BACKGROUND Major issues in the implementation of screening for lung cancer by means of low-dose computed tomography (CT) are the definition of a positive result and the management of lung nodules detected on the scans. We conducted a population-based prospective study to determine factors predicting the probability that lung nodules detected on the first screening low-dose CT scans are malignant or will be found to be malignant on follow-up. METHODS We analyzed data from two cohorts of participants undergoing low-dose CT screening. The development data set included participants in the Pan-Canadian Early Detection of Lung Cancer Study (PanCan). The validation data set included participants involved in chemoprevention trials at the British Columbia Cancer Agency (BCCA), sponsored by the U.S. National Cancer Institute. The final outcomes of all nodules of any size that were detected on baseline low-dose CT scans were tracked. Parsimonious and fuller multivariable logistic-regression models were prepared to estimate the probability of lung cancer. RESULTS In the PanCan data set, 1871 persons had 7008 nodules, of which 102 were malignant, and in the BCCA data set, 1090 persons had 5021 nodules, of which 42 were malignant. Among persons with nodules, the rates of cancer in the two data sets were 5.5% and 3.7%, respectively. Predictors of cancer in the model included older age, female sex, family history of lung cancer, emphysema, larger nodule size, location of the nodule in the upper lobe, part-solid nodule type, lower nodule count, and spiculation. Our final parsimonious and full models showed excellent discrimination and calibration, with areas under the receiver-operating-characteristic curve of more than 0.90, even for nodules that were 10 mm or smaller in the validation set. CONCLUSIONS Predictive tools based on patient and nodule characteristics can be used to accurately estimate the probability that lung nodules detected on baseline screening low-dose CT scans are malignant. (Funded by the Terry Fox Research Institute and others; ClinicalTrials.gov number, NCT00751660.)

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Non-lung cancer outcomes drive screening efficiency in diverse, tobacco-exposed populations. Use of risk selection can reduce the budget impact, and screening may even offer cost savings if noncurative treatment costs continue to rise.

Background Current lung cancer screening guidelines use either mean diameter, volume, or density of the largest lung nodule on the previous CT scan or appearance of a new nodule to ascertain the timing of the next CT scan. We aimed to develop an accurate screening protocