A combined clinical-ultrasound radiomics model for differentiating benign and malignant BI-RADS category 4 breast masses

The integration of ultrasound radiomic features with clinical data improves diagnostic performance in differentiating benign from malignant BI-RADS 4 breast masses. The combined model holds potential for aiding clinical decision-making but requires further validation in larger, independent datasets.

Authors

Institutions

Publication Details

Journal
American Journal of Translational Research
Published
2025-01-01
DOI
https://doi.org/10.62347/sbku2090
Primary Topic
MRI in cancer diagnosis
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A combined clinical-ultrasound radiomics model for differentiating benign and malignant BI-RADS category 4 breast masses

Qing Zhang
American Journal of Translational Research
MRI in cancer diagnosis
article

A combined clinical-ultrasound radiomics model for differentiating benign and malignant BI-RADS category 4 breast masses

Qing Zhang
article en

Abstract

The integration of ultrasound radiomic features with clinical data improves diagnostic performance in differentiating benign from malignant BI-RADS 4 breast masses. The combined model holds potential for aiding clinical decision-making but requires further validation in larger, independent datasets.

American Journal of Translational ResearchVol. 17(8)
Jiangsu University (CN), Affiliated Hospital of Jiangsu University (CN)
Government of Jiangsu Province
Openalex Percentile: Top 68%
MRI in cancer diagnosis
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.