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Purpose: To evaluate prospectively the value of size, shape, margin and density in discriminating between benign and malignant CT screen detected solid non-calcified pulmonary nodules. Material and Methods: This study was institutional review board approved. For this study 405 participants of the NELSON lung cancer screening trial with 469 indeterminate or potentially malignant solid pulmonary nodules (>50mm3) were selected. The nodules were classified based on size, shape (round, polygonal, irregular) and margin (smooth, lobulated, spiculated). Mean nodule density and nodule volume were automatically generated by software. Analyses were performed by univariate and multivariate logistic regression. Results were presented as likelihood ratios (LR) with 95% confidence intervals (CI). Receiver operating characteristic analysis was performed for mean density as predictor for lung cancer. Results: Of the 469 nodules, 387 (83%) were between 50 and 500mm3, 82 (17%) >500mm3, 59 (13%) malignant, 410 (87%) benign. The median size of the nodules was 103mm3 (range 50-5486mm3). In multivariate analysis lobulated nodules had LR of 11 compared to smooth; spiculated nodules a LR of 7 compared to smooth; irregular nodules a LR of 6 compared to round and polygonal; volume a LR of 3. The mean nodule CT density did not predict the presence of lung cancer (AUC 0.37, 95% CI 0.32-0.43). Conclusion: In solid non-calcified nodules larger than 50mm3, size and to a lesser extent a lobulated or spiculated margin and irregular shape increased the likelihood that a nodule was malignant. Nodule density had no discriminative power. [ABSTRACT FROM AUTHOR]

Abstract: Background: It is believed that making an informed decision about (screening) participation is associated with better health-related quality of life (HRQoL) outcomes. This is the first study in cancer screening to explore this association in subjects participating in a lung cancer computed tomography (CT) screening trial. Methods: Participants that made either an informed decision to participate (n =155) or not (n =133) were selected for this study. Differences in HRQoL, measured as generic HRQoL (Short Form 12 [SF-12] and EuroQol questionnaire [EQ-5D]), anxiety/distress (State-Trait Anxiety Inventory [STAI-6], Impact of Event Scale [IES] and Consequences of Screening-Lung Cancer [COS-LC]), were tested with Mann–Whitney U tests and ANOVA at three assessment points (when deciding about participation, before trial randomisation and 2months after receiving the CT result). Results: Subjects who made an informed decision to participate had no better scores than those who did not make an informed decision for 23 out of 24 HRQoL comparisons, except for a better mean score for mental health (Mental Component Summary (MCS)=53.9±9.2 versus 51.0±10.1, p =0.003) before randomisation. For subjects with an indeterminate CT result (n =64), no significant differences were found between subjects with (n =35) or without (n =29) an informed decision. Conclusion: Subjects who did not make an informed decision to participate in lung cancer CT screening trial did not experience worse HRQoL during screening than subjects who did make an informed decision, either in general or after receiving an indeterminate result. [ABSTRACT FROM AUTHOR]

Objective: To assess volumetric measurement variability in pulmonary nodules detected at low-dose chest CT with three reconstruction settings.Methods: The volume of 200 solid pulmonary nodules was measured three times using commercially available semi-automated software of low-dose chest CT data-sets reconstructed with 1 mm section thickness and a soft kernel (A), 2 mm and a soft kernel (B), and 2 mm and a sharp kernel (C), respectively. Repeatability coefficients of the three measurements within each setting were calculated by the Bland and Altman method. A three-level model was applied to test the impact of reconstruction setting on the measured volume.Results: The repeatability coefficients were 8.9, 22.5 and 37.5% for settings A, B and C. Three-level analysis showed that settings A and C yielded a 1.29 times higher estimate of nodule volume compared with setting B (P = 0.03). The significant interaction among setting, nodule location and morphology demonstrated that the effect of the reconstruction setting was different for different types of nodules. Low-dose CT reconstructed with 1 mm section thickness and a soft kernel provided the most repeatable volume measurement.Conclusion: A wide, nodule-type-dependent range of agreement between volume measurements with different reconstruction settings suggests strict consistency is required for serial CT studies. [ABSTRACT FROM AUTHOR]

Objective: To evaluate performance of computer-aided detection (CAD) beyond double reading for pulmonary nodules on low-dose computed tomography (CT) by nodule volume.Methods: A total of 400 low-dose chest CT examinations were randomly selected from the NELSON lung cancer screening trial. CTs were evaluated by two independent readers and processed by CAD. A total of 1,667 findings marked by readers and/or CAD were evaluated by a consensus panel of expert chest radiologists. Performance was evaluated by calculating sensitivity of pulmonary nodule detection and number of false positives, by nodule characteristics and volume.Results: According to the screening protocol, 90.9 % of the findings could be excluded from further evaluation, 49.2 % being small nodules (less than 50 mm(3)). Excluding small nodules reduced false-positive detections by CAD from 3.7 to 1.9 per examination. Of 151 findings that needed further evaluation, 33 (21.9 %) were detected by CAD only, one of them being diagnosed as lung cancer the following year. The sensitivity of nodule detection was 78.1 % for double reading and 96.7 % for CAD. A total of 69.7 % of nodules undetected by readers were attached nodules of which 78.3 % were vessel-attached.Conclusions: CAD is valuable in lung cancer screening to improve sensitivity of pulmonary nodule detection beyond double reading, at a low false-positive rate when excluding small nodules.Key Points: • Computer-aided detection (CAD) has known advantages for computed tomography (CT). • Combined CAD/nodule size cut-off parameters assist CT lung cancer screening. • This combination improves the sensitivity of pulmonary nodule detection by CT. • It increases the positive predictive value for cancer detection. [ABSTRACT FROM AUTHOR]