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Solitary pulmonary nodules (SPNs) measuring 8 to 30 mm in diameter require further workup to determine the likelihood of malignancy.What is the diagnostic performance of a lung cancer prediction convolutional neural network (LCP-CNN) in SPNs using unenhanced and contrast-enhanced CT imaging compared with the current clinical workup?This was a post hoc analysis of the Single Pulmonary Nodule Investigation: Accuracy and Cost-Effectiveness of Dynamic Contrast Enhanced Computed Tomography in the Characterisation of Solitary Pulmonary Nodules trial, a prospective multicenter study comparing the diagnostic accuracy of dynamic contrast-enhanced (DCE) CT imaging with PET imaging in SPNs. The LCP-CNN was designed and validated in an external cohort. LCP-CNN-generated risk scores were created from the noncontrast and contrast-enhanced CT scan images from the DCE CT imaging. The gold standard was histologic analysis or 2 years of follow-up. The area under the receiver operating characteristic curves (AUC) were calculated using LCP-CNN score, maximum standardized uptake value, and DCE CT scan maximum enhancement and were compared using the DeLong test.Two hundred seventy participants (mean ± SD age, 68.3 ± 8.8 years; 49% women) underwent PET with CT scan imaging and DCE CT imaging with CT scan data available centrally for LCP-CNN analysis. The accuracy of the LCP-CNN on the noncontrast images (AUC, 0.83; 95% CI, 0.79-0.88) was superior to that of DCE CT imaging (AUC, 0.76; 95% CI, 0.69-0.82; P = .03) and equal to that of PET with CT scan imaging (AUC, 0.86; 95% CI, 0.81-0.90; P = .35). The presence of contrast resulted in a small reduction in diagnostic accuracy, with the AUC falling from 0.83 (95% CI, 0.79-0.88) on the noncontrast images to 0.80 to 0.83 after contrast (P .05 for 240 s after contrast only).An LCP-CNN algorithm provides an AUC equivalent to PET with CT scan imaging in the diagnosis of solitary pulmonary nodules.ClinicalTrials.gov Identifier; No.: NCT02013063.

We proposed to assess the feasibility of low mechanical index (MI) contrast enhanced ultrasound (CEUS) in the characterisation of thoracic lesions. Fifty patients were prospectively examined by CEUS and images acquired on a low MI (0.17-0.24) setting following injection of SonoVue. From region-of-interest (ROI) generated signal intensity (SI) time curves, the maximum SI, bolus arrival time (BAT), time to peak intensity (TTP), wash-in slope and mean transit time (MTT) were calculated. Using the Wilcoxon rank test; parameters and threshold values for positive differentiation were determined. In addition, for the parameters that allowed positive differentiation between malignant and benign lesions receiver operator curves (ROC) were obtained. The wash-in slope, TTP and MTT (p = 0.0003,0.0001, 0.02) allowed positive differentiation. The sensitivity and specificity was 93% and 78%, with 6.87 s(-1) threshold value for the wash-in slope, 78% and 89% with 11.84 s threshold for the TTP and 48% and 89% with 78.6 s threshold for the MTT. CEUS is a useful tool for differentiating malignant and benign thoracic lesions.