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PD-L1 and PD-1 Expression in Early Stage Uterine Endometrioid Carcinomaopen access

Authors
An, Hyo JungYang, Jung WookKim, Min HyeSong, Dae Hyun
Issue Date
Jan-2024
Publisher
INT INST ANTICANCER RESEARCH
Keywords
endometrioid carcinoma; machine learning; PD1; PDL1
Citation
In vivo (Athens, Greece), v.38, no.1, pp 246 - 252
Pages
7
Indexed
SCOPUS
Journal Title
In vivo (Athens, Greece)
Volume
38
Number
1
Start Page
246
End Page
252
URI
https://scholarworks.gnu.ac.kr/handle/sw.gnu/69194
DOI
10.21873/invivo.13431
ISSN
0258-851X
1791-7549
Abstract
BACKGROUND/AIM: Immune checkpoint inhibitors (ICI) and tumor-infiltrating lymphocytes (TILs) for cancer treatment in clinical oncology have revolutionized patient care. However, no gold standard exists for the criteria of analytical validity of TILs of different types of cancer. MATERIALS AND METHODS: Clinicopathological data from 60 patients with endometrioid carcinoma (EC) who had undergone surgical treatment at the Gyeongsang National University Hospital between January 2002 and December 2009, were investigated. The programmed cell death protein 1 (PD-1)/programmed cell death ligand 1 (PDL1) expression levels were characterized by immunohistochemical staining patterns, and the interpretations derived from machine learning morphometric analysis (Genie) and the pathologists' assessments were compared. In solid tumors, pathologists assessed the proportion of positive cells in each core of the tissue microarray. For Genie, the proportion of positive cells in the entire core and the number of positive cells per 1 mm2 were used. RESULTS: Both the pathologists and Genie identified the same trend in association with tumor size, with significant differences (p=0.026, p=0.033). Genie expression showed a significant association with PD1 expression, and pathologists identified a significant association with PDL1 expression in immune cells. CONCLUSION: The PD1 expression levels identified in immune cells of EC specimens were similar between the pathologists and Genie, suggesting that there is little resistance to the introduction of morphometric analysis. To our knowledge, this is the first study to introduce and validate machine learning as an integrated method for predicting prognosis and treatment based on PD1 expression in EC tumor microenvironments. Copyright © 2024, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.
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