Development and validation of a clinical prediction model to diagnose Warthin tumor based on non-contrast computed tomography features and clinical characteristics

The nomogram model incorporating clinical and non-contrast CT features demonstrates high accuracy for differentiating WT from other parotid gland tumors in clinical practice.

Authors

Institutions

Publication Details

Journal
American Journal of Otolaryngology
Published
2025-12-02
DOI
https://doi.org/10.1016/j.amjoto.2025.104767
Primary Topic
Salivary Gland Tumors 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

Development and validation of a clinical prediction model to diagnose Warthin tumor based on non-contrast computed tomography features and clinical characteristics

Xiangming Meng, Changling Sun, Dawei Chang, Fan Xu
American Journal of Otolaryngology
Salivary Gland Tumors Diagnosis and Treatment
article

Development and validation of a clinical prediction model to diagnose Warthin tumor based on non-contrast computed tomography features and clinical characteristics

Xiangming Meng, Changling Sun, Dawei Chang, Fan Xu
article en

Abstract

The nomogram model incorporating clinical and non-contrast CT features demonstrates high accuracy for differentiating WT from other parotid gland tumors in clinical practice.

American Journal of OtolaryngologyVol. 47(1)
Jiangnan University (CN), Wuxi Fourth People's Hospital (CN), Wuxi People's Hospital (CN)
Openalex Percentile: Top 43%
Salivary Gland Tumors 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.