Context The frequency of adrenal incidentalomas and their association with comorbid conditions have been assessed mostly in retrospective studies that may be prone to ascertainment bias. Objective The objective of this work is to evaluate the frequency of adrenal incidentalomas and their associated comorbid conditions. Design A prospective cohort study was conducted. Setting This study took place at a radiology department at a public hospital. Participants Unselected outpatients who underwent an abdominal computed tomography (CT) from January 2017 to June 2018. Patients with known or suspected adrenal disease or malignancy were excluded. Exposure All abdominal CT scans were evaluated by an experienced radiologist. Hormonal workup including a 1-mg dexamethasone suppression test was performed in patients bearing adrenal incidentalomas. Main Outcome and Measure Frequency of adrenal incidentalomas in abdominal CT of unselected patients; frequency of comorbid conditions, and hormonal workup in patients bearing adrenal incidentalomas. Results We recruited 601 patients, and in 7.3% of them an adrenal tumor was found serendipitously. The patients bearing an adrenal incidentaloma had higher body mass index (P = .009) and waist circumference (P = .004) and were more frequently diabetic (P = .0038). At multivariable regression analysis, diabetes was significantly associated with the presence of adrenal incidentalomas (P = .003). Autonomous cortisol secretion was observed in 50% of patients who did not suppress cortisol less than 50 nmol/L after 1 mg dexamethasone. Conclusions The frequency of adrenal incidentalomas is higher than previously reported. Moreover, adrenal incidentalomas are tied to increased risk of type 2 diabetes. This finding is free from ascertainment bias because patients with adrenal incidentalomas were drawn from a prospective cohort with the same risk of diabetes as the background population.

The detection rate of noncalcified lung nodules for tomosynthesis was comparable with rates reported for CT. A small subgroup underwent low-dosage CT and entered a follow-up program. Overall, lung cancer was detected in approximately 1% of cases. Digital chest tomosynthesis holds promise as a first-line lung cancer screening tool.

Objectives: within this investigation we investigated several approaches to enhance the positive predictive value (PPV) of chest digital tomosynthesis (DTS) in the lung cancer detection Methods: the investigation was carried out within the SOS clinical trial (NCT03645018) for lung cancer screening with DTS. Lung nodules were identified by visual analysis and then classified using the diameter and the radiological aspect of the nodule following a modified lung-RADS classification.Haralick texture features were extracted from the segmented nodules. Both semantic variables and radiomics features were used to build a predictive model using two approaches: logistic regression model on a sub-set of variables selected with backward feature selection or machine learning using the whole sub-set of variables. We used two machine learning methods: a Random Forest and a neural network. Machine learning methods were applied to a training set and validated on a test set. Methods were compared using diagnostic accuracy metrics.Results: binary visual analysis had a good sensitivity (0.95) but a low PPV (0.14). Lung-RADS classification increased the PPV (0.19) but with an unacceptable low sensitivity (0.65). Analogously, logistic regression showed a mildly increased PPV (0.22) and a low sensitivity (0.67). Random Forest demonstrated a low accuracy with a moderate PPV (0.40) but with a dramatically low sensitivity (0.30). Neural network demonstrated to be the best predictor with a nearly perfect PPV (0.95) and a high sensitivity (0.90).Conclusions: among the various technique to reduce the false positive rates of DTS the neural network demonstrated a very high PPV. The use of visual analysis along with neural network could help radiologists to depict a follow-up strategy after a positive DTS.

Thyroidectomy is a safe procedure often performed either for benign or malignant thyroid diseases. Complication rate is low and tracheal injury associated with thyroidectomy is rarely described. The trachea may be perforated or lacerated intraoperatively; nevertheless, damage is usually recognized and directly repaired with reduced patient morbidity. We review a case of a 45-year-old male with a history of non-invasive thyroid cancer who underwent a total thyroidectomy with a tracheal necrosis and a subsequent rupture presenting 4 days following surgical operation. At home, while coughing, the patient experienced rapid swelling of neck, face and upper part of the chest. Computed tomography scan images demonstrated extensive subcutaneous emphysema and a defect in cervical trachea, confirmed also by bronchoscopy. The patient, without delay, underwent an exploration of the neck with a debridement of laceration. In view of the fact that a local infection was present, only a right pre-thyroid muscle flap was stitched on the defect. The patients recovered uneventfully.

The use of a common classification for nodule detection in DTS and CT helps in comparing the two technologies. DTS detected and correctly classified 74% of the nodules seen by CT but lost 4 nodules identified by CT. Concordance between DTS and CT rose to 86% of the nodules when considering LUNG-RADS on a binary scale.