دورية أكاديمية
أعرض في EDS

Using Artificial Intelligence to Identify Tumor Microenvironment Heterogeneity in Non-Small Cell Lung Cancers.

التفاصيل البيبلوغرافية
العنوان: Using Artificial Intelligence to Identify Tumor Microenvironment Heterogeneity in Non-Small Cell Lung Cancers.
المؤلفون: DuCote TJ; Department of Toxicology and Cancer Biology, University of Kentucky, Lexington, Kentucky., Naughton KJ; Department of Toxicology and Cancer Biology, University of Kentucky, Lexington, Kentucky., Skaggs EM; Department of Toxicology and Cancer Biology, University of Kentucky, Lexington, Kentucky., Bocklage TJ; Markey Cancer Center, University of Kentucky, Lexington, Kentucky; Department of Pathology and Laboratory Medicine, University of Kentucky, Lexington, Kentucky., Allison DB; Markey Cancer Center, University of Kentucky, Lexington, Kentucky; Department of Pathology and Laboratory Medicine, University of Kentucky, Lexington, Kentucky., Brainson CF; Department of Toxicology and Cancer Biology, University of Kentucky, Lexington, Kentucky; Markey Cancer Center, University of Kentucky, Lexington, Kentucky. Electronic address: cfbrainson@uky.edu.
المصدر: Laboratory investigation; a journal of technical methods and pathology [Lab Invest] 2023 Aug; Vol. 103 (8), pp. 100176. Date of Electronic Publication: 2023 May 12.
نوع المنشور: Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
اللغة: English
بيانات الدورية: Publisher: Elsevier Inc Country of Publication: United States NLM ID: 0376617 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1530-0307 (Electronic) Linking ISSN: 00236837 NLM ISO Abbreviation: Lab Invest Subsets: MEDLINE
أسماء مطبوعة: Publication: 2023- : [New York] : Elsevier Inc.
Original Publication: Baltimore : Williams & Wilkins
مواضيع طبية MeSH: Carcinoma, Non-Small-Cell Lung*/pathology , Lung Neoplasms*/pathology, Humans ; Animals ; Mice ; Hematoxylin ; Artificial Intelligence ; Tumor Microenvironment ; Eosine Yellowish-(YS)
مستخلص: Lung cancer heterogeneity is a major barrier to effective treatments and encompasses not only the malignant epithelial cell phenotypes and genetics but also the diverse tumor-associated cell types. Current techniques used to investigate the tumor microenvironment can be time-consuming, expensive, complicated to interpret, and often involves destruction of the sample. Here we use standard hematoxylin and eosin-stained tumor sections and the HALO AI nuclear phenotyping software to characterize 6 distinct cell types (epithelial, mesenchymal, macrophage, neutrophil, lymphocyte, and plasma cells) in both murine lung cancer models and human lung cancer samples. CD3 immunohistochemistry and lymph node sections were used to validate lymphocyte calls, while F4/80 immunohistochemistry was used for macrophage validation. Consistent with numerous prior studies, we demonstrated that macrophages predominate the adenocarcinomas, whereas neutrophils predominate the squamous cell carcinomas in murine samples. In human samples, we showed a strong negative correlation between neutrophils and lymphocytes as well as between mesenchymal cells and lymphocytes and that higher percentages of mesenchymal cells correlate with poor prognosis. Taken together, we demonstrate the utility of this AI software to identify, quantify, and compare distributions of cell types on standard hematoxylin and eosin-stained slides. Given the simplicity and cost-effectiveness of this technique, it may be widely beneficial for researchers designing new therapies and clinicians working to select favorable treatments for their patients.
(Copyright © 2023 United States & Canadian Academy of Pathology. Published by Elsevier Inc. All rights reserved.)
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معلومات مُعتمدة: P20 GM121327 United States GM NIGMS NIH HHS; P30 CA177558 United States CA NCI NIH HHS; R01 CA237643 United States CA NCI NIH HHS
فهرسة مساهمة: Keywords: artificial intelligence; non–small cell lung cancer; tumor immunology; tumor microenvironment
المشرفين على المادة: YKM8PY2Z55 (Hematoxylin)
TDQ283MPCW (Eosine Yellowish-(YS))
تواريخ الأحداث: Date Created: 20230514 Date Completed: 20230818 Latest Revision: 20240802
رمز التحديث: 20240802
مُعرف محوري في PubMed: PMC10527157
DOI: 10.1016/j.labinv.2023.100176
PMID: 37182840
قاعدة البيانات: MEDLINE
الوصف
تدمد:1530-0307
DOI:10.1016/j.labinv.2023.100176