دورية أكاديمية
أعرض في EDS

Obtaining spatially resolved tumor purity maps using deep multiple instance learning in a pan-cancer study.

التفاصيل البيبلوغرافية
العنوان: Obtaining spatially resolved tumor purity maps using deep multiple instance learning in a pan-cancer study.
المؤلفون: Oner MU; Bioinformatics Institute, Agency for Science, Technology and Research (A∗STAR), Singapore 138671, Singapore.; School of Computing, National University of Singapore, Singapore 117417, Singapore., Chen J; Genome Institute of Singapore, Agency for Science, Technology and Research (A∗STAR), Singapore 138672, Singapore., Revkov E; Genome Institute of Singapore, Agency for Science, Technology and Research (A∗STAR), Singapore 138672, Singapore.; School of Computing, National University of Singapore, Singapore 117417, Singapore., James A; Department of Anatomical Pathology, Singapore General Hospital, Singapore 169608, Singapore., Heng SY; Department of Anatomical Pathology, Singapore General Hospital, Singapore 169608, Singapore., Kaya AN; Genome Institute of Singapore, Agency for Science, Technology and Research (A∗STAR), Singapore 138672, Singapore., Alvarez JJS; Genome Institute of Singapore, Agency for Science, Technology and Research (A∗STAR), Singapore 138672, Singapore.; School of Computing, National University of Singapore, Singapore 117417, Singapore., Takano A; Department of Anatomical Pathology, Singapore General Hospital, Singapore 169608, Singapore., Cheng XM; Department of Anatomical Pathology, Singapore General Hospital, Singapore 169608, Singapore., Lim TKH; Department of Anatomical Pathology, Singapore General Hospital, Singapore 169608, Singapore., Tan DSW; Division of Medical Oncology, National Cancer Centre Singapore, Singapore 169610, Singapore.; Oncology Academic Clinical Programme, Duke-NUS Medical School, Singapore 169857, Singapore.; Genome Institute of Singapore, Agency for Science, Technology and Research (A∗STAR), Singapore 138672, Singapore., Zhai W; Genome Institute of Singapore, Agency for Science, Technology and Research (A∗STAR), Singapore 138672, Singapore.; Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China.; Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China., Skanderup AJ; Genome Institute of Singapore, Agency for Science, Technology and Research (A∗STAR), Singapore 138672, Singapore.; School of Computing, National University of Singapore, Singapore 117417, Singapore.; Division of Medical Oncology, National Cancer Centre Singapore, Singapore 169610, Singapore., Sung WK; School of Computing, National University of Singapore, Singapore 117417, Singapore.; Genome Institute of Singapore, Agency for Science, Technology and Research (A∗STAR), Singapore 138672, Singapore., Lee HK; Bioinformatics Institute, Agency for Science, Technology and Research (A∗STAR), Singapore 138671, Singapore.; School of Computing, National University of Singapore, Singapore 117417, Singapore.; Singapore Eye Research Institute (SERI), Singapore 169856, Singapore.; Image and Pervasive Access Lab (IPAL), Singapore 138632, Singapore.; Rehabilitation Research Institute of Singapore, Singapore 308232, Singapore.; Singapore Institute for Clinical Sciences, Singapore 117609, Singapore.
المصدر: Patterns (New York, N.Y.) [Patterns (N Y)] 2021 Dec 09; Vol. 3 (2), pp. 100399. Date of Electronic Publication: 2021 Dec 09 (Print Publication: 2022).
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Elsevier Inc Country of Publication: United States NLM ID: 101767765 Publication Model: eCollection Cited Medium: Internet ISSN: 2666-3899 (Electronic) Linking ISSN: 26663899 NLM ISO Abbreviation: Patterns (N Y) Subsets: PubMed not MEDLINE
أسماء مطبوعة: Original Publication: [New York] : Elsevier Inc., [2020]-
مستخلص: 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.
Competing Interests: The authors declare no competing interests.
(© 2021 The Authors.)
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فهرسة مساهمة: Keywords: computational pathology; deep learning; digital histopathology; digital pathology; genomic sequencing; multiple instance learning; spatial omics; tumor microenvironment; tumor purity; whole-slide images
تواريخ الأحداث: Date Created: 20220224 Latest Revision: 20220225
رمز التحديث: 20231215
مُعرف محوري في PubMed: PMC8848022
DOI: 10.1016/j.patter.2021.100399
PMID: 35199060
قاعدة البيانات: MEDLINE
الوصف
تدمد:2666-3899
DOI:10.1016/j.patter.2021.100399