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Cervical lymphadenopathy in children: a diagnostic tree analysis model based on ultrasonographic and clinical findings

  • Paediatric
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Abstract

Objectives

To establish a diagnostic tree analysis (DTA) model based on ultrasonography (US) findings and clinical characteristics for differential diagnosis of common causes of cervical lymphadenopathy in children.

Methods

A total of 242 patients (131 boys, 111 girls; mean age, 11.2 ± 0.3 years; range, 1 month–18 years) with pathologically confirmed Kikuchi disease (n = 127), reactive hyperplasia (n = 64), lymphoma (n = 24), or suppurative lymphadenitis (n = 27) who underwent neck US were included. US images were retrospectively reviewed to assess lymph node (LN) characteristics, and clinical information was collected from patient records. DTA models were created using a classification and regression tree algorithm on the basis of US imaging and clinical findings. The patients were randomly divided into training (70%, 170/242) and validation (30%, 72/242) datasets to assess the diagnostic performance of the DTA models.

Results

In the DTA model based on all predictors, perinodal fat hyperechogenicity, LN echogenicity, and short diameter of the largest LN were significant predictors for differential diagnosis of cervical lymphadenopathy (overall accuracy, 85.3% and 83.3% in the training and validation datasets). In the model based on categorical parameters alone, perinodal fat hyperechogenicity, LN echogenicity, and loss of fatty hilum were significant predictors (overall accuracy, 84.7% and 86.1% in the training and validation datasets).

Conclusions

Perinodal fat hyperechogenicity, heterogeneous echotexture, short diameter of the largest LN, and loss of fatty hilum were significant US findings in the DTA for differential diagnosis of cervical lymphadenopathy in children.

Key Points

• Diagnostic tree analysis model based on ultrasonography and clinical findings would be helpful in differential diagnosis of pediatric cervical lymphadenopathy.

• Significant predictors were perinodal fat hyperechogenicity, heterogeneous echotexture, short diameter of the largest LN, and loss of fatty hilum.

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Abbreviations

CART:

Classification and regression tree

CNB:

Core needle biopsy

CRP:

C-reactive protein

CT:

Computed tomography

DTA:

Decision tree analysis

ESR:

Erythrocyte sedimentation rate

FNA:

Fine needle aspiration

ICC:

Interclass correlation coefficient

LD:

Long diameter

LN:

Lymph node

PACS:

Picture archival and communication system

SD:

Short diameter

US:

Ultrasonography

WBC:

White blood cell count

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Funding

This study has received funding by Seoul National University College of Medicine Education Research Foundation.

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Authors and Affiliations

  1. Department of Radiology, Seoul National University Bundang Hospital, 82 Gumi-ro 173 Beon-gil, Bundang-gu, Seongnam, 13620, South Korea

    Ji Eun Park, Young Jin Ryu, Ji Young Kim & Young Hoon Kim

  2. Department of Radiology, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea

    Young Jin Ryu, Ji Young Kim & Young Hoon Kim

  3. Department of Pediatrics, Seoul National University Bundang Hospital, Seongnam, South Korea

    Ji Young Park, Hyunju Lee & Hyoung Soo Choi

  4. Department of Pediatrics, Seoul National University College of Medicine, Seoul, South Korea

    Hyunju Lee & Hyoung Soo Choi

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  1. Ji Eun Park
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Correspondence to Young Jin Ryu.

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The scientific guarantor of this publication is Young Jin Ryu.

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The authors declare that they have no competing interests.

Statistics and biometry

The Division of Statistics in the Medical Research Collaborating Center at Seoul National University Bundang Hospital kindly provided statistical advice for this manuscript.

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Written informed consent was waived by the Institutional Review Board.

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Institutional Review Board approval was obtained.

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• Retrospective

• Case-control study

• Performed at one institution

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Park, J.E., Ryu, Y.J., Kim, J.Y. et al. Cervical lymphadenopathy in children: a diagnostic tree analysis model based on ultrasonographic and clinical findings. Eur Radiol 30, 4475–4485 (2020). https://doi.org/10.1007/s00330-020-06794-w

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  • DOI: https://doi.org/10.1007/s00330-020-06794-w

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