Role of Progesterone Receptor Level in Predicting Axillary Lymph Node Metastasis in Clinical T1-T2N0 Luminal Type Breast Cancer

<i>Background and Objectives</i>: Axillary lymph node metastasis and the number of metastatic lymph nodes are important prognostic factors which are directly related to overall survival in women with breast cancer. Several factors have been identified to predict the likelihood of axillary lymph node metastasis in early-stage breast cancer. High PR expression is often more prevalent in the luminal A subgroup, which is associated with a better prognosis. The aim of this study was to determine the relationship between the percentage of PR expression and the likelihood of axillary metastasis in Her-2-negative, clinical T1-T2N0 luminal type breast cancer. <i>Materials and Methods</i>: A hundred and ninety-nine cases with luminal type, Her-2-negative, clinically and radiologically axilla-negative T1-T2 breast cancer who received radiotherapy were evaluated retrospectively. The pathological specimens were assessed by an experienced pathologist. <i>Results:</i> The statistical evaluation showed that tumor diameter greater than 2 cm, (<i>p</i> = 0.003), presence of lymphovascular invasion (<i>p</i> = 0.001), and PR expression level below 80% (<i>p</i> = 0.037) were identified as significant predictors of lymph node positivity in breast cancer patients. <i>Conclusions:</i> Percentage of progesterone receptor expression along with other molecular biological markers and clinicopathological parameters should be evaluated altogether when predicting axillary metastasis risk before surgery.

Authors

Institutions

Publication Details

Journal
Medicina
Published
2025-04-12
DOI
https://doi.org/10.3390/medicina61040710
Primary Topic
Breast Cancer Treatment Studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Role of Progesterone Receptor Level in Predicting Axillary Lymph Node Metastasis in Clinical T1-T2N0 Luminal Type Breast Cancer

Canan Kelten Talu, Ferhat Eyiler, Mihriban Erdogan, Gönül Tekkurşun Demir et al.
Medicina
Breast Cancer Treatment Studies
article

Role of Progesterone Receptor Level in Predicting Axillary Lymph Node Metastasis in Clinical T1-T2N0 Luminal Type Breast Cancer

Canan Kelten Talu, Ferhat Eyiler, Mihriban Erdogan, Gönül Tekkurşun Demir, Zeliha Güzelöz, Seval Akay, Erdal Yı́lmaz, Olçun Ümit Ünal
article en

Abstract

<i>Background and Objectives</i>: Axillary lymph node metastasis and the number of metastatic lymph nodes are important prognostic factors which are directly related to overall survival in women with breast cancer. Several factors have been identified to predict the likelihood of axillary lymph node metastasis in early-stage breast cancer. High PR expression is often more prevalent in the luminal A subgroup, which is associated with a better prognosis. The aim of this study was to determine the relationship between the percentage of PR expression and the likelihood of axillary metastasis in Her-2-negative, clinical T1-T2N0 luminal type breast cancer. <i>Materials and Methods</i>: A hundred and ninety-nine cases with luminal type, Her-2-negative, clinically and radiologically axilla-negative T1-T2 breast cancer who received radiotherapy were evaluated retrospectively. The pathological specimens were assessed by an experienced pathologist. <i>Results:</i> The statistical evaluation showed that tumor diameter greater than 2 cm, (<i>p</i> = 0.003), presence of lymphovascular invasion (<i>p</i> = 0.001), and PR expression level below 80% (<i>p</i> = 0.037) were identified as significant predictors of lymph node positivity in breast cancer patients. <i>Conclusions:</i> Percentage of progesterone receptor expression along with other molecular biological markers and clinicopathological parameters should be evaluated altogether when predicting axillary metastasis risk before surgery.

MedicinaVol. 61(4)
Izmir University (TR), Sağlık Bilimleri Üniversitesi (TR)
Good health and well-being
Openalex Percentile: Top 91%
Breast Cancer Treatment Studies
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.