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European Radiology
Published in: European Radiology 11/2020

01-11-2020 | Positron Emission Tomography | Head and Neck

Deep learning analysis using FDG-PET to predict treatment outcome in patients with oral cavity squamous cell carcinoma

Authors: Noriyuki Fujima, V. Carlota Andreu-Arasa, Sara K. Meibom, Gustavo A. Mercier, Andrew R. Salama, Minh Tam Truong, Osamu Sakai

Published in: European Radiology | Issue 11/2020

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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.
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Metadata
Title
Deep learning analysis using FDG-PET to predict treatment outcome in patients with oral cavity squamous cell carcinoma
Authors
Noriyuki Fujima
V. Carlota Andreu-Arasa
Sara K. Meibom
Gustavo A. Mercier
Andrew R. Salama
Minh Tam Truong
Osamu Sakai
Publication date
01-11-2020
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 11/2020
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-020-06982-8

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