Project description:ObjectiveTo determine the optimum definition of growth for indeterminate pulmonary nodules detected in lung cancer screening.Materials and methodsIndividuals with indeterminate nodules as defined by volume of 50-500 mm3 (solid nodules) and solid component volume of 50-500 mm3 or average diameter of non-solid component ≥8 mm (part-solid nodules) on baseline lung cancer screening low-dose chest CT (LDCT) were included. The average diameters and volumes of the nodules were measured on baseline and follow-up LDCTs with semi-automated segmentation. Sensitivities and specificities for lung cancer diagnosis of nodule growth defined by a) percentage volume growth ≥25% (defined in the NELSON study); b) absolute diameter growth >1.5 mm (defined in the Lung-RADS version 1.1); and c) subjective decision by a radiologist were evaluated. Sensitivities and specificities of diagnostic referral based on various thresholds of volume doubling time (VDT) were also evaluated.ResultsAltogether, 115 nodules (one nodule per individual; 93 solid and 22 part-solid nodules; 105 men; median age, 68 years) were evaluated (median follow-up interval: 201 days; interquartile range: 127-371 days). Percentage volume growth ≥25% exhibited higher sensitivity but lower specificity than those of diametrical measurement compared to absolute diameter growth >1.5 mm (sensitivity, 69.2% vs. 42.3%, p = 0.023; specificity, 82.0% vs. 96.6%, p = 0.002). The radiologist had an equivalent sensitivity (53.9%; p = 0.289) but higher specificity (98.9%; p = 0.002) compared to those of volume growth, but did not differ from those of diameter growth (p>0.05 both in sensitivity and specificity). Compared to the VDT threshold of 600 days (sensitivity, 61.5%; specificity, 87.6%), VDT thresholds ≤200 and ≤300 days exhibited significantly lower sensitivity (30.8%, p = 0.013) and higher specificity (94.4%, p = 0.041), respectively.ConclusionGrowth evaluation of screening-detected indeterminate nodules with volumetric measurement exhibited higher sensitivity but lower specificity compared to diametric measurements.

Project description:Background Subsolid pulmonary nodules, comprising pure ground-glass nodules (GGNs) and part-solid nodules (PSNs), have a high risk of indolent malignancy. Lung Imaging Reporting and Data System (Lung-RADS) nodule management guidelines are based on expert opinion and lack independent validation. Purpose To evaluate Lung-RADS estimates of the malignancy rates of subsolid nodules, using nodules from the National Lung Screening Trial (NLST), and to compare Lung-RADS to the NELSON trial classification as well as the Brock University calculator. Materials and Methods Subsets of GGNs and PSNs were selected from the NLST for this retrospective study. A thoracic radiologist reviewed the baseline and follow-up CT images, confirmed that they were true subsolid nodules, and measured the nodules. The primary outcome for each nodule was the development of malignancy within the follow-up period (median, 6.5 years). Nodules were stratified according to Lung-RADS, NELSON trial criteria, and the Brock model. For analyses, nodule subsets were weighted on the basis of frequency in the NLST data set. Nodule stratification models were tested by using receiver operating characteristic curves. Results A total of 622 nodules were evaluated, of which 434 nodules were subsolid. At baseline, 304 nodules were classified as Lung-RADS category 2, with a malignancy rate of 3%, which is greater than the 1% in Lung-RADS (P = .004). The malignancy rate for GGNs smaller than 10 mm (two of 129, 1.3%) was smaller than that for GGNs measuring 10-19 mm (11 of 153, 6%) (P = .01). The malignancy rate for Lung-RADS category 3 was 14% (13 of 67), which is greater than the reported 2% in Lung-RADS (P < .001). The Brock model predicted malignancy better than Lung-RADS and the NELSON trial scheme (area under the receiver operating characteristic curve = 0.78, 0.70, and 0.67, respectively; P = .02 for Brock model vs NELSON trial scheme). Conclusion Subsolid nodules classified as Lung Imaging Reporting and Data System (Lung-RADS) categories 2 and 3 have a higher risk of malignancy than reported. The Brock risk calculator performed better than measurement-based classification schemes such as Lung-RADS. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Kauczor and von Stackelberg in this issue.

