Deep Learning for Fully Automated Prediction of Overall Survival in Patients with Oropharyngeal Cancer Using FDG-PET Imaging.
Cohort Studies
Deep Learning
Female
Fluorodeoxyglucose F18
Head and Neck Neoplasms
/ diagnostic imaging
Humans
Male
Middle Aged
Oropharyngeal Neoplasms
/ diagnostic imaging
Positron-Emission Tomography
Predictive Value of Tests
Prognosis
Radiopharmaceuticals
Squamous Cell Carcinoma of Head and Neck
/ diagnostic imaging
Survival Rate
Journal
Clinical cancer research : an official journal of the American Association for Cancer Research
ISSN: 1557-3265
Titre abrégé: Clin Cancer Res
Pays: United States
ID NLM: 9502500
Informations de publication
Date de publication:
15 07 2021
15 07 2021
Historique:
received:
22
12
2020
revised:
09
03
2021
accepted:
30
04
2021
pubmed:
6
5
2021
medline:
1
4
2022
entrez:
5
5
2021
Statut:
ppublish
Résumé
Accurate prognostic stratification of patients with oropharyngeal squamous cell carcinoma (OPSCC) is crucial. We developed an objective and robust deep learning-based fully-automated tool called the DeepPET-OPSCC biomarker for predicting overall survival (OS) in OPSCC using [ The DeepPET-OPSCC prediction model was built and tested internally on a discovery cohort ( After adjustment for potential confounders, DeepPET-OPSCC was found to be an independent predictor of OS in both discovery and TCIA test cohorts [HR = 2.07; 95% confidence interval (CI), 1.31-3.28 and HR = 2.39; 95% CI, 1.38-4.16; both DeepPET-OPSCC achieved an accurate OS prediction in patients with OPSCC and enabled an objective, unbiased, and rapid assessment for OPSCC prognostication.
Identifiants
pubmed: 33947697
pii: 1078-0432.CCR-20-4935
doi: 10.1158/1078-0432.CCR-20-4935
doi:
Substances chimiques
Radiopharmaceuticals
0
Fluorodeoxyglucose F18
0Z5B2CJX4D
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
3948-3959Informations de copyright
©2021 American Association for Cancer Research.
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