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Clinical prediction model for pediatric lymphadenopathy: enhancing diagnostic precision and treatment decision making
Tipsuda Tangsriwong1, Thipsumon Tangsiwong2
1Department of Surgery, Buddhachinaraj Hospital, 90 Srithamtraipidok Road, Nai Mueang Subdistrict, Mueang Phitsanulok District, Phitsanulok, 65000, Thailand. tiffefai@gmail.com.
Insights
A new predictive model accurately identifies pediatric lymphadenopathy, distinguishing between benign and malignant causes. This tool helps reduce unnecessary biopsies for children with enlarged lymph nodes.
Area of Science:
- Pediatric Oncology
- Diagnostic Imaging
- Clinical Pathology
Background:
- Lymph node enlargement is a common finding in children, with most cases being physiological.
- Accurate diagnosis of pediatric lymphadenopathy is crucial for appropriate management and to avoid unnecessary invasive procedures.
Purpose of the Study:
- To develop and validate a clinical prediction model for pediatric lymphadenopathy.
- To improve diagnostic accuracy and guide decisions regarding lymph node biopsy in children.
Main Methods:
- Retrospective, cross-sectional study of 188 children under 15 who underwent lymph node biopsy (2012-2022).
- Multivariable risk regression analysis was used to identify key clinical characteristics.
- A logistic regression equation was developed to predict probabilities of reactive hyperplasia, benign, or malignant lesions.
Main Results:
- The predictive model incorporated 12 clinical characteristics, achieving 92.2% accuracy for benign cases (AUROC=0.92) and 98.6% for malignancy (AUROC=0.98).
- Of 188 children, 37.2% had benign lymphadenopathy and 14.4% had malignant lymphadenopathy.
- Overall accuracy for predicting both benign and malignant tumors was 68.3%.
Conclusions:
- The developed model shows reasonable accuracy in predicting pediatric lymphadenopathy based on clinical features.
- The model aids in reducing unnecessary biopsies for benign conditions while effectively identifying potential malignancies.
- While tending to overestimate malignancy, the model did not miss diagnoses, improving diagnostic confidence.
Introduction:
Lymph node enlargement is common in children, with 90% of physiologically palpable lymph nodes. This study aimed to develop a predictive model based on clinical characteristics to enhance the diagnosis of pediatric lymphadenopathy and provide insights into biopsy outcomes.
Materials And Methods:
A clinical prediction rule was developed using a retrospective, cross-sectional design for patients under 15 years who underwent lymph node biopsy from 2012 to 2022. Multivariable risk regression was used to analyze benign and malignant lesions, presenting results through risk difference and AUROC for each group. Predicted probabilities were applied in a logistic regression equation to classify patients' lymphadenopathy as reactive hyperplasia, benign, or malignant.
Results:
Of 188 children, 70 (37.2%) had benign lymphadenopathy beyond reactive hyperplasia, and 27 (14.4%) had malignant lymphadenopathy. The predictive model included 12 characteristics such as size, location, duration, associated symptoms, and lymph node examination. Predictive accuracy was 92.2% for benign cases (AUROC = 0.92; 95% CI 0.87-0.96) and 98.6% for malignancy (AUROC = 0.98; 95% CI 0.94-0.99). Overall accuracy for predicting both benign and malignant tumors was 68.3%.
Conclusion:
The model demonstrated reasonably accurate predictions for the clinical characteristics of pediatric lymphadenopathy. It tended to overestimate malignancy but did not miss diagnoses, aiding in reducing unnecessary lymph node biopsies in benign cases.
