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Volume 16
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10.3390/diagnostics16020355
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Automated Lymph Node Localization and Segmentation in Patients with Head and Neck Cancer: Opportunities and Limitations of Using a Generic AI Model

by
Miriam Rinneburger
1,*,
Heike Carolus
2,
Andra-Iza Iuga
1,
Mathilda Weisthoff
1,
Simon Lennartz
1,
Nils Große Hokamp
1,
Liliana Lourenco Caldeira
1,
Astha Jaiswal
1,
David Maintz
1,
Fabian Christopher Laqua
3,
Bettina Baeßler
4,
Tobias Klinder
2 and
Thorsten Persigehl
1
1
Institute of Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany
2
Philips Innovative Technologies, 22335 Hamburg, Germany
3
Institute of Diagnostic and Interventional Radiology, RoMed Klinikum Rosenheim, 83022 Rosenheim, Germany
4
Institute of Diagnostic and Interventional Radiology, University Hospital Würzburg, 97080 Würzburg, Germany
*
Author to whom correspondence should be addressed.
Diagnostics 2026, 16(2), 355; https://doi.org/10.3390/diagnostics16020355
Submission received: 5 January 2026 / Revised: 15 January 2026 / Accepted: 16 January 2026 / Published: 21 January 2026
(This article belongs to the Special Issue Advances in Head and Neck and Oral Maxillofacial Radiology)
Versions Notes

Abstract

Background/Objectives: Accurate assessment of lymph nodes is of paramount importance for correct cN staging in head and neck cancer; however, it is very time-consuming for radiologists, and lymph node metastases of head and neck cancers may show distinct characteristics, such as central necrosis or very large size. Here, we evaluate the performance of a previously developed generic cervical lymph node segmentation model in a cohort of patients with head and neck cancer. Methods: In our retrospective single-center, multi-vendor study, we included 125 patients with head and neck cancer with at least one untreated lymph node metastasis. On the respective cervical CT scan, an experienced radiologist segmented lymph nodes semi-automatically. All 3D segmentations were confirmed by a second reader. These manual segmentations were compared to segmentations generated by an AI model previously trained on a different dataset of varying cancers. Results: In cervical CT scans from 125 patients (61.9 years ± 10.6, 100 men), 3656 lymph nodes were segmented as ground-truth, including 544 clinical metastases. The AI achieved an average recall of 0.70 with 6.5 false positives per CT scan. The average global Dice accounts for 0.73 per scan, with an average Hausdorff distance of 0.88 mm. When analyzing the individual nodes, segmentation accuracy was similar for non-metastatic and metastatic lymph nodes, with a sensitivity of 0.89 and 0.85. Localization performance was lower for metastatic than for non-metastatic lymph nodes, with a recall of 0.65 and 0.74, respectively. Model performance was worse for enlarged nodes (short-axis diameter ≥ 15 mm), with a recall of 0.36 and a sensitivity of 0.67. Conclusions: The AI model for generic cervical lymph node segmentation shows good performance for smaller nodes (SAD ≤ 15 mm) with respect to localization and segmentation accuracy. However, for clearly enlarged and necrotic nodes, a retraining of the generic AI algorithm seems to be required for accurate cN staging.
Keywords: head and neck cancer; deep learning; artificial intelligence; lymph nodes; computed tomography; staging head and neck cancer; deep learning; artificial intelligence; lymph nodes; computed tomography; staging

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MDPI and ACS Style

Rinneburger, M.; Carolus, H.; Iuga, A.-I.; Weisthoff, M.; Lennartz, S.; Große Hokamp, N.; Caldeira, L.L.; Jaiswal, A.; Maintz, D.; Laqua, F.C.; et al. Automated Lymph Node Localization and Segmentation in Patients with Head and Neck Cancer: Opportunities and Limitations of Using a Generic AI Model. Diagnostics 2026, 16, 355. https://doi.org/10.3390/diagnostics16020355

AMA Style

Rinneburger M, Carolus H, Iuga A-I, Weisthoff M, Lennartz S, Große Hokamp N, Caldeira LL, Jaiswal A, Maintz D, Laqua FC, et al. Automated Lymph Node Localization and Segmentation in Patients with Head and Neck Cancer: Opportunities and Limitations of Using a Generic AI Model. Diagnostics. 2026; 16(2):355. https://doi.org/10.3390/diagnostics16020355

Chicago/Turabian Style

Rinneburger, Miriam, Heike Carolus, Andra-Iza Iuga, Mathilda Weisthoff, Simon Lennartz, Nils Große Hokamp, Liliana Lourenco Caldeira, Astha Jaiswal, David Maintz, Fabian Christopher Laqua, and et al. 2026. "Automated Lymph Node Localization and Segmentation in Patients with Head and Neck Cancer: Opportunities and Limitations of Using a Generic AI Model" Diagnostics 16, no. 2: 355. https://doi.org/10.3390/diagnostics16020355

APA Style

Rinneburger, M., Carolus, H., Iuga, A.-I., Weisthoff, M., Lennartz, S., Große Hokamp, N., Caldeira, L. L., Jaiswal, A., Maintz, D., Laqua, F. C., Baeßler, B., Klinder, T., & Persigehl, T. (2026). Automated Lymph Node Localization and Segmentation in Patients with Head and Neck Cancer: Opportunities and Limitations of Using a Generic AI Model. Diagnostics, 16(2), 355. https://doi.org/10.3390/diagnostics16020355

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