Deep learning analysis using FDG-PET to predict treatment outcome in patients with oral cavity squamous cell carcinoma
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HEAD AND NECK
Deep learning analysis using FDG-PET to predict treatment outcome in patients with oral cavity squamous cell carcinoma Noriyuki Fujima 1,2 & V. Carlota Andreu-Arasa 1 & Sara K. Meibom 1 & Gustavo A. Mercier 1 & Andrew R. Salama 3,4 & Minh Tam Truong 5 & Osamu Sakai 1,3,5 Received: 20 April 2020 / Revised: 20 April 2020 / Accepted: 26 May 2020 # European Society of Radiology 2020
Abstract Objective To assess the utility of deep learning analysis using 18F-fluorodeoxyglucose (FDG) uptake by positron emission tomography (PET/CT) to predict disease-free survival (DFS) in patients with oral cavity squamous cell carcinoma (OCSCC). Methods One hundred thirteen patients with OCSCC who received pretreatment FDG-PET/CT were included. They were divided into training (83 patients) and test (30 patients) sets. The diagnosis of treatment control/failure and the DFS rate were obtained from patients’ medical records. In deep learning analyses, three planes of axial, coronal, and sagittal FDG-PET images were assessed by ResNet-101 architecture. In the training set, image analysis was performed for the diagnostic model creation. The test data set was subsequently analyzed for confirmation of diagnostic accuracy. T-stage, clinical stage, and conventional FDG-PET parameters (the maximum and mean standardized uptake value (SUVmax and SUVmean), heterogeneity index, metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were also assessed with determining the optimal cutoff from training dataset and then validated their diagnostic ability from test dataset. Results In dividing into patients with treatment control and failure, the highest diagnostic accuracy of 0.8 was obtained using deep learning classification, with a sensitivity of 0.8, specificity of 0.8, positive predictive value of 0.89, and negative predictive value of 0.67. In the Kaplan-Meier analysis, the DFS rate was significantly different only with the analysis of deep learning– based classification (p < .01). Conclusions Deep learning–based diagnosis with FDG-PET images may predict treatment outcome in patients with OCSCC. Key Points • Deep learning–based diagnosis of FDG-PET images showed the highest diagnostic accuracy to predict the treatment outcome in patients with oral cavity squamous cell carcinoma. • Deep learning–based diagnosis was shown to differentiate patients between good and poor disease-free survival more clearly than conventional T-stage, clinical stage, and conventional FDG-PET-based parameters. Keywords Deep learning . Squamous cell carcinoma of head and neck . Positron emission tomography . Fluorodeoxyglucose F18 . Disease-free survival
Abbreviations AUC DFS DL
FDG-PET/CT Area under curve Disease-free survival Deep learning
* Osamu Sakai [email protected] 1
Department of Radiology, Boston Medical Center, Boston University School of Medicine, FGH Building, 3rd Floor, 820 Harrison Avenue, Boston, MA 02118, USA
2
Research Center for Cooperative Projects, Hokkaido University Graduate School of Medicine, Sapporo, Japan
MTV OCS
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