Purpose. In this study, we proposed an automated deep learning (DL) method for head and neck cancer (HNC) gross tumor volume (GTV) contouring on positron emission tomography-computed tomography (PET-CT) images. Materials and Methods. PET-CT images were collected from 22 newly diagnosed HNC patients, of whom 17 (Database 1) and 5 (Database 2) were from two centers, respectively. An oncologist and a radiologist decided the gold standard of GTV manually by consensus. We developed a deep convolutional neural network (DCNN) and trained the network based on the two-dimensional PET-CT images and the gold standard of GTV in the training dataset. We did two experiments: Experiment 1, with Database 1 only, and Experiment 2, with both Databases 1 and 2. In both Experiment 1 and Experiment 2, we evaluated the proposed method using a leave-one-out cross-validation strategy. We compared the median results in Experiment 2 (GTVa) with the performance of other methods in the literature and with the gold standard (GTVm). Results. A tumor segmentation task for a patient on coregistered PET-CT images took less than one minute. The dice similarity coefficient (DSC) of the proposed method in Experiment 1 and Experiment 2 was 0.481∼0.872 and 0.482∼0.868, respectively. The DSC of GTVa was better than that in previous studies. A high correlation was found between GTVa and GTVm (R = 0.99, P<0.001). The median volume difference (%) between GTVm and GTVa was 10.9%. The median values of DSC, sensitivity, and precision of GTVa were 0.785, 0.764, and 0.789, respectively. Conclusion. A fully automatic GTV contouring method for HNC based on DCNN and PET-CT from dual centers has been successfully proposed with high accuracy and efficiency. Our proposed method is of help to the clinicians in HNC management.

Introduction: The pathological grading of pancreatic neuroendocrine neoplasms (pNENs) is an independent predictor of survival and indicator for treatment. Deep learning (DL) with a convolutional neural network (CNN) may improve the preoperative prediction of pNEN grading. Methods: Ninety-three pNEN patients with preoperative contrast-enhanced computed tomography (CECT) from Hospital I were retrospectively enrolled. A CNN-based DL algorithm was applied to the CECT images to obtain 3 models (arterial, venous, and arterial/venous models), the performances of which were evaluated via an eightfold cross-validation technique. The CECT images of the optimal phase were used for comparing the DL and traditional machine learning (TML) models in predicting the pathological grading of pNENs. The performance of radiologists by using qualitative and quantitative computed tomography findings was also evaluated. The best DL model from the eightfold cross-validation was evaluated on an independent testing set of 19 patients from Hospital II who were scanned on a different scanner. The Kaplan-Meier (KM) analysis was employed for survival analysis. Results: The area under the curve (AUC; 0.81) of arterial phase in validation set was significantly higher than those of venous (AUC 0.57, p = 0.03) and arterial/venous phase (AUC 0.70, p = 0.03) in predicting the pathological grading of pNENs. Compared with the TML models, the DL model gave a higher (although insignificantly) AUC. The highest OR was achieved for the p ratio <0.9, the AUC and accuracy for diagnosing G3 pNENs were 0.80 and 79.1% respectively. The DL algorithm achieved an AUC of 0.82 and an accuracy of 88.1% for the independent testing set. The KM analysis showed a statistical significant difference between the predicted G1/2 and G3 groups in the progression-free survival (p = 0.001) and overall survival (p < 0.001). Conclusion: The CNN-based DL method showed a relatively robust performance in predicting pathological grading of pNENs from CECT images.

Background.: Converging evidence has revealed both functional and structural abnormalities in adolescents with early-onset conduct disorder (EO-CD). The neurological abnormalities underlying EO-CD may be different from that of adolescent-onset conduct disorder (AO-CD) patients. However, the cortical structure in AO-CD patients remains largely unknown. The aim of the present study was to investigate the cortical alterations in AO-CD patients.Method.: We investigated T1-weighted brain images from AO-CD patients and age-, gender- and intelligence quotient-matched controls. Cortical structures including thickness, folding and surface area were measured using the surface-based morphometric method. Furthermore, we assessed impulsivity and antisocial symptoms using the Barratt Impulsiveness Scale (BIS) and the Antisocial Process Screening Device (APSD).Results.: Compared with the controls, we found significant cortical thinning in the paralimbic system in AO-CD patients. For the first time, we observed cortical thinning in the precuneus/posterior cingulate cortex (PCC) in AO-CD patients which has not been reported in EO-CD patients. Prominent folding abnormalities were found in the paralimbic structures and frontal cortex while diminished surface areas were shown in the precentral and inferior temporal cortex. Furthermore, cortical thickness of the paralimbic structures was found to be negatively correlated with impulsivity and antisocial behaviors measured by the BIS and APSD, respectively.Conclusions.: The present study indicates that AO-CD is characterized by cortical structural abnormalities in the paralimbic system, and, in particular, we highlight the potential role of deficient structures including the precuneus and PCC in the etiology of AO-CD.