Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Clicks: 290
ID: 120348
2018
Free PDF
Article Quality & Performance Metrics
Overall Quality Improving Quality
0.0 /100
Combines engagement data with AI-assessed academic quality
AI Quality Assessment
Not analyzed
Abstract
Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumor-infiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are deriv …
Reference Key
j2018cellspatial Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Saltz J;Gupta R;Hou L;Kurc T;Singh P;Nguyen V;Samaras D;Shroyer KR;Zhao T;Batiste R;Van Arnam J; ;Shmulevich I;Rao AUK;Lazar AJ;Sharma A;Thorsson V;;
Journal Cell reports
Year 2018
DOI
DOI not found
URL
https://www.pubmed.gov/29617659/
Keywords
National Center for Biotechnology Information NCBI NLM MEDLINE humans pubmed abstract nih national institutes of health national library of medicine research support non-u.s. gov't u.s. gov't non-p.h.s. N.I.H. Extramural computer-assisted / methods* image interpretation neoplasms / pathology* pmid:29617659 pmc5943714 doi:10.1016/j.celrep.2018.03.086 joel saltz rajarsi gupta vésteinn thorsson deep learning* lymphocytes tumor-infiltrating / metabolism tumor-infiltrating / pathology*

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.