Childhood lymphadenopathy: ultrasonographic predictors of malignancy in a retrospective cohort of 500 patients


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Çalışkan Kamış Ş., Yağcı B.

Frontiers in Pediatrics, cilt.13, 2025 (SCI-Expanded, Scopus)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 13
  • Basım Tarihi: 2025
  • Doi Numarası: 10.3389/fped.2025.1663515
  • Dergi Adı: Frontiers in Pediatrics
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, EMBASE, Directory of Open Access Journals
  • Anahtar Kelimeler: lymphadenopathy, pediatrics, ultrasonography, biopsy, malignancy, lymphoma
  • Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
  • Sağlık Bilimleri Üniversitesi Adresli: Evet

Özet

Introduction: Pediatric lymphadenopathy is common and usually benign, yet selecting children who need biopsy remains challenging. We evaluated clinical and ultrasonographic (US) predictors of malignancy in a large pediatric cohort. Methods: We retrospectively analyzed 500 children (0-18 years) evaluated for regional lymphadenopathy at a tertiary pediatric hematology-oncology clinic (June 2024-March 2025). Demographics, node location/size/count, US features, viral tests, and biopsy/bone-marrow results were extracted. Univariable tests and multivariable logistic regression identified independent predictors of malignancy. Model performance was assessed with Hosmer-Lemeshow and ROC AUC. Results: Median age was 6 years; cervical nodes predominated (92.2%), and multiple nodes were frequent (93.6%). Biopsy was performed in 47 children; malignancy was found in 23. Final diagnoses in the cohort were reactive (87.4%), infectious (6.8%), hematologic malignancy (4.4%), and solid tumor metastasis (1.4%). On univariable analysis, node size ≥2 cm, multiplicity, and region were associated with malignancy (p < 0.05). In multivariable analysis, only US assessment remained independently predictive: nodes categorized as suspicious on US had ∼56-fold higher odds of malignancy (adjusted OR ≈ 55.6; 95% CI 14.3-200; p < 0.001), whereas size and region were not significant. The model showed good calibration and excellent discrimination (AUC = 0.92; 95% CI 0.88-0.96). Discussion: US features outperformed traditional parameters (size/site) for predicting malignancy. Incorporating US-driven criteria into pathways may reduce unnecessary biopsies while preserving timely cancer detection.