Abstract 3607
Background
We aimed to establish a prognostic model based on magnetic resonance imaging using deep learning to predict disease-free survival in patients with non-metastatic nasopharyngeal carcinoma.
Methods
In this retrospective, cohort study, we included 1636 patients who were diagnosed with non-metastatic nasopharyngeal carcinoma and underwent radical treatment at the Sun Yat-sen University Cancer Center. Patients from October 2010 to March 2015 were randomly divided into training cohort (n = 878) and validation cohort (n = 376); 382 patients from April 2015 to September 2015 were separated as test cohort. 3D DenseNet models learned deep representations of pre-treatment MRI and risk scores were extracted to predict PFS in the training cohort. We evaluated the accuracy of the prognostic model in validation and test cohorts. The primary endpoint was DFS, and the secondary endpoint was distant metastasis-free survival (DMFS).
Results
A series of risk scores for each patient were extracted from 3D DenseNet models, and an optimal cut-off value of risk scores was generated to classify patients into low-risk and high-risk group in the training cohort. Patients with low-risk scores had better DFS (hazard ratio [HR] 0.62, 95% CI 0.55 -0.70; p < 0.0001) and DMFS (HR 0.62, 95% CI 0.48 -0.81; p < 0.0003) than patients with low-risk scores. And we validated the prognostic accuracy of risk scores in the validation and test cohorts. In addition, patients who received concurrent chemotherapy had a poorer DFS (hazard ratio [HR] 7.79, 95% CI 1.08 -56.00; p < 0.041) compared with those who did not receive concurrent chemotherapy in low-risk group, meanwhile, patients with or without concurrent chemotherapy had similar outcomes in the high-risk group (HR 2.39, 95% CI 0.59 -9.62; p = 0.22). We also developed a nomogram based on risk scores and several clinical factors that predicted an individual’s risk of DFS.
Conclusions
MRI-based 3D DenseNet models are effective tools to learn deep representations and extract risk scores of DFS. Risk scores can be reliable prognostic factors to select which patients benefit from concurrent chemotherapy.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
The authors.
Funding
The National Natural Science Foundation of China.
Disclosure
All authors have declared no conflicts of interest.
Resources from the same session
5877 - Efficacy of anti-PD(L)1 treatment in patients with metastatic urothelial cancer based on mRNA- and protein- based PD-L1 determination: Results from the multicentric, retrospective FOsMIC trial
Presenter: Jonas Jarczyk
Session: Poster Display session 3
Resources:
Abstract
5204 - A differential bladder microbiota composition is associated with tumor grade in bladder cancer.
Presenter: Monica Parra-Grande
Session: Poster Display session 3
Resources:
Abstract
4904 - Molecular characterization of metastatic urothelial carcinoma (mUC) in prior or current smokers (PCS) vs non-smokers (NS)
Presenter: Victor Sacristan Santos
Session: Poster Display session 3
Resources:
Abstract
5370 - Evaluation of different diagnostic methods for identification of FGFR alteration in advanced urothelial carcinomas: Proficiency Results based on multiple RNA extraction kits and mutation detection methods
Presenter: Veronika Weyerer
Session: Poster Display session 3
Resources:
Abstract
2579 - Title: Genomic characterization of non-schistosomiasis-related squamous cell carcinoma (NSR-SCC) of the urinary bladder: a retrospective study of potential prognostic and predictive biomarkers
Presenter: Esmail Al-ezzi
Session: Poster Display session 3
Resources:
Abstract
2203 - TiNivo: Tivozanib combined with nivolumab results in prolonged progression free survival in patients with metastatic renal cell carcinoma (mRCC). Final Results.
Presenter: Philippe Barthelemy
Session: Poster Display session 3
Resources:
Abstract
4712 - First-Line Pembrolizumab (pembro) Monotherapy for Advanced Non‒Clear Cell Renal Cell Carcinoma (nccRCC): Updated Follow-Up for KEYNOTE-427 Cohort B
Presenter: Cristina Suárez
Session: Poster Display session 3
Resources:
Abstract
2091 - First-Line Pembrolizumab (pembro) Monotherapy in Advanced Clear Cell Renal Cell Carcinoma (ccRCC): Updated Follow-Up For KEYNOTE-427 Cohort A
Presenter: James Larkin
Session: Poster Display session 3
Resources:
Abstract
2368 - Association Between Depth of Response and Overall Survival: Exploratory Analysis in Patients With Previously Untreated Advanced Renal Cell Carcinoma (aRCC) in CheckMate 214
Presenter: Viktor Grünwald
Session: Poster Display session 3
Resources:
Abstract
6008 - Quality of life in previously untreated patients with advanced renal cell carcinoma (aRCC) in CheckMate 214: updated results
Presenter: David Cella
Session: Poster Display session 3
Resources:
Abstract