Exploring the diagnostic value of ultrasound radiomics for neonatal respiratory distress syndrome

Background Neonatal respiratory distress syndrome (NRDS) is a prevalent cause of respiratory failure and death among newborns, and prompt diagnosis is imperative. Historically, diagnosis of NRDS relied mostly on typical clinical manifestations, chest X-rays, and CT scans. However, recently, ultrasound has emerged as a valuable and preferred tool for aiding NRDS diagnosis. Nevertheless, evaluating lung ultrasound imagery necessitates rigorous training and may be subject to operator-dependent bias, limiting its widespread use. As a result, it is essential to investigate a new, reliable, and operator-independent diagnostic approach that does not require subjective factors or operator expertise. This article aims to explore the diagnostic potential of ultrasound-based radiomics in differentiating NRDS from other non-NRDS lung disease. Methods A total of 150 neonatal lung disease cases were consecutively collected from the department of neonatal intensive care unit of the Quanzhou Maternity and Children’s Hospital, Fujian Province, from September 2021 to October 2022. Of these patients, 60 were diagnosed with NRDS, whereas 30 were diagnosed with neonatal pneumonia, meconium aspiration syndrome (MAS), and transient tachypnea (TTN). Two ultrasound images with characteristic manifestations of each lung disease were acquired and divided into training (n = 120) and validation cohorts (n = 30) based on the examination date using an 8:2 ratio. The imaging texture features were extracted using PyRadiomics and, after the screening, machine learning models such as random forest (RF), logistic regression (LR), K-nearest neighbors (KNN), support vector machine (SVM), and multilayer perceptron (MLP) were developed to construct an imaging-based diagnostic model. The diagnostic efficacy of each model was analyzed. Lastly, we randomly selected 282 lung ultrasound images and evaluated the diagnostic efficacy disparities between the optimal model and doctors across differing levels of expertise. Results Twenty-two imaging-based features with the highest weights were selected to construct a predictive model for neonatal respiratory distress syndrome. All models exhibited favorable diagnostic performances. Analysis of the Youden index demonstrated that the RF model had the highest score in both the training (0.99) and validation (0.90) cohorts. Additionally, the calibration curve indicated that the RF model had the best calibration (P = 0.98). When compared to the diagnostic performance of experienced and junior physicians, the RF model had an area under the curve (AUC) of 0.99; however, the values for experienced and junior physicians were 0.98 and 0.85, respectively. The difference in diagnostic efficacy between the RF model and experienced physicians was not statistically significant (P = 0.24), whereas that between the RF model and junior physicians was statistically significant (P < 0.0001). Conclusion The RF model exhibited excellent diagnostic performance in the analysis of texture features based on ultrasound radiomics for diagnosing NRDS..

Medienart:	E-Artikel

Erscheinungsjahr:	2024
Erschienen:	2024

Enthalten in:	Zur Gesamtaufnahme - volume:24
Enthalten in:	BMC pediatrics - 24(2024), 1 vom: 25. März

Sprache:	Englisch

Beteiligte Personen:	Lin, Weiru [VerfasserIn] Ruan, Junxian [VerfasserIn] Liu, Zhiyong [VerfasserIn] Liu, Caihong [VerfasserIn] Wang, Jianan [VerfasserIn] Chen, Linjun [VerfasserIn] Zhang, Weifeng [VerfasserIn] Lyu, Guorong [VerfasserIn]

Links:	Volltext [kostenfrei]

BKL:	44.00
Themen:	Machine learning Neonatal respiratory distress syndrome Predictive model Radiomics Ultrasound diagnosis

Anmerkungen:	© The Author(s) 2024

doi:	10.1186/s12887-024-04704-3

funding:
Förderinstitution / Projekttitel:

PPN (Katalog-ID):	SPR05529426X