Structured reporting for fibrosing lung disease: a model shared by radiologist and pulmonologist

Objectives: To apply the Delphi exercise with iterative involvement of radiologists and pulmonologists with the aim of defining a structured reporting template for high-resolution computed tomography (HRCT) of patients with fibrosing lung disease (FLD). Methods: The writing committee selected the HRCT criteria\ue2\u80\u94the Delphi items\ue2\u80\u94for rating from both radiology panelists (RP) and pulmonology panelists (PP). The Delphi items were first rated by RPs as \ue2\u80\u9cessential\ue2\u80\u9d, \ue2\u80\u9coptional\ue2\u80\u9d, or \ue2\u80\u9cnot relevant\ue2\u80\u9d. The items rated \ue2\u80\u9cessential\ue2\u80\u9d by < 80% of the RP were selected for the PP rating. The format of reporting was rated by both RP and PP. Results: A total of 42 RPs and 12 PPs participated to the survey. In both Delphi round 1 and 2, 10/27 (37.7%) items were rated \ue2\u80\u9cessential\ue2\u80\u9d by more than 80% of RP. The remaining 17/27 (63.3%) items were rated by the PP in round 3, with 2/17 items (11.7%) rated \ue2\u80\u9cessential\ue2\u80\u9d by the PP. PP proposed additional items for conclusion domain, which were rated by RPs in the fourth round. Poor consensus was observed for the format of reporting. Conclusions: This study provides a template for structured report of FLD that features essential items as agreed by expert thoracic radiologists and pulmonologists

Critique of Al-Ameri et al. (2015) - Risk of malignancy in pulmonary nodules: A validation study of four prediction models

[No abstract available] Lette

Solid pulmonary nodule risk assessment and decision analysis: comparison of four prediction models in 285 cases

The aim of this study was to compare classification results from four major risk prediction models in a wide population of incidentally detected solitary pulmonary nodules (SPNs) which were selected to crossmatch inclusion criteria for the selected models

CT imaging features in the characterization of non-growing solid pulmonary nodules in non-smokers

BACKGROUND: A disappearing or persistent solid pulmonary nodule is a neglected clinical entity that still poses serious interpretative issues to date. Traditional knowledge deriving from previous reports suggests particular features, such as smooth edges or regular shape, to be significantly associated with benignity. A large number of benign nodules are reported among smokers in lung cancer screening programmes. The aim of this single-center retrospective study was to correlate specific imaging features to verify if traditional knowledge as well as more recent acquisitions regarding benign SPNs can be considered reliable in a current case series of nodules collected in a non-smoker cohort of patients. MATERIAL AND METHODS: Fifty-three solid SPNs proven as non-growing during follow-up imaging were analyzed with regard to their imaging features at thin-section CT, their predicted malignancy risk according to three major risk assessment models, minimum density analysis and contrast enhanced-CT in the relative subgroups of nodules which underwent such tests. RESULTS: Eleven nodules disappeared during follow-up, 29 showed volume loss and 16 had a VDT of 1121 days or higher. There were 48 nodules located peripherally (85.71%). Evaluation of the enhancement after contrast media (n=29) showed mean enhancement ±SD of 25.72±35.03 HU, median of 18 HU, ranging from 0 to 190 HU. Minimum density assessment (n=30) showed mean minimum HU ±SD of –28.27±47.86 HU, median of -25 HU, ranging from -144 to 68 HU. Mean malignancy risk ±SD was 15.05±26.69% for the BIMC model, 17.22±19.00% for the Mayo Clinic model and 19.07±33.16% for the Gurney's model. CONCLUSIONS: Our analysis suggests caution in using traditional knowledge when dealing with current small solid peripheral indeterminate SPNs and highlights how quantitative growth at follow-up should be the cornerstone of characterization

Distribution of Solid Solitary Pulmonary Nodules within the Lungs on Computed Tomography: A Review of 208 Consecutive Lesions of Biopsy-Proven Nature

