Radiomics could predict surgery at 10 years in Crohn's disease.
Crohn's disease
Outcomes
Radiomics
Journal
Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
ISSN: 1878-3562
Titre abrégé: Dig Liver Dis
Pays: Netherlands
ID NLM: 100958385
Informations de publication
Date de publication:
08 2023
08 2023
Historique:
received:
15
06
2022
revised:
04
11
2022
accepted:
07
11
2022
medline:
28
7
2023
pubmed:
27
11
2022
entrez:
26
11
2022
Statut:
ppublish
Résumé
Predicting clinical outcomes represents a major challenge in Crohn's disease (CD). Radiomics provides a method to extract quantitative features from medical images and may successfully predict clinical course. The aim of this pilot study is to evaluate the use of radiomics to predict 10-year surgery for CD patients. We selected a cohort of CD patients with CT scan enterographies and a 10-year follow up. The R library Moddicom was used to extract radiomic features from each lesion of CD, segmented in the CT scans. A logistic regression model based on selected radiomic features was developed to predict 10-year surgery. The model was evaluated by computing the area under the curve (AUC) of the receiver operating characteristic curve, sensitivity, specificity, positive and negative predictive values (PPV, NPV). We enroled 30 patients, with 44 CT scans and 93 lesions. We extracted 217 radiomic features from each lesion. The developed model was based on two radiomic features and presented an AUC (95% CI) of 0.83 (0.73-0.91) in predicting 10-year surgery. Sensitivity, specificity, PPV, NPV of the radiomic model were equal to 0.72, 0.90, 0.79, 0.86, respectively. Radiomics could be a helpful tool to identify patients with high risk for surgery and needing a stricter monitoring.
Sections du résumé
BACKGROUND
Predicting clinical outcomes represents a major challenge in Crohn's disease (CD). Radiomics provides a method to extract quantitative features from medical images and may successfully predict clinical course.
AIMS
The aim of this pilot study is to evaluate the use of radiomics to predict 10-year surgery for CD patients.
METHODS
We selected a cohort of CD patients with CT scan enterographies and a 10-year follow up. The R library Moddicom was used to extract radiomic features from each lesion of CD, segmented in the CT scans. A logistic regression model based on selected radiomic features was developed to predict 10-year surgery. The model was evaluated by computing the area under the curve (AUC) of the receiver operating characteristic curve, sensitivity, specificity, positive and negative predictive values (PPV, NPV).
RESULTS
We enroled 30 patients, with 44 CT scans and 93 lesions. We extracted 217 radiomic features from each lesion. The developed model was based on two radiomic features and presented an AUC (95% CI) of 0.83 (0.73-0.91) in predicting 10-year surgery. Sensitivity, specificity, PPV, NPV of the radiomic model were equal to 0.72, 0.90, 0.79, 0.86, respectively.
CONCLUSION
Radiomics could be a helpful tool to identify patients with high risk for surgery and needing a stricter monitoring.
Identifiants
pubmed: 36435716
pii: S1590-8658(22)00784-8
doi: 10.1016/j.dld.2022.11.005
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1042-1048Informations de copyright
Copyright © 2022. Published by Elsevier Ltd.
Déclaration de conflit d'intérêts
Declaration of Competing Interest All Authors have no conflict of interest to declare