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

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.

Risk assessment of osteoradionecrosis associated with periodontitis using 18F-FDG PET/CT.

PURPOSE: Osteoradionecrosis (ORN) is a serious complication after radiotherapy (RT), even in the era of intensity modulated radiation therapy (IMRT). The purpose of this study was to evaluate whether 18F-FDG PET/CT can predict ORN associated with periodontal disease in patients with oropharyngeal or oral cavity squamous cell carcinoma (OP/OC SCC) undergoing RT. METHODS: One hundred and five OP/OC SCC patients treated with RT who underwent pretreatment 18F-FDG PET/CT between October 2007 and June 2016 were retrospectively reviewed. A post-treatment diagnosis of ORN was made clinically based on presence of exposed irradiated mandibular bone that failed to heal after a period of three months without persistent or recurrent tumor. The maximum standardized uptake value (SUVmax) of periodontal regions identified on PET/CT was measured for all patients. Image-based staging of periodontitis was also performed using American Academy of Periodontology staging system on CT. RESULTS: Among 105 patients, 14 (13.3 %) developed ORN. The SUVmax of the periodontal region in patients with ORN (3.35 ±â€¯1.23) was significantly higher than patients without ORN (1.92 ±â€¯0.66) (P <  .01). The corresponding CT stage of periodontitis in patients with ORN was significantly higher (2.71±0.47) than patients without ORN (1.80±0.73) (P <  .01). ROC analysis revealed the cut-off values of developing ORN were 2.1 in SUVmax, and II in CT stage of periodontitis. The corresponding AUC was 0.86 and 0.82, respectively. CONCLUSIONS: Pretreatment 18F-FDG PET/CT identification of periodontitis may be helpful to predict the future development of ORN in patients with OP/OC SCC undergoing RT.

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

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.

Prediction of the human papillomavirus status in patients with oropharyngeal squamous cell carcinoma by FDG-PET imaging dataset using deep learning analysis: A hypothesis-generating study.

PURPOSE: To assess the diagnostic accuracy of imaging-based deep learning analysis to differentiate between human papillomavirus (HPV) positive and negative oropharyngeal squamous cell carcinomas (OPSCCs) using FDG-PET images. METHODS: One hundred and twenty patients with OPSCC who underwent pretreatment FDG-PET/CT were included and divided into the training 90 patients and validation 30 patients cohorts. In the training session, 2160 FDG-PET images were analyzed after data augmentation process by a deep learning technique to create a diagnostic model to discriminate between HPV-positive and HPV-negative OPSCCs. Validation cohort data were subsequently analyzed for confirmation of diagnostic accuracy in determining HPV status by the deep learning-based diagnosis model. In addition, two radiologists evaluated the validation cohort image-data to determine the HPV status based on each tumor's imaging findings. RESULTS: In deep learning analysis with training session, the diagnostic model using training dataset was successfully created. In the validation session, the deep learning diagnostic model revealed sensitivity of 0.83, specificity of 0.83, positive predictive value of 0.88, negative predictive value of 0.77, and diagnostic accuracy of 0.83, while the visual assessment by two radiologists revealed 0.78, 0.5, 0.7, 0.6, and 0.67 (reader 1), and 0.56, 0.67, 0.71, 0.5, and 0.6 (reader 2), respectively. Chi square test showed a significant difference between deep learning- and radiologist-based diagnostic accuracy (reader 1: P = 0.016, reader 2: P = 0.008). CONCLUSIONS: Deep learning diagnostic model with FDG-PET imaging data can be useful as one of supportive tools to determine the HPV status in patients with OPSCC.

Noninvasive mapping of regional response to segmental allergen challenge using magnetic resonance imaging and [F-18]fluorodeoxyglucose positron emission tomography.

Magnetic resonance (MR) and positron emission tomography (PET) imaging techniques were coregistered to demonstrate regional ventilation and inflammation in the lung for in vivo, noninvasive evaluation of regional lung function associated with allergic inflammation. Four Brown Norway rats were imaged pre- and post segmental allergen challenge using respiratory-gated He-3 magnetic resonance imaging (MRI) to visualize ventilation, T(1)-weighted proton MRI to depict inflammatory infiltrate, and [F-18]fluorodeoxyglucose-PET to detect regional glucose metabolism by inflammatory cells. Segmental allergen challenges were delivered and the pre- and postchallenge lung as well as the contralateral lung were compared. Coregistration of the imaging results demonstrated that regions of ventilation defects, inflammatory infiltrate, and increased glucose metabolism correlated well with the site of allergen challenge delivery and inflammatory cell recruitment, as confirmed by histology. This method demonstrates that fusion of functional and anatomic PET and MRI image data may be useful to elucidate the functional correlates of inflammatory processes in the lungs.