Ultrasound radiomics-based machine learning models for risk stratification of follicular thyroid tumors

Integrating peritumoral radiomic features with clinical-ultrasound features using logistic regression enhances the differentiation between benign and malignant follicular thyroid tumors.

Authors

Institutions

Publication Details

Journal
Frontiers in Oncology
Published
2026-01-05
DOI
https://doi.org/10.3389/fonc.2025.1707586
Primary Topic
Thyroid Cancer Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Ultrasound radiomics-based machine learning models for risk stratification of follicular thyroid tumors

X. Wang, F. Richard Yu, Kunpeng Cao, Hongyan Deng et al.
Frontiers in Oncology
Thyroid Cancer Diagnosis and Treatment
article

Ultrasound radiomics-based machine learning models for risk stratification of follicular thyroid tumors

X. Wang, F. Richard Yu, Kunpeng Cao, Hongyan Deng, Ya Yuan
article en

Abstract

Integrating peritumoral radiomic features with clinical-ultrasound features using logistic regression enhances the differentiation between benign and malignant follicular thyroid tumors.

Frontiers in OncologyVol. 15
Shanghai Medical College of Fudan University (CN), Nanjing Medical University (CN)
Openalex Percentile: Top 97%
Thyroid Cancer Diagnosis and Treatment
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.

Ultrasound radiomics-based machine learning models for risk stratification of follicular thyroid tumors — X. Wang, F. Richard Yu, et al. · Frontiers in Oncology (2026) | TGRS Research Map | TGRS