Radiomics-based prediction of nonalcoholic fatty liver disease following pancreatoduodenectomy.
Exocrine pancreatic insufficiency
Machine learning
Malnutrition
Nonalcoholic fatty liver disease
Pancreatectomy
Journal
Surgery today
ISSN: 1436-2813
Titre abrégé: Surg Today
Pays: Japan
ID NLM: 9204360
Informations de publication
Date de publication:
06 Apr 2024
06 Apr 2024
Historique:
received:
18
12
2023
accepted:
09
01
2024
medline:
6
4
2024
pubmed:
6
4
2024
entrez:
6
4
2024
Statut:
aheadofprint
Résumé
Predicting nonalcoholic fatty liver disease (NAFLD) following pancreaticoduodenectomy (PD) is challenging, which delays therapeutic intervention and makes its prevention difficult. We conducted this study to assess the potential application of preoperative computed tomography (CT) radiomics for predicting NAFLD. The subjects of this retrospective study were 186 patients with PD from a single institution. We extracted the predictors of NAFLD after PD statistically from conventional clinical and radiomic features of the estimated remnant pancreas and whole liver region on preoperative nonenhanced CT images. Based on these predictors, we developed a machine-learning predictive model, which integrated clinical and radiomic features. A comparative model used only clinical features as predictors. The incidence of NAFLD after PD was 43.5%. The variables of the clinicoradiomic model included one shape feature of the pancreas, two texture features of the liver, and sex; the variables of the clinical model were age, sex, and chemoradiotherapy. The accuracy%, precision%, recall%, F1 score, and area under the curve of the two models were 75.0, 72.7, 66.7, 69.6, and 0.80; and 69.6, 68.4, 54.2, 60.5, and 0.69, respectively. Preoperative CT-derived radiomic features from the pancreatic and liver regions are promising for the prediction of NAFLD post-PD. Using these features enhances the predictive model, enabling earlier intervention for high-risk patients.
Identifiants
pubmed: 38581555
doi: 10.1007/s00595-024-02822-0
pii: 10.1007/s00595-024-02822-0
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024. The Author(s) under exclusive licence to Springer Nature Singapore Pte Ltd.
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