Skip to main content

Advertisement

SpringerLink
Log in

Observer- and radiomics model-based computed tomography classification of suppurative versus tuberculous lymphadenitis complicated with nodal necrosis of the neck in children

  • Original Article
  • Published:
Pediatric Radiology Aims and scope Submit manuscript

Abstract

Background

Computed tomography (CT) can be used for the early detection of lymphadenitis. Radiomics is able to identify a large amount of hidden information from images. However, few CT-based radiomics studies on cervical lymphadenitis in children have been published.

Objective

This study aimed to investigate the role of visual CT analysis and CT radiomics in differentiating cervical suppurative node necrosis from tuberculous node necrosis in pediatric patients.

Materials and methods

A total of 101 patients with cervical suppurative lymphadenitis (n=52) or cervical tuberculous lymphadenitis (n=49) were included. Clinical data and CT images were retrieved for analysis. For visual observation, 11 major CT features were identified for univariate and multivariate analyses. For radiomics analysis, image segmentation, feature value extraction, and dimension reduction, feature selection and the construction of radiomics-based models were performed through the RadCloud platform.

Results

For the visual observation, significant differences were found between the two groups, including the short diameter of the largest necrotic lymph node (P=0.03), sharp border of the node (P=0.02), fusion of nodes (P=0.02), regular silhouette of the necrotic area (P=0.001), multilocular necrotic area (P=0.02), node calcification (P=0.004), and enhancement degree of the nodal nonnecrotic area (P=0.01). No feature was found to be an independent predictor for suppurative or tuberculous lymphadenitis (P>0.05 for all features). Concerning the radiomics analysis, after feature value extraction and dimension reduction, nine related features were selected. The support vector machine classifier achieved high diagnostic performance in distinguishing suppurative from tuberculous lymphadenitis. The area under the curve, accuracy, sensitivity, and specificity of the support vector machine model test set were 0.89 (95% confidence interval: 0.72–1.00), 0.88, 0.78, and 0.90, respectively.

Conclusion

Compared to observer-based CT image analyses, radiomics model-based CT image analyses exhibit better performance in the differential diagnosis of cervical suppurative and tuberculous lymphadenitis complicated with nodal necrosis in children.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Lou F, Zhang T, Ming C et al (2020) Clinic analysis of cervical lymph node lesions in children. Lin Chung Er Bi Yan Hou Tou Jing Wai Ke Za Zhi 34:928–931

    Google Scholar 

  2. Lyon SM, Rossman MD (2017) Pulmonary tuberculosis. Microbiol Spectr 5(1):10–1128

    Article  Google Scholar 

  3. Naranje P, Bhalla AS, Sherwani P (2019) Chest tuberculosis in children. Indian J Pediatr 86:448–458

    Article  PubMed  Google Scholar 

  4. Roy CF, Balakrishnan K, Boudewyns A et al (2023) International Pediatric Otolaryngology Group: Consensus guidelines on the diagnosis and management of nontuberculous mycobacterial cervicofacial lymphadenitis. Int J Pediatr Otorhinolaryngol 166:111469

    Article  PubMed  Google Scholar 

  5. Baek CH, Kim SI, Ko YH et al (2000) Polymerase chain reaction detection of mycobacterium tuberculosis from fine-needle aspirate for the diagnosis of cervical tuberculous lymphadenitis. Laryngoscope 110:30–34

    Article  CAS  PubMed  Google Scholar 

  6. Mittal P, Handa U, Mohan H et al (2011) Comparative evaluation of fine needle aspiration cytology, culture, and PCR in diagnosis of tuberculous lymphadenitis. Diagn Cytopathol 39:822–826

    Article  PubMed  Google Scholar 

  7. Ahuja AT, Ying M, Ho SY et al (2008) Ultrasound of malignant cervical lymph nodes. Cancer Imaging 8:48–56

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Eisenmenger LB, Wiggins RH 3rd (2015) Imaging of head and neck lymph nodes. Radiol Clin North Am 53:115–132

    Article  PubMed  Google Scholar 

  9. Schröder RJ, Rost B, Hidajat N et al (2002) Value of contrast-enhanced ultrasound vs. CT and MRI in palpable enlarged lymph nodes of the head and neck. Rofo 174:1099–1106

    Article  PubMed  Google Scholar 

  10. Holzapfel K, Duetsch S, Fauser C et al (2009) Value of diffusion-weighted MR imaging in the differentiation between benign and malignant cervical lymph nodes. Eur J Radiol 72:381–387

    Article  PubMed  Google Scholar 

  11. Steinkamp HJ, Hosten N, Richter C et al (1994) Enlarged cervical lymph nodes at helical CT. Radiology 191:795–798

    Article  CAS  PubMed  Google Scholar 

  12. Cunqueiro A, Gomes WA, Lee P et al (2019) CT of the neck: image analysis and reporting in the emergency setting. Radiographics 39:1760–1781

    Article  PubMed  Google Scholar 

  13. Mayerhoefer ME, Materka A, Langs G et al (2020) Introduction to radiomics. J Nucl Med 61:488–495

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Tsai YL, Chen SW, Kao CH et al (2022) Neck lymph node recurrence in HNC patients might be predicted before radiotherapy using radiomics extracted from CT images and XGBoost algorithm. J Pers Med 12:1377

