Potential biomarkers for predicting the efficacy of a pembrolizumab-containing regimen in advanced cervical cancer: A real-world analysis

CPS and TPS showed a modest correlation with progression-free survival and NLR prior to immunotherapy demonstrated the best treatment efficacy for advanced cervical cancer.

Authors

Institutions

Publication Details

Journal
Journal of Turkish Society of Obstetric and Gynecology
Published
2025-07-01
DOI
https://doi.org/10.4274/tjod.galenos.2025.93607
Primary Topic
Endometrial and Cervical Cancer Treatments
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Potential biomarkers for predicting the efficacy of a pembrolizumab-containing regimen in advanced cervical cancer: A real-world analysis

Shintaro Yanazume, Shinichi Togami, Akihide Tanimoto, Ikumi Kitazono et al.
Journal of Turkish Society of Obstetric and Gynecology
Endometrial and Cervical Cancer Treatments
article

Potential biomarkers for predicting the efficacy of a pembrolizumab-containing regimen in advanced cervical cancer: A real-world analysis

Shintaro Yanazume, Shinichi Togami, Akihide Tanimoto, Ikumi Kitazono, Hiroaki Kobayashi
article en

Abstract

CPS and TPS showed a modest correlation with progression-free survival and NLR prior to immunotherapy demonstrated the best treatment efficacy for advanced cervical cancer.

Journal of Turkish Society of Obstetric and GynecologyVol. 22(3)
Kagoshima University (JP), Kagoshima University Hospital (JP)
Openalex Percentile: Top 79%
Endometrial and Cervical Cancer Treatments
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.