Preoperative prediction of pathological grade in pancreatic ductal adenocarcinoma based on
18F-FDG PET/CT
Machine learning
Pancreatic cancer
Radiomics
XGBoost
Journal
EJNMMI research
ISSN: 2191-219X
Titre abrégé: EJNMMI Res
Pays: Germany
ID NLM: 101560946
Informations de publication
Date de publication:
25 Feb 2021
25 Feb 2021
Historique:
received:
02
11
2020
accepted:
10
02
2021
entrez:
25
2
2021
pubmed:
26
2
2021
medline:
26
2
2021
Statut:
epublish
Résumé
To develop and validate a machine learning model based on radiomic features derived from A total of 149 patients (83 men, 66 women, mean age 61 years old) with pathologically proven PDAC and a preoperative The prediction model based on a twelve-feature-combined radiomics signature could stratify PDAC patients into grade 1 and grade 2/3 groups with AUC of 0.994 in the training set and 0.921 in the validation set. The model developed is capable of predicting pathological differentiation grade of PDAC based on preoperative
Identifiants
pubmed: 33630176
doi: 10.1186/s13550-021-00760-3
pii: 10.1186/s13550-021-00760-3
pmc: PMC7907291
doi:
Types de publication
Journal Article
Langues
eng
Pagination
19Subventions
Organisme : National Natural Science Foundation of China
ID : 82071967
Organisme : CAMS Initiative for Innovative Medicine
ID : CAMS-2018-I2M-3-001
Organisme : Tsinghua University-Peking Union Medical College Hospital Initiative Scientific Research Program
ID : 52300300519
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