ArticlePatterns (New York, N.Y.)2022
Obtaining spatially resolved tumor purity maps using deep multiple instance learning in a pan-cancer study.
Article in Patterns (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 17 citations in OpenAlex.
- Distinct prognostic value of [Communications medicine · 2026Article
- Is there a minimum percentage of sarcomatoid component required to affect outcomes of localised renal cell carcinoma?BJU international · 2025Article
- Tumour purity assessment with deep learning in colorectal cancer and impact on molecular analysis.The Journal of pathology · 2025Article
- A weakly supervised deep learning framework for automated PD-L1 expression analysis in lung cancer.Frontiers in immunology · 2025Article
- Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review.Journal of pathology informatics · 2024Article
- Artificial intelligence-augmented histopathologic review using image analysis to optimize DNA yield from formalin-fixed paraffin-embedded slides.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2022Article
- Oner, Sung, and Lee: Researchers in digital pathology for the future of modern medicine.Patterns (New York, N.Y.) · 2022Article
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Authors and funding
15 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tumor purity is the percentage of cancer cells within a tissue section. Pathologists estimate tumor purity to select samples for genomic analysis by manually reading hematoxylin-eosin (H&E)-stained slides, which is tedious, time consuming, and prone to inter-observer variability. Besides, pathologists' estimates do not correlate well with genomic tumor purity values, which are inferred from genomic data and accepted as accurate for downstream analysis. We developed a deep multiple instance learning model predicting tumor purity from H&E-stained digital histopathology slides. Our model successfully predicted tumor purity in eight The Cancer Genome Atlas (TCGA) cohorts and a local Singapore cohort. The predictions were highly consistent with genomic tumor purity values. Thus, our model can be utilized to select samples for genomic analysis, which will help reduce pathologists' workload and decrease inter-observer variability. Furthermore, our model provided tumor purity maps showing the spatial variation within sections. They can help better understand the tumor microenvironment.
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