The solitary pulmonary nodule (SPN) is a common radiologic abnormality on chest x-rays or computed tomography (CT) scans of the lungs. The differential diagnosis of SPNs is particularly wide as it includes a multitude of benign as well as malignant entities. Nodule location within the lungs has been proposed as a predictive feature in the literature. This study aims at illustrating the distribution within the lungs of a large current series of consecutive SPNs according to their histological subtype, which was definitely proved at core biopsy

Multicentre external validation of the BIMC model for solid solitary pulmonary nodule malignancy prediction

To provide multicentre external validation of the Bayesian Inference Malignancy Calculator (BIMC) model by assessing diagnostic accuracy in a cohort of solitary pulmonary nodules (SPNs) collected in a clinic-based setting. To assess model impact on SPN decision analysis and to compare findings with those obtained via the Mayo Clinic model

Enhanced characterization of solid solitary pulmonary nodules with Bayesian analysis-based computer-aided diagnosis

The aim of this study was to prospectively assess the accuracy gain of Bayesian analysis-based computer-aided diagnosis (CAD) vs human judgment alone in characterizing solitary pulmonary nodules (SPNs) at computed tomography (CT). The study included 100 randomly selected SPNs with a definitive diagnosis. Nodule features at first and follow-up CT scans as well as clinical data were evaluated individually on a 1 to 5 points risk chart by 7 radiologists, firstly blinded then aware of Bayesian Inference Malignancy Calculator (BIMC) model predictions. Raters' predictions were evaluated by means of receiver operating characteristic (ROC) curve analysis and decision analysis. Overall ROC area under the curve was 0.758 before and 0.803 after the disclosure of CAD predictions (P = 0.003). A net gain in diagnostic accuracy was found in 6 out of 7 readers. Mean risk class of benign nodules dropped from 2.48 to 2.29, while mean risk class of malignancies rose from 3.66 to 3.92. Awareness of CAD predictions also determined a significant drop on mean indeterminate SPNs (15 vs 23.86 SPNs) and raised the mean number of correct and confident diagnoses (mean 39.57 vs 25.71 SPNs). This study provides evidence supporting the integration of the Bayesian analysis-based BIMC model in SPN characterization

Enhanced characterization of solid solitary pulmonary nodules with Bayesian analysis-based computer-aided diagnosis

The aim of this study was to prospectively assess the accuracy gain of Bayesian analysis-based computer-aided diagnosis (CAD) vs human judgment alone in characterizing solitary pulmonary nodules (SPNs) at computed tomography (CT). The study included 100 randomly selected SPNs with a definitive diagnosis. Nodule features at first and follow-up CT scans as well as clinical data were evaluated individually on a 1 to 5 points risk chart by 7 radiologists, firstly blinded then aware of Bayesian Inference Malignancy Calculator (BIMC) model predictions. Raters' predictions were evaluated by means of receiver operating characteristic (ROC) curve analysis and decision analysis. Overall ROC area under the curve was 0.758 before and 0.803 after the disclosure of CAD predictions (P = 0.003). A net gain in diagnostic accuracy was found in 6 out of 7 readers. Mean risk class of benign nodules dropped from 2.48 to 2.29, while mean risk class of malignancies rose from 3.66 to 3.92. Awareness of CAD predictions also determined a significant drop on mean indeterminate SPNs (15 vs 23.86 SPNs) and raised the mean number of correct and confident diagnoses (mean 39.57 vs 25.71 SPNs). This study provides evidence supporting the integration of the Bayesian analysis-based BIMC model in SPN characterization

Multicenter external validation of two malignancy risk prediction models in patients undergoing 18F-FDG-PET for solitary pulmonary nodule evaluation

To achieve multicentre external validation of the Herder and Bayesian Inference Malignancy Calculator (BIMC) models

Multicentre external validation of the BIMC model for solid solitary pulmonary nodule malignancy prediction

To provide multicentre external validation of the Bayesian Inference Malignancy Calculator (BIMC) model by assessing diagnostic accuracy in a cohort of solitary pulmonary nodules (SPNs) collected in a clinic-based setting. To assess model impact on SPN decision analysis and to compare findings with those obtained via the Mayo Clinic model