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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.

Background: Olfactory neuroblastoma (ONB) is a rare tumor originating from olfactory epithelium. Here we retrospectively analyzed the long-term treatment outcomes and toxicity of radiotherapy for ONB patients for whom computed tomography (CT) and three-dimensional treatment planning was conducted to reappraise the role of radiotherapy in the light of recent advanced technology and chemotherapy. Methods: Seventeen patients with ONB treated between July 1992 and June 2013 were included. Three patients were Kadish stage B and 14 were stage C. All patients were treated with radiotherapy with or without surgery or chemotherapy. The radiation dose was distributed from 50 Gy to 66 Gy except for one patient who received 40 Gy preoperatively. Results: The median follow-up time was 95 months (range 8-173 months). The 5-year overall survival (OS) and relapse-free survival (RFS) rates were estimated at 88% and 74%, respectively. Five patients with stage C disease had recurrence with the median time to recurrence of 59 months (range 7-115 months). Late adverse events equal to or above Grade 2 in CTCAE v4.03 were observed in three patients. Conclusion: Multimodal therapy including radiotherapy with precise treatment planning based on CT simulation achieved an excellent local control rate with acceptable toxicity and reasonable overall survival for patients with ONB.