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
- Ying–Jun Angela Zhang (ORCID: https://orcid.org/0000-0002-7304-6849)
- Litao Sun (ORCID: https://orcid.org/0000-0002-4724-0971)
- Gaoyi Yang (ORCID: https://orcid.org/0000-0002-0759-3684)
- Hongxiang Fu (ORCID: https://orcid.org/0000-0002-9873-8606)
- Peijun Chen (ORCID: https://orcid.org/0000-0002-6076-1894)
- Xiang‐Yu Meng (ORCID: https://orcid.org/0009-0001-4623-4510)
- Ying Wang (ORCID: https://orcid.org/0009-0008-3662-2692)
Institutions
- 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)
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