Project description:BackgroundThe coexistence of emphysema and lung nodules could interact with each other and then lead to potential higher lung cancer risk. The study aimed to explore the association between emphysema combined with lung nodules and lung cancer risk.MethodsA total of 21,949 participants from the National Lung Screening Trial (NLST) who underwent low-dose computed tomography (LDCT) examination were included. Participants were categorized into four groups (NENN group (non-emphysema and non-nodules), E group (emphysema without nodules), N group (nodules without emphysema), and E + N group (nodules with emphysema)) according to whether there were lung nodules and emphysema. Multivariable Cox regression and stratified analyses were performed to estimate the association between the four groups and lung cancer risk.ResultsAmong the 21,949 participants, there were 9,040 (41.2%), 5,819 (26.5%), 4,737 (21.6%), and 2,353 (10.7%) participants in the NENN group, E group, N group, and E + N group. The risk of lung cancer incidence increased in turn in NENN group, E group, N group and E + N group. Compared with NENN group, the age-adjusted hazard ratios (HRs) (95% confidence intervals (CIs)) of lung cancer incidence were 2.07 (1.69 - 2.54) for E group, 4.13 (3.47 - 5.05) for N group, and 6.26 (5.14 - 7.62) for E + N group. The association was robust to adjustment for potential confounders (1.83 (1.47 - 2.27) for E group, 3.97 (3.24 - 4.86) for N group, and 5.23 (4.28 - 6.48) for E + N group). Comparable results as the lung cancer incidence were observed for lung cancer mortality, whether in age-adjusted model (E group: 1.85 (1.39 - 2.46), N group: 2.49 (1.89 - 3.29), E + N group: 4.27 (3.21 - 5.68)) or fully adjusted model (E group: 1.56 (1.15 - 2.11), N group: 2.43 (1.81 - 3.26), E + N group: 3.39 (2.50 - 4.61)). However, the trend of all-cause mortality risk among the four groups was somewhat different from that of lung cancer risk, whether in age-adjusted model (1.37 (1.21 - 1.54) for E group, 1.06 (0.92 - 1.21) for N group, and 1.75 (1.51 - 2.02) for E + N group) or fully adjusted model (1.26 (1.10 - 1.44) for E group, 1.09 (0.94 - 1.27) for N group, and 1.52 (1.30 - 1.79) for E + N group).ConclusionBased on a large-scale lung cancer screening trial in the United States, this study demonstrated that either emphysema or lung nodules can increase lung cancer risk, and lung nodules combined with emphysema can further increase the lung cancer risk and all-cause mortality. The significance of these findings for lung cancer screening should be evaluated.

Project description:BackgroundLung cancer is the most commonly diagnosed cancer worldwide. Its survival rate can be significantly improved by early screening. Biomarkers based on radiomics features have been found to provide important physiological information on tumors and considered as having the potential to be used in the early screening of lung cancer. In this study, we aim to establish a radiomics model and develop a tool to improve the discrimination between benign and malignant pulmonary nodules.MethodsA retrospective study was conducted on 875 patients with benign or malignant pulmonary nodules who underwent computed tomography (CT) examinations between June 2013 and June 2018. We assigned 612 patients to a training cohort and 263 patients to a validation cohort. Radiomics features were extracted from the CT images of each patient. Least absolute shrinkage and selection operator (LASSO) was used for radiomics feature selection and radiomics score calculation. Multivariate logistic regression analysis was used to develop a classification model and radiomics nomogram. Radiomics score and clinical variables were used to distinguish benign and malignant pulmonary nodules in logistic model. The performance of the radiomics nomogram was evaluated by the area under the curve (AUC), calibration curve and Hosmer-Lemeshow test in both the training and validation cohorts.ResultsA radiomics score was built and consisted of 20 features selected by LASSO from 1288 radiomics features in the training cohort. The multivariate logistic model and radiomics nomogram were constructed using the radiomics score and patients' age. Good discrimination of benign and malignant pulmonary nodules was obtained from the training cohort (AUC, 0.836; 95% confidence interval [CI]: 0.793-0.879) and validation cohort (AUC, 0.809; 95% CI: 0.745-0.872). The Hosmer-Lemeshow test also showed good performance for the logistic regression model in the training cohort (P = 0.765) and validation cohort (P = 0.064). Good alignment with the calibration curve indicated the good performance of the nomogram.ConclusionsThe established radiomics nomogram is a noninvasive preoperative prediction tool for malignant pulmonary nodule diagnosis. Validation revealed that this nomogram exhibited excellent discrimination and calibration capacities, suggesting its clinical utility in the early screening of lung cancer.

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Growth dynamics of lung nodules: implications for classification in lung cancer screening

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