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
5544 - Evaluation of a radiomic signature of CD8 cells in patients treated with immunotherapy-radiotherapy in three clinical trials.
Presenter: Roger Sun
Session: Poster Display session 3
Resources:
Abstract
1117 - Biomarkers predictive of overall survival in advanced cancer patients treated with a peptide-based cancer vaccine.
Presenter: Shigetaka Suekane
Session: Poster Display session 3
Resources:
Abstract
1922 - Expression of PD-L1 in plasma exosomes of NSCLC patients and its associations with PD-L1 expression of corresponding tumor tissues
Presenter: Shaorong Yu
Session: Poster Display session 3
Resources:
Abstract
5495 - Patient’s perspective on digital biomarkers in advanced urologic malignancies
Presenter: Severin Rodler
Session: Poster Display session 3
Resources:
Abstract
3166 - A comprehensive Pan-cancer study of FGFR Aberrations in Chinese cancer patients
Presenter: Yang Gao
Session: Poster Display session 3
Resources:
Abstract
3277 - A Systemic Inflammation Response Index (SIRI) correlates with survival and could be a Predictive Factor for mFOLFIRINOX in Metastatic Pancreatic Cancer (PC)
Presenter: Vilma Pacheco-Barcia
Session: Poster Display session 3
Resources:
Abstract
2680 - Circulating biomarkers and risk of immune-related adverse events (irAEs) in patients (pts) with advanced Non-small cell lung cancer (aNSCLC) and metastatic melanoma (mMel)
Presenter: Alberto Pavan
Session: Poster Display session 3
Resources:
Abstract
4066 - Breast cancer in young women of Kazakh population depending on germline mutations: results of next-generation sequencing
Presenter: Dilyara Kaidarova
Session: Poster Display session 3
Resources:
Abstract
5514 - Discovery of an ImmunoTranscriptomics signature in blood for early colorectal cancer detection
Presenter: Paolo Angelino
Session: Poster Display session 3
Resources:
Abstract
1595 - Serum Netrin-1 as a Biomarker for Colorectal Cancer Detection
Presenter: Jinzhou Zhu
Session: Poster Display session 3
Resources:
Abstract