A deep learning MR-based radiomic nomogram may predict survival for nasopharyngeal carcinoma patients with stage T3N1M0.
Radiother Oncol
; 151: 1-9, 2020 10.
Article
in En
| MEDLINE
| ID: mdl-32634460
ABSTRACT
PURPOSE:
To estimate the prognostic value of deep learning (DL) magnetic resonance (MR)-based radiomics for stage T3N1M0 nasopharyngeal carcinoma (NPC) patients receiving induction chemotherapy (ICT) prior to concurrent chemoradiotherapy (CCRT).METHODS:
A total of 638 stage T3N1M0 NPC patients (training cohort n = 447; test cohort n = 191) were enrolled and underwent MRI scans before receiving ICT + CCRT. From the pretreatment MR images, DL-based radiomic signatures were developed to predict disease-free survival (DFS) in an end-to-end way. Incorporating independent clinical prognostic parameters and radiomic signatures, a radiomic nomogram was built through multivariable Cox proportional hazards method. The discriminative performance of the radiomic nomogram was assessed using the concordance index (C-index) and the Kaplan-Meier estimator.RESULTS:
Three DL-based radiomic signatures were significantly correlated with DFS in the training (C-index 0.695-0.731, all p < 0.001) and test (C-index 0.706-0.755, all p < 0.001) cohorts. Integrating radiomic signatures with clinical factors significantly improved the predictive value compared to the clinical model in the training (C-index 0.771 vs. 0.640, p < 0.001) and test (C-index 0.788 vs. 0.625, p = 0.001) cohorts. Furthermore, risk stratification using the radiomic nomogram demonstrated that the high-risk group exhibited short-lived DFS compared to the low-risk group in the training cohort (hazard ratio [HR] 6.12, p < 0.001), which was validated in the test cohort (HR 6.90, p < 0.001).CONCLUSIONS:
Our DL-based radiomic nomogram may serve as a noninvasive and useful tool for pretreatment prognostic prediction and risk stratification in stage T3N1M0 NPC.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Nasopharyngeal Neoplasms
/
Deep Learning
Type of study:
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
Language:
En
Journal:
Radiother Oncol
Year:
2020
Document type:
Article