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Cervical lymphadenopathy in children: a diagnostic tree analysis model based on ultrasonographic and clinical
Ji Eun Park1, Young Jin Ryu2,3, Ji Young Kim1,4
1Department of Radiology, Seoul National University Bundang Hospital, 82 Gumi-ro 173 Beon-gil, Bundang-gu, Seongnam, 13620, South Korea.
Insights
A diagnostic tree analysis model using ultrasonography (US) and clinical data aids in differentiating pediatric cervical lymphadenopathy causes. Key US findings include perinodal fat changes and lymph node echotexture.
Area of Science:
- Pediatric Radiology
- Diagnostic Imaging
- Oncology
Background:
- Cervical lymphadenopathy in children has diverse causes, necessitating accurate differential diagnosis.
- Ultrasonography (US) is a primary imaging modality for evaluating pediatric cervical lymphadenopathy.
Purpose of the Study:
- To develop a diagnostic tree analysis (DTA) model using US findings and clinical characteristics.
- To differentiate common causes of cervical lymphadenopathy in children.
Main Methods:
- Retrospective review of US images and clinical data from 242 pediatric patients.
- Development of DTA models using classification and regression tree algorithms.
- Validation of models using training (70%) and validation (30%) datasets.
Main Results:
- The DTA model achieved high diagnostic accuracy (83.3-86.1%) in both training and validation sets.
- Significant US predictors included perinodal fat hyperechogenicity, lymph node echogenicity, and short diameter of the largest lymph node.
- Loss of fatty hilum was also a significant predictor in the categorical parameter model.
Conclusions:
- A DTA model integrating US and clinical findings is valuable for pediatric cervical lymphadenopathy diagnosis.
- Specific US features like perinodal fat hyperechogenicity and heterogeneous echotexture are crucial diagnostic indicators.
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.
