A deep learning-based radiomic nomogram for prognosis and treatment decision in advanced nasopharyngeal carcinoma: A multicentre study

Autor: Shaobo Liang, Di Dong, Lianzhen Zhong, Jie Tian, Xue-Liang Fang, Ning Zhang, Jun Ma, Hong Shan, Runnan Cao, Fan Zhang, Cong Li, Mengjie Fang, Liwen Zhang, Ling-Long Tang, Yujia Liu, Xun Zhao, Zhenhua Hu, Wei Jiang
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Male
Oncology
Medicine (General)
DFS
disease-free survival
Interquartile range
Medicine
Stage (cooking)
pre-EBV DNA
pre-treatment plasma Epstein-Barr virus DNA
ICT
induction chemotherapy
Nasopharyngeal Carcinoma
Hazard ratio
General Medicine
Middle Aged
Female
Research Paper
NPC
nasopharyngeal carcinoma
Adult
medicine.medical_specialty
Adolescent
Radiomic nomogram
Clinical Decision-Making
CCRT
concurrent chemoradiotherapy
C-index
Harrell's concordance index
WHO
World Health Organization
General Biochemistry
Genetics and Molecular Biology
MR
magnetic resonance
Multi-task deep learning
Deep Learning
R5-920
Internal medicine
MCox
multivariate Cox proportional hazards regression
Humans
IQR
interquartile range
Survival analysis
Aged
business.industry
Radiotherapy Planning
Computer-Assisted
Induction chemotherapy
Nasopharyngeal Neoplasms
Nomogram
medicine.disease
HR
hazard ratio
CPTDN
Combined prognosis and treatment decision nomogram
Nomograms
Regimen
Treatment decision
Nasopharyngeal carcinoma
Advanced nasopharyngeal carcinoma
business
Zdroj: EBioMedicine, Vol 70, Iss, Pp 103522-(2021)
EBioMedicine
ISSN: 2352-3964
Popis: Background: Induction chemotherapy (ICT) plus concurrent chemoradiotherapy (CCRT) and CCRT alone were the optional treatment regimens in locoregionally advanced nasopharyngeal carcinoma (NPC) patients. Currently, the choice of them remains equivocal in clinical practice. We aimed to develop a deep learning-based model for treatment decision in NPC. Methods: A total of 1872 patients with stage T3N1M0 NPC were enrolled from four Chinese centres and received either ICT+CCRT or CCRT. A nomogram was constructed for predicting the prognosis of patients with different treatment regimens using multi-task deep learning radiomics and pre-treatment MR images, based on which an optimal treatment regimen was recommended. Model performance was assessed by the concordance index (C-index) and the Kaplan-Meier estimator. Findings: The nomogram showed excellent prognostic ability for disease-free survival in both the CCRT (C-index range: 0.888-0.921) and ICT+CCRT (C-index range: 0.784-0.830) groups. According to the prognostic difference between treatments using the nomogram, patients were divided into the ICT-preferred and CCRT-preferred groups. In the ICT-preferred group, patients receiving ICT+CCRT exhibited prolonged survival over those receiving CCRT in the internal and external test cohorts (hazard ratio [HR]: 0.17, p
Databáze: OpenAIRE
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