Journal of clinical ultrasound : JCU · 2025
Development of a Predictive Model Based on Ultrasonographic Characteristics to Distinguish Neonatal Adrenal Cystic Neuroblastoma From Hematoma.
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Chen Munan, Hu Yanxiu, Wang Xiaoman
Аннотация
PURPOSE: This study aimed to develop a predictive model based on ultrasonographic characteristics to improve the diagnostic accuracy in differentiating cystic neuroblastoma from hematoma.
METHODS: This retrospective study included newborns who had undergone their first ultrasonography from 2013 to 2023. In total, 39 267 newborns, including those with hematoma and suspected cystic neuroblastoma, were included. Ultrasonographic characteristics of newborns with hematoma and suspected cystic neuroblastoma were compared, and data analysis was performed using a binary logistic regression model.
RESULTS: Anterior-posterior size, vertical size, presence of calcification, and cystic fluid echogenicity were identified as significant predictive factors for distinguishing between cystic neuroblastoma and hematoma. The area under the curve of the model was 0.962, indicating a high diagnostic efficacy.
CONCLUSION: The predictive model constructed based on ultrasonographic characteristics effectively differentiated between cystic neuroblastoma and hematoma, providing a highly efficient diagnostic tool for clinical use. To further validate and optimize this predictive model, future research should expand the sample size and include multicenter data.
METHODS: This retrospective study included newborns who had undergone their first ultrasonography from 2013 to 2023. In total, 39 267 newborns, including those with hematoma and suspected cystic neuroblastoma, were included. Ultrasonographic characteristics of newborns with hematoma and suspected cystic neuroblastoma were compared, and data analysis was performed using a binary logistic regression model.
RESULTS: Anterior-posterior size, vertical size, presence of calcification, and cystic fluid echogenicity were identified as significant predictive factors for distinguishing between cystic neuroblastoma and hematoma. The area under the curve of the model was 0.962, indicating a high diagnostic efficacy.
CONCLUSION: The predictive model constructed based on ultrasonographic characteristics effectively differentiated between cystic neuroblastoma and hematoma, providing a highly efficient diagnostic tool for clinical use. To further validate and optimize this predictive model, future research should expand the sample size and include multicenter data.