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
3290 - Identification of meningioma patients in high risk of tumor recurrence using microRNA profiling
Presenter: Josef Srovnal
Session: Poster Display session 3
Resources:
Abstract
2477 - Antecedent of cancer and mortality after the first ST segment elevation acute myocardial infarction treated with primary coronary angioplasty. A prospective cohort study
Presenter: Irene Sillero
Session: Poster Display session 3
Resources:
Abstract
1894 - Genomic characterisation of locally advanced pancreatic adenocarcinoma
Presenter: Sarah Picardo
Session: Poster Display session 3
Resources:
Abstract
3280 - Comparison of freshly prepared and frozen cells from colorectal cancer surgical samples for phenotyping experiments- a pilot study
Presenter: Sandra Mersakova
Session: Poster Display session 3
Resources:
Abstract
3419 - Hyaluronan (HA) Accumulation in the Tumor Microenvironment (TME) is Increased in Colorectal Cancer (CRC) and Associated with Consensus Molecular Subtypes (CMS) 4 Molecular Subtype
Presenter: Barbara Blouw
Session: Poster Display session 3
Resources:
Abstract
1833 - Evaluation of CT-based radiomics in patients with renal cell carcinoma
Presenter: An Zhao
Session: Poster Display session 3
Resources:
Abstract
5883 - Detection of Double Protein Expression in Diffuse Large B Cell Lymphoma
Presenter: Mohamed Gouda
Session: Poster Display session 3
Resources:
Abstract
5415 - Encyclopedic Tumor Analysis for organ agnostic treatment with Axitinib in combination regimens for advanced cancers
Presenter: Tim Crook
Session: Poster Display session 3
Resources:
Abstract
3297 - Computational model to predict response rate of clinical trials
Presenter: Orsolya Lorincz
Session: Poster Display session 3
Resources:
Abstract
4355 - Analysis of BRCA genes and homologous recombination deficiency (HRD) scores in tumours from patients (pts) with metastatic breast cancer (mBC) in the OlympiAD trial
Presenter: Mark Robson
Session: Poster Display session 3
Resources:
Abstract