phenotypes Using Self-Supervised Learning on Unannotated Pathology Slides

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