“Because It Kind of Falls in Between, Doesn’t It? Like an Acute Thing and a Chronic”: the Psychological Experience of Anaphylaxis in Adulthood

Anaphylaxis is a serious, rare condition increasing in prevalence. This study explored the psychological experience of adult-onset anaphylaxis from patient, family and staff perspectives. Semi-structured interviews were conducted with twelve participants. Two global themes emerged from thematic analysis: ‘controllability’ (‘an unknown and distressing experience’, ‘the importance of control over triggers’ and ‘responsibility but no control: the impact on others’) and ‘conflict’ (‘rejecting illness identity’, ‘minimisation of risk’, ‘accessing specialist care: running in slow motion’ and ‘patient-centred versus service-centred care’). Findings highlight the importance of perceived control and emphasise the presence of conflict in the experience of this complex, episodic condition

Impact of solitary pulmonary nodule size on qualitative and quantitative assessment using 18F-fluorodeoxyglucose PET/CT: the SPUTNIK trial

Purpose: To compare qualitative and semi-quantitative PET/CT criteria, and the impact of nodule size on the diagnosis of solitary pulmonary nodules in a prospective multicentre trial. / Methods: Patients with an SPN on CT ≥ 8 and ≤ 30 mm were recruited to the SPUTNIK trial at 16 sites accredited by the UK PET Core Lab. Qualitative assessment used a five-point ordinal PET-grade compared to the mediastinal blood pool, and a combined PET/CT grade using the CT features. Semi-quantitative measures included SUVmax of the nodule, and as an uptake ratio to the mediastinal blood pool (SURBLOOD) or liver (SURLIVER). The endpoints were diagnosis of lung cancer via biopsy/histology or completion of 2-year follow-up. Impact of nodule size was analysed by comparison between nodule size tertiles. / Results: Three hundred fifty-five participants completed PET/CT and 2-year follow-up, with 59% (209/355) malignant nodules. The AUCs of the three techniques were SUVmax 0.87 (95% CI 0.83;0.91); SURBLOOD 0.87 (95% CI 0.83; 0.91, p = 0.30 versus SUVmax); and SURLIVER 0.87 (95% CI 0.83; 0.91, p = 0.09 vs. SUVmax). The AUCs for all techniques remained stable across size tertiles (p > 0.1 for difference), although the optimal diagnostic threshold varied by size. For nodules  16 mm, an SUVmax ≥ 3.6 or visual PET uptake greater than the mediastinum was the most accurate. / Conclusion: In this multicentre trial, SUVmax was the most accurate technique for the diagnosis of solitary pulmonary nodules. Diagnostic thresholds should be altered according to nodule size. / Trial registration: ISRCTN - ISRCTN30784948. ClinicalTrials.gov - NCT0201306

Diagnostic Accuracy of a Convolutional Neural Network Assessment of Solitary Pulmonary Nodules Compared With PET With CT Imaging and Dynamic Contrast-Enhanced CT Imaging Using Unenhanced and Contrast-Enhanced CT Imaging

Background Solitary pulmonary nodules (SPNs) measuring 8 to 30 mm in diameter require further workup to determine the likelihood of malignancy. Research Question 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? Study Design and Methods 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. Results 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). Interpretation An LCP-CNN algorithm provides an AUC equivalent to PET with CT scan imaging in the diagnosis of solitary pulmonary nodules