Surgical prediction of neonatal necrotizing enterocolitis based on radiomics and clinical information.
Abdom Radiol (NY)
; 49(4): 1020-1030, 2024 04.
Article
in En
| MEDLINE
| ID: mdl-38285178
ABSTRACT
PURPOSE:
To assess the predictive value of radiomics for surgical decision-making in neonatal necrotizing enterocolitis (NEC) when abdominal radiographs (ARs) do not suggest an absolute surgical indication for free pneumoperitoneum.METHODS:
In this retrospective study, we finally included 171 newborns with NEC and obtained their ARs and clinical data. The dataset was randomly divided into a training set (70%) and a test set (30%). We developed machine learning models for predicting surgical treatment using clinical features and radiomic features, respectively, and combined these features to build joint models. We assessed predictive performance of the different models by receiver operating characteristic curve (ROC) analysis and compared area under curve (AUC) using the Delong test. Decision curve analysis (DCA) was used to assess the potential clinical benefit of the models to patients.RESULTS:
There was no significant difference in AUC between the clinical model and the four radiomic models (P > 0.05). The XGBoost joint model had better predictive efficacy and stability (AUC, training set 0.988, test set 0.959). Its AUC in the test set was significantly higher than that of the clinical model (P < 0.05). DCA showed that the XGBoost joint model achieved higher net clinical benefit compared to the clinical model in the threshold probability range (0.2-0.6).CONCLUSION:
Radiomic features based on AR are objective and reproducible. The joint model combining radiomic features and clinical signs has good surgical predictive efficacy and may be an important method to help primary neonatal surgeons assess the surgical risk of NEC neonates.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Enterocolitis, Necrotizing
Type of study:
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limits:
Female
/
Humans
/
Newborn
Language:
En
Journal:
Abdom Radiol (NY)
Year:
2024
Document type:
Article
Affiliation country:
Publication country:
EEUU
/
ESTADOS UNIDOS
/
ESTADOS UNIDOS DA AMERICA
/
EUA
/
UNITED STATES
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UNITED STATES OF AMERICA
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US
/
USA