METHODS: Positron emission tomography (PET) and computed tomography (CT) image data from 97 patients with LC and 77 patients with TB nodules were collected. One hundred radiomic features were extracted from both PET and CT imaging using the pyradiomics platform, and 2048 deep learning features were obtained through a residual neural network approach. Four models included traditional machine learning model with radiomic features as input (traditional radiomics), a deep learning model with separate input of image features (deep convolutional neural networks [DCNN]), a deep learning model with two inputs of radiomic features and deep learning features (radiomics-DCNN) and a deep learning model with inputs of radiomic features and deep learning features and clinical information (integrated model). The models were evaluated using area under the curve (AUC), sensitivity, accuracy, specificity, and F1-score metrics.

RESULTS: The results of the classification of TB nodules and LC showed that the integrated model achieved an AUC of 0.84 (0.82-0.88), sensitivity of 0.85 (0.80-0.88), and specificity of 0.84 (0.83-0.87), performing better than the other models.

CASE PRESENTATION: A young man involved in a car accident and sustained blunt thoracic injuries, among others. As part of primary survey, FAST scan was performed. Subxiphoid view to look for evidence of pericardial effusion showed part of the cardiac image obscured by A-lines. Other cardiac windows showed only A-lines, as well. A suspicion of pneumopericardium was raised and CT scan confirmed the diagnosis.

CASE PRESENTATION: We report a case series that illustrate the diagnostic value of focused airway ultrasound in the diagnosis of laryngeal trauma in patients presenting with blunt neck injury.

PURPOSE: To evaluate the accuracy, safety, and diagnostic outcome of fluoroscopic guided and CT transpedicular biopsy techniques.

STUDY DESIGN: Prospective randomized trial.

PATIENT SAMPLE: Sixty consecutive patients with clinical symptoms and radiological features suggestive of spinal infection or malignancy were recruited and randomized into fluoroscopic or CT guided spinal biopsy groups. Both groups were similar in terms of patient demographics, distribution of spinal infections and malignancy cases, and the level of biopsies.

OUTCOME MEASURES: The primary outcome measure was diagnostic accuracy of both methods, determined based on true positive, true negative, false positive, and false negative biopsy findings. Secondary outcome measures included radiation exposure to patients and doctors, complications, and postbiopsy pain score.

METHODS: A transpedicular approach was performed with an 8G core biopsy needle. Specimens were sent for histopathological and microbiological examinations. Diagnosis was made based on biopsy results, clinical criteria and monitoring of disease progression during a 6-month follow up duration. Clinical criteria included presence of risk factors, level of inflammatory markers and magnetic resonance imaging findings. Radiation exposure to patients and doctors was measured with dosimeters.

RESULTS: There was no significant difference between the diagnostic accuracy of fluoroscopic and CT guided spinal biopsy (p=0.67) or between the diagnostic accuracy of spinal infection and spinal tumor in both groups (p=0.402 for fluoroscopy group and p=0.223 for CT group). Radiation exposure to patients was approximately 26 times higher in the CT group. Radiation exposure to doctors in the CT group was approximately 2 times higher compared to the fluoroscopic group if a lead shield was not used. Lead shields significantly reduced radiation exposure to doctors anywhere from 2 to 8 times. No complications were observed for either group and the differences in postbiopsy pain scores were not significant.