    Article  PubMed  PubMed Central  Google Scholar 

  15. Wang H, Chen X, Liu H et al (2021) Computed tomography-based radiomics for differential of retroperitoneal neuroblastoma and ganglioneuroblastoma in children. Nan Fang Yi Ke Da Xue Xue Bao 41:1569–1576

    CAS  PubMed  Google Scholar 

  16. Grégoire V, Ang K, Budach W et al (2014) Delineation of the neck node levels for head and neck tumors: a 2013 update. DAHANCA, EORTC, HKNPCSG, NCIC CTG, NCRI, RTOG, TROG consensus guidelines. Radiother Oncol 110:172–181

  17. Kayi Cangir A, Orhan K, Kahya Y et al (2021) CT imaging-based machine learning model: a potential modality for predicting low-risk and high-risk groups of thymoma: “impact of surgical modality choice.” World J Surg Oncol 19:147

    Article  PubMed  PubMed Central  Google Scholar 

  18. Cangir AK, Orhan K, Kahya Y et al (2022) A CT-based radiomic signature for the differentiation of pulmonary hamartomas from carcinoid tumors. Diagnostics (Basel) 12:416

    Article  PubMed  Google Scholar 

  19. Kato H, Kanematsu M, Kato Z et al (2013) Necrotic cervical nodes: usefulness of diffusion-weighted MR imaging in the differentiation of suppurative lymphadenitis from malignancy. Eur J Radiol 82:e28–e35

    Article  PubMed  Google Scholar 

  20. Srinivas CV, Nair S (2019) Clinicopathological profile of cervical tubercular lymphadenitis with special reference to fine needle aspiration cytology. Indian J Otolaryngol Head Neck Surg 71:205–211

    Article  PubMed  Google Scholar 

  21. Agarwal A (2020) Paediatric osteoarticular tuberculosis: a review. J Clin Orthop Trauma 11:202–207

    Article  PubMed  PubMed Central  Google Scholar 

  22. Fraser IP (2009) Suppurative lymphadenitis. Curr Infect Dis Rep 11:383–388

    Article  PubMed  Google Scholar 

  23. Kim WS, Moon WK, Kim IO et al (1997) Pulmonary tuberculosis in children: evaluation with CT. AJR Am J Roentgenol 168:1005–1009

    Article  CAS  PubMed  Google Scholar 

  24. Luo MY, Liu L, Lai LS et al (2010) Deepgoing study on intrathoracic tuberculous lymphadenitis in adults using multidetector CT. Chin Med J (Engl) 123:1283–1288

    PubMed  Google Scholar 

  25. Lee S, Yoo JH, Lee SW (2012) Kikuchi disease: differentiation from tuberculous lymphadenitis based on patterns of nodal necrosis on CT. AJNR Am J Neuroradiol 33:135–140

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Kim BH, Lee C, Lee JY et al (2022) Initial experience of a deep learning application for the differentiation of Kikuchi-Fujimoto’s disease from tuberculous lymphadenitis on neck CECT. Sci Rep 12:14184

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Gillies RJ, Kinahan PE, Hricak H (2015) Radiomics: images are more than pictures, they are data. Radiology 278:563–577

    Article  PubMed  Google Scholar 

  28. Liu A, Wang Z, Yang Y et al (2020) Preoperative diagnosis of malignant pulmonary nodules in lung cancer screening with a radiomics nomogram. Cancer Commun (Lond) 40:16–24

    Article  PubMed  Google Scholar 

  29. Fasmer KE, Hodneland E, Dybvik JA et al (2021) Whole-volume tumor MRI radiomics for prognostic modeling in endometrial cancer. J Magn Reson Imaging 53:928–937

    Article  PubMed  Google Scholar 

  30. Li Y, Liu Y, Liang Y et al (2022) Radiomics can differentiate high-grade glioma from brain metastasis: a systematic review and meta-analysis. Eur Radiol 32:8039–8051

    Article  PubMed  Google Scholar 

  31. Song J, Yin Y, Wang H et al (2020) A review of original articles published in the emerging field of radiomics. Eur J Radiol 127:108991

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Radiology, Children’s Hospital of Chongqing Medical University, The Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Pediatrics, 136 Zhongshan Er Lu, Yuzhong District, Chongqing, 400000, China

    Rui Zhang, Ye Xu, Sijie Gao & Wei Li

  2. Huiying Medical Technology Co., Ltd., Haidian District, Beijing, 100192, China

    Yang Jing

Authors
  1. Rui Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ye Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sijie Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yang Jing
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design; material preparation, data collection, and analysis were performed by R.Z., S.G., and W.L.; the first draft of the manuscript was written by R.Z. and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Wei Li.

Ethics declarations

Ethics approval

Ethical approval was waived by Children’s Hospital of Chongqing Medical University in view of the retrospective nature of the study and all the procedures being performed were part of the routine care.

Conflicts of interest

None

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 16 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, R., Xu, Y., Gao, S. et al. Observer- and radiomics model-based computed tomography classification of suppurative versus tuberculous lymphadenitis complicated with nodal necrosis of the neck in children. Pediatr Radiol 53, 2586–2596 (2023). https://doi.org/10.1007/s00247-023-05761-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00247-023-05761-z

Keywords

Search

Navigation