LymphUs: A multicenter open-access database of lymph node ultrasound images in patients with papillary thyroid carcinoma for clinical and artificial intelligence research

Approximately 30–50% of Papillary thyroid carcinoma (PTC) patients develop cervical lymph nodes (LNs) metastasis, significantly increasing the risk of disease recurrence and impacting long-term outcomes. We introduced an open-access multicenter lymph node ultrasound image database (LymphUs) specifically designed to advance research in LN assessment for PTC. Ultrasound imaging was performed on PTC patients at two independent clinical centers using standardized acquisition protocols. Experienced radiologists at each center documented sixteen semantic features for each LN. All LNs were annotated with segmentation masks serving as ground truth, and classification into benign or malignant categories was confirmed by fine needle aspiration biopsy results. The LymphUs comprises ultrasound images with segmentation masks from 338 PTC patients with suspected LN metastasis, divided into two center-specific cohorts: 180 patients (81 malignant, 99 benign) and 158 patients (82 malignant, 76 benign). The complete dataset, including semantic features and expert annotations, is freely accessible for research purposes. The LymphUs bridges a critical gap in medical imaging resources by providing a large-scale, multicenter ultrasound database for cervical LN assessment in PTC, supporting diagnostic algorithms, standardized reporting systems, and artificial intelligence applications to enhance preoperative LN staging and treatment planning.

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Publication Details

Journal
Data in Brief
Published
2026-03-17
DOI
https://doi.org/10.1016/j.dib.2026.112694
Primary Topic
AI in cancer detection
Type
article
Field-Weighted Citation Impact
0.00
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article

LymphUs: A multicenter open-access database of lymph node ultrasound images in patients with papillary thyroid carcinoma for clinical and artificial intelligence research

Ali Abbasian Ardakani, Saeed Mohammadzadeh, Mohammad Mirza-Aghazadeh-Attari, Ru-San Tan et al.
Data in Brief
AI in cancer detection
article

LymphUs: A multicenter open-access database of lymph node ultrasound images in patients with papillary thyroid carcinoma for clinical and artificial intelligence research

Ali Abbasian Ardakani, Saeed Mohammadzadeh, Mohammad Mirza-Aghazadeh-Attari, Ru-San Tan, Alisa Mohebbi, Massimo Salvi, Afshin Mohammadi, Sepideh Hatamikia, U. Rajendra Acharya
article en

Abstract

Approximately 30–50% of Papillary thyroid carcinoma (PTC) patients develop cervical lymph nodes (LNs) metastasis, significantly increasing the risk of disease recurrence and impacting long-term outcomes. We introduced an open-access multicenter lymph node ultrasound image database (LymphUs) specifically designed to advance research in LN assessment for PTC. Ultrasound imaging was performed on PTC patients at two independent clinical centers using standardized acquisition protocols. Experienced radiologists at each center documented sixteen semantic features for each LN. All LNs were annotated with segmentation masks serving as ground truth, and classification into benign or malignant categories was confirmed by fine needle aspiration biopsy results. The LymphUs comprises ultrasound images with segmentation masks from 338 PTC patients with suspected LN metastasis, divided into two center-specific cohorts: 180 patients (81 malignant, 99 benign) and 158 patients (82 malignant, 76 benign). The complete dataset, including semantic features and expert annotations, is freely accessible for research purposes. The LymphUs bridges a critical gap in medical imaging resources by providing a large-scale, multicenter ultrasound database for cervical LN assessment in PTC, supporting diagnostic algorithms, standardized reporting systems, and artificial intelligence applications to enhance preoperative LN staging and treatment planning.

Data in BriefVol. 66
Danube Private University (AT), Universität für Weiterbildung Krems (AT), Johns Hopkins University (US), Politecnico di Torino (IT), University of Southern Queensland (AU), Urmia University (IR), Austrian Center for Medical Innovation and Technology (AT), Universal Scientific Education and Research Network (IR), Duke-NUS Medical School (SG), National Heart Centre Singapore (SG), Shahid Beheshti University of Medical Sciences (IR), Tehran University of Medical Sciences (IR), Medical University of Vienna (AT)
Openalex Percentile: Top 21%
AI in cancer detection
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