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

OBJECTIVES: 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. METHODS: Clinical and imaging data were retrospectively collected from 200 patients from three centres. Accuracy was assessed by means of receiver-operating characteristic (ROC) areas under the curve (AUCs). Decision analysis was performed by adopting both the American College of Chest Physicians (ACCP) and the British Thoracic Society (BTS) risk thresholds. RESULTS: ROC analysis showed an AUC of 0.880 (95 % CI, 0.832-0.928) for the BIMC model and of 0.604 (95 % CI, 0.524-0.683) for the Mayo Clinic model. Difference was 0.276 (95 % CI, 0.190-0.363, P < 0.0001). Decision analysis showed a slightly reduced number of false-negative and false-positive results when using ACCP risk thresholds. CONCLUSIONS: The BIMC model proved to be an accurate tool when characterising SPNs. In a clinical setting it can distinguish malignancies from benign nodules with minimal errors by adopting current ACCP or BTS risk thresholds and guiding lesion-tailored diagnostic and interventional procedures during the work-up. KEY POINTS: • The BIMC model can accurately discriminate malignancies in the clinical setting • The BIMC model showed ROC AUC of 0.880 in this multicentre study • The BIMC model compares favourably with the Mayo Clinic model.

Comparing CT colonography and flexible sigmoidoscopy: a randomised trial within a population-based screening programme.

IMPORTANCE AND AIMS: The role of CT colonography (CTC) as a colorectal cancer (CRC) screening test is uncertain. The aim of our trial was to compare participation and detection rate (DR) with sigmoidoscopy (flexible sigmoidoscopy (FS)) and CTC in a screening setting. DESIGN SETTING AND PARTICIPANTS: We conducted two randomised clinical trials (RCTs). (1) Participation RCT: individuals, aged 58 years, living in Turin (Italy), were randomly assigned to be invited to FS or CTC screening; (2) detection RCT: residents in northern Italy, aged 58-60, giving their consent to recruitment, were randomly allocated to CTC or FS. Polyps ≥6 mm at CTC, or 'high-risk' distal lesions at FS, were referred for colonoscopy (TC). MAIN OUTCOME MEASURES: Participation rate (proportion of invitees examined); DR of advanced adenomas or CRC (advanced neoplasia (AN)). RESULTS: Participation was 30.4% (298/980) for CTC and 27.4% (267/976) for FS (relative risk (RR) 1.1; 95% CI 0.98 to 1.29). Among men, participation was higher with CTC than with FS (34.1% vs 26.5%, p=0.011). In the detection RCT, 2673 subjects had FS and 2595 had CTC: the AN DR was 4.8% (127/2673, including 9 CRCs) with FS and 5.1% (133/2595, including 10 CRCs) with CTC (RR 1.08; 95% CI 0.85 to 1.37). Distal AN DR was 3.9% (109/2673) with FS and 2.9% (76/2595) with CTC (RR 0.72; 95% CI 0.54 to 0.96); proximal AN DR was 1.2% (34/2595) for FS vs 2.7% (69/2595) for CTC (RR 2.06; 95% CI 1.37 to 3.10). CONCLUSIONS AND RELEVANCE: Participation and DR for FS and CTC were comparable. AN DR was twice as high in the proximal colon and lower in the distal colon with CTC than with FS. Men were more likely to participate in CTC screening. TRIAL REGISTRATION NUMBER: NCT01739608; Pre-results.

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.

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/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.

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

OBJECTIVES: 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. METHODS: A total of 285 solitary pulmonary nodules with a definitive diagnosis were evaluated by means of four major risk assessment models developed from non-screening populations, namely the Mayo, Gurney, PKUPH and BIMC models. Accuracy was evaluated by receiver operating characteristic (ROC) area under the curve (AUC) analysis. Each model's fitness to provide reliable help in decision analysis was primarily assessed by adopting a surgical threshold of 65 % and an observation threshold of 5 % as suggested by ACCP guidelines. RESULTS: ROC AUC values, false positives, false negatives and indeterminate nodules were respectively 0.775, 3, 8, 227 (Mayo); 0.794, 41, 6, 125 (Gurney); 0.889, 42, 0, 144 (PKUPH); 0.898, 16, 0, 118 (BIMC). CONCLUSIONS: Resultant data suggests that the BIMC model may be of greater help than Mayo, Gurney and PKUPH models in preoperative SPN characterization when using ACCP risk thresholds because of overall better accuracy and smaller numbers of indeterminate nodules and false positive results. KEY POINTS: • The BIMC and PKUPH models offer better characterization than older prediction models • Both the PKUPH and BIMC models completely avoided false negative results • The Mayo model suffers from a large number of indeterminate results.