Clinical utility of ultrasound radiomics models in differentiating cervical tuberculous lymphadenitis from cervical lymph node metastasis

ObjectiveCervical tuberculous lymphadenitis (CTBL) and cervical lymph node metastasis (CLNM) share similar imaging characteristics, making differentiation challenging. This study aims to evaluate the clinical utility of a multimodal radiomics model combining grayscale ultrasound (GUS), elastography ultrasound (EUS), and contrast-enhanced ultrasound (CEUS) for distinguishing CTBL from CLNM.MethodsA high-quality dataset comprising 203 cases of CTBL was used to train and test the radiomics models. The performance of single-modal (GUS, EUS, CEUS) and combined models was compared using AUC, sensitivity, specificity, and accuracy metrics. An independent test set of 45 cases was included for validation.ResultsThe combined GUS + EUS + CEUS model outperformed single-modal models, achieving AUCs of 0.894, 0.832, and 0.919 in the training, validation, and test sets, respectively. Its diagnostic performance was comparable to a clinical model in validation and test sets, demonstrating superior generalizability and robustness. Wavelet features accounted for all selected features, enhancing the model's discrimination ability.ConclusionsThe integration of three ultrasound modalities captures multidimensional imaging features, reducing reliance on subjective interpretation. This multimodal radiomics approach provides a standardized diagnostic tool with significant clinical potential, particularly for less experienced physicians. Further validation with diverse datasets is needed to confirm its utility.

Authors

Institutions

Publication Details

Journal
Clinical Hemorheology and Microcirculation
Published
2025-08-11
DOI
https://doi.org/10.1177/13860291251364588
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Clinical utility of ultrasound radiomics models in differentiating cervical tuberculous lymphadenitis from cervical lymph node metastasis

Ying–Jun Angela Zhang, Litao Sun, Gaoyi Yang, Hongxiang Fu et al.
1 links
Clinical Hemorheology and Microcirculation
Radiomics and Machine Learning in Medical Imaging
article

Clinical utility of ultrasound radiomics models in differentiating cervical tuberculous lymphadenitis from cervical lymph node metastasis

Ying–Jun Angela Zhang, Litao Sun, Gaoyi Yang, Hongxiang Fu, Peijun Chen, Xiang‐Yu Meng, Ying Wang
article en

Abstract

ObjectiveCervical tuberculous lymphadenitis (CTBL) and cervical lymph node metastasis (CLNM) share similar imaging characteristics, making differentiation challenging. This study aims to evaluate the clinical utility of a multimodal radiomics model combining grayscale ultrasound (GUS), elastography ultrasound (EUS), and contrast-enhanced ultrasound (CEUS) for distinguishing CTBL from CLNM.MethodsA high-quality dataset comprising 203 cases of CTBL was used to train and test the radiomics models. The performance of single-modal (GUS, EUS, CEUS) and combined models was compared using AUC, sensitivity, specificity, and accuracy metrics. An independent test set of 45 cases was included for validation.ResultsThe combined GUS + EUS + CEUS model outperformed single-modal models, achieving AUCs of 0.894, 0.832, and 0.919 in the training, validation, and test sets, respectively. Its diagnostic performance was comparable to a clinical model in validation and test sets, demonstrating superior generalizability and robustness. Wavelet features accounted for all selected features, enhancing the model's discrimination ability.ConclusionsThe integration of three ultrasound modalities captures multidimensional imaging features, reducing reliance on subjective interpretation. This multimodal radiomics approach provides a standardized diagnostic tool with significant clinical potential, particularly for less experienced physicians. Further validation with diverse datasets is needed to confirm its utility.

Clinical Hemorheology and Microcirculation
Zhejiang Provincial People's Hospital (CN), Affiliated Hangzhou First People's Hospital, Westlake University, School of Medicine (CN), Hangzhou Red Cross Hospital (CN), Hangzhou Medical College (CN)
Openalex Percentile: Top 71%
Radiomics and Machine Learning in Medical Imaging
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.