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Multi-Parametric Deep Learning Model for Prediction of Overall Survival after Postoperative Concurrent Chemoradiotherapy in Glioblastoma Patients
- Source :
- Cancers, Cancers; Volume 12; Issue 8; Pages: 2284, Cancers, Vol 12, Iss 2284, p 2284 (2020)
- Publication Year :
- 2020
-
Abstract
- This study aimed to investigate the performance of a deep learning-based survival-prediction model, which predicts the overall survival (OS) time of glioblastoma patients who have received surgery followed by concurrent chemoradiotherapy (CCRT). The medical records of glioblastoma patients who had received surgery and CCRT between January 2011 and December 2017 were retrospectively reviewed. Based on our inclusion criteria, 118 patients were selected and semi-randomly allocated to training and test datasets (3:1 ratio, respectively). A convolutional neural network–based deep learning model was trained with magnetic resonance imaging (MRI) data and clinical profiles to predict OS. The MRI was reconstructed by using four pulse sequences (22 slices) and nine images were selected based on the longest slice of glioblastoma by a physician for each pulse sequence. The clinical profiles consist of personal, genetic, and treatment factors. The concordance index (C-index) and integrated area under the curve (iAUC) of the time-dependent area-under-the-curve curves of each model were calculated to evaluate the performance of the survival-prediction models. The model that incorporated clinical and radiomic features showed a higher C-index (0.768 (95% confidence interval (CI): 0.759, 0.776)) and iAUC (0.790 (95% CI: 0.783, 0.797)) than the model using clinical features alone (C-index = 0.693 (95% CI: 0.685, 0.701); iAUC = 0.723 (95% CI: 0.716, 0.731)) and the model using radiomic features alone (C-index = 0.590 (95% CI: 0.579, 0.600); iAUC = 0.614 (95% CI: 0.607, 0.621)). These improvements to the C-indexes and iAUCs were validated using the 1000-times bootstrapping method; all were statistically significant (p < 0.001). This study suggests the synergistic benefits of using both clinical and radiomic parameters. Furthermore, it indicates the potential of multi-parametric deep learning models for the survival prediction of glioblastoma patients.
- Subjects :
- Cancer Research
medicine.medical_specialty
lcsh:RC254-282
Article
030218 nuclear medicine & medical imaging
03 medical and health sciences
0302 clinical medicine
Overall survival
medicine
survival prediction
Multi parametric
medicine.diagnostic_test
business.industry
Deep learning
Area under the curve
glioblastoma
deep learning
Magnetic resonance imaging
medicine.disease
lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens
radiomics
Confidence interval
Concurrent chemoradiotherapy
Oncology
030220 oncology & carcinogenesis
Artificial intelligence
Radiology
business
Glioblastoma
Subjects
Details
- ISSN :
- 20726694
- Volume :
- 12
- Issue :
- 8
- Database :
- OpenAIRE
- Journal :
- Cancers
- Accession number :
- edsair.doi.dedup.....5c313e6fb8b894ed05f14b3710f96e37