Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.patter.2021.100399
Title: Obtaining spatially resolved tumor purity maps using deep multiple instance learning in a pan-cancer study
Authors: Oner, MU
Chen, J 
Revkov, E
James, A
Heng, SY
Kaya, AN
Alvarez, JJS
Takano, A 
Cheng, XM 
Lim, TKH
Tan, DSW 
Zhai, W
Skanderup, AJ 
Sung, WK 
Lee, HK 
Keywords: computational pathology
deep learning
digital histopathology
digital pathology
genomic sequencing
multiple instance learning
spatial omics
tumor microenvironment
tumor purity
whole-slide images
Issue Date: 11-Feb-2022
Publisher: Elsevier BV
Citation: Oner, MU, Chen, J, Revkov, E, James, A, Heng, SY, Kaya, AN, Alvarez, JJS, Takano, A, Cheng, XM, Lim, TKH, Tan, DSW, Zhai, W, Skanderup, AJ, Sung, WK, Lee, HK (2022-02-11). Obtaining spatially resolved tumor purity maps using deep multiple instance learning in a pan-cancer study. Patterns 3 (2) : 100399-. ScholarBank@NUS Repository. https://doi.org/10.1016/j.patter.2021.100399
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.
Source Title: Patterns
URI: https://scholarbank.nus.edu.sg/handle/10635/226614
ISSN: 2666-3899
DOI: 10.1016/j.patter.2021.100399
Appears in Collections:Staff Publications
Elements

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
Obtaining spatially resolved tumor purity maps using deep multiple instance learning in a pan-cancer study.pdf3.85 MBAdobe PDF

OPEN

NoneView/Download

SCOPUSTM   
Citations

1
checked on Oct 1, 2022

Page view(s)

35
checked on Oct 6, 2022

Download(s)

1
checked on Oct 6, 2022

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.