Prediction of the local treatment outcome in patients with oropharyngeal squamous cell carcinoma using deep learning analysis of pretreatment FDG-PET images

• Published in: BMC cancer Vol. 21; no. 1; pp. 1 - 13
• Main Authors: Fujima, Noriyuki, Andreu-Arasa, V. Carlota, Meibom, Sara K, Mercier, Gustavo A, Truong, Minh Tam, Hirata, Kenji, Yasuda, Koichi, Kano, Satoshi, Homma, Akihiro, Kudo, Kohsuke, Sakai, Osamu
• Format: Journal Article
• Language: English
• Published: London BioMed Central Ltd 06-08-2021; BioMed Central; BMC
• Subjects: Biopsy; Care and treatment; Chemotherapy; Clinical outcomes; Datasets; Deep learning; Diagnosis; FDG-PET; Machine learning; Medical imaging; Medical prognosis; Medical records; Morphology; Oropharyngeal squamous cell carcinoma; Patients; PET imaging; Positron emission tomography; Radiation therapy; Scanners; Squamous cell carcinoma; Throat cancer; Treatment outcome; United States
• ISSN: 1471-2407; 1471-2407
• DOI: 10.1186/s12885-021-08599-6

Background This study aimed to assess the utility of deep learning analysis using pretreatment FDG-PET images to predict local treatment outcome in oropharyngeal squamous cell carcinoma (OPSCC) patients. Methods One hundred fifty-four OPSCC patients who received pretreatment FDG-PET were included and divided into training (n = 102) and test (n = 52) sets. The diagnosis of local failure and local progression-free survival (PFS) rates were obtained from patient medical records. In deep learning analyses, axial and coronal images were assessed by three different architectures (AlexNet, GoogLeNET, and ResNet). In the training set, FDG-PET images were analyzed after the data augmentation process for the diagnostic model creation. A multivariate clinical model was also created using a binomial logistic regression model from a patient's clinical characteristics. The test data set was subsequently analyzed for confirmation of diagnostic accuracy. Assessment of local PFS rates was also performed. Results Training sessions were successfully performed with an accuracy of 74-89%. ROC curve analyses revealed an AUC of 0.61-0.85 by the deep learning model in the test set, whereas it was 0.62 by T-stage, 0.59 by clinical stage, and 0.74 by a multivariate clinical model. The highest AUC (0.85) was obtained with deep learning analysis of ResNet architecture. Cox proportional hazards regression analysis revealed deep learning-based classification by a multivariate clinical model (P < .05), and ResNet (P < .001) was a significant predictor of the treatment outcome. In the Kaplan-Meier analysis, the deep learning-based classification divided the patient's local PFS rate better than the T-stage, clinical stage, and a multivariate clinical model. Conclusions Deep learning-based diagnostic model with FDG-PET images indicated its possibility to predict local treatment outcomes in OPSCCs. Keywords: Deep learning, Oropharyngeal squamous cell carcinoma, FDG-PET, Treatment outcome