A deep learning risk prediction model for overall survival in patients with gastric cancer: A multicenter study.
Computed tomography
Deep learning
Gastric cancer
Individualized treatment
Overall survival
Journal
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
ISSN: 1879-0887
Titre abrégé: Radiother Oncol
Pays: Ireland
ID NLM: 8407192
Informations de publication
Date de publication:
09 2020
09 2020
Historique:
received:
05
01
2020
revised:
08
06
2020
accepted:
09
06
2020
pubmed:
17
6
2020
medline:
15
4
2021
entrez:
17
6
2020
Statut:
ppublish
Résumé
Risk prediction of overall survival (OS) is crucial for gastric cancer (GC) patients to assess the treatment programs and may guide personalized medicine. A novel deep learning (DL) model was proposed to predict the risk for OS based on computed tomography (CT) images. We retrospectively collected 640 patients from three independent centers, which were divided into a training cohort (center 1 and center 2, n = 518) and an external validation cohort (center 3, n = 122). We developed a DL model based on the architecture of residual convolutional neural network. We augmented the size of training dataset by image transformations to avoid overfitting. We also developed radiomics and clinical models for comparison. The performance of the three models were comprehensively assessed. Totally 518 patients were prepared by data augmentation and fed into DL model. The trained DL model significantly classified patients into high-risk and low-risk groups in training cohort (P-value <0.001, concordance index (C-index): 0.82, hazard ratio (HR): 9.79) and external validation cohort (P-value <0.001, C-index: 0.78, HR: 11.76). Radiomics model was developed with selected 24 features and clinical model was developed with three significant clinical variables (P-value <0.05). The comparison illustrated DL model had the best performance for risk prediction of OS according to the C-index (training: DL vs Clinical vs Radiomics = 0.82 vs 0.73 vs 0.66; external validation: 0.78 vs 0.71 vs 0.72). The DL model is a powerful model for risk assessment, and potentially serves as an individualized recommender for decision-making in GC patients.
Sections du résumé
BACKGROUND AND PURPOSE
Risk prediction of overall survival (OS) is crucial for gastric cancer (GC) patients to assess the treatment programs and may guide personalized medicine. A novel deep learning (DL) model was proposed to predict the risk for OS based on computed tomography (CT) images.
MATERIALS AND METHODS
We retrospectively collected 640 patients from three independent centers, which were divided into a training cohort (center 1 and center 2, n = 518) and an external validation cohort (center 3, n = 122). We developed a DL model based on the architecture of residual convolutional neural network. We augmented the size of training dataset by image transformations to avoid overfitting. We also developed radiomics and clinical models for comparison. The performance of the three models were comprehensively assessed.
RESULTS
Totally 518 patients were prepared by data augmentation and fed into DL model. The trained DL model significantly classified patients into high-risk and low-risk groups in training cohort (P-value <0.001, concordance index (C-index): 0.82, hazard ratio (HR): 9.79) and external validation cohort (P-value <0.001, C-index: 0.78, HR: 11.76). Radiomics model was developed with selected 24 features and clinical model was developed with three significant clinical variables (P-value <0.05). The comparison illustrated DL model had the best performance for risk prediction of OS according to the C-index (training: DL vs Clinical vs Radiomics = 0.82 vs 0.73 vs 0.66; external validation: 0.78 vs 0.71 vs 0.72).
CONCLUSION
The DL model is a powerful model for risk assessment, and potentially serves as an individualized recommender for decision-making in GC patients.
Identifiants
pubmed: 32540334
pii: S0167-8140(20)30333-9
doi: 10.1016/j.radonc.2020.06.010
pii:
doi:
Types de publication
Journal Article
Multicenter Study
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
73-80Informations de copyright
Copyright © 2020 Elsevier B.V. All rights reserved.