Predictors of High-Burden Residual Axillary Disease After Neoadjuvant Therapy in Breast Cancer

Identifying patients at risk of high-burden residual axillary disease is essential to optimize neoadjuvant strategies. Increasing axillary pathological complete response may reduce the need for ALND, minimizing surgical morbidity without compromising oncological outcomes.

Authors

Institutions

Publication Details

Journal
Cancers
Published
2025-05-08
DOI
https://doi.org/10.3390/cancers17101596
Citations
1
Primary Topic
Breast Cancer Treatment Studies
Type
article
Field-Weighted Citation Impact
0.94
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Predictors of High-Burden Residual Axillary Disease After Neoadjuvant Therapy in Breast Cancer

Andrea Sagona, Simone Di Maria Grimaldi, E. Barbieri, Damiano Gentile et al.
1 citations
Cancers
Breast Cancer Treatment Studies
0.94
article

Predictors of High-Burden Residual Axillary Disease After Neoadjuvant Therapy in Breast Cancer

Andrea Sagona, Simone Di Maria Grimaldi, E. Barbieri, Damiano Gentile, Corrado Tinterri, Jacopo Canzian
article en
1 citations

Abstract

Identifying patients at risk of high-burden residual axillary disease is essential to optimize neoadjuvant strategies. Increasing axillary pathological complete response may reduce the need for ALND, minimizing surgical morbidity without compromising oncological outcomes.

CancersVol. 17(10)
Humanitas University (IT), IRCCS Humanitas Research Hospital (IT)
Good health and well-being
Openalex Percentile: Top 25%
Breast Cancer Treatment Studies
0.94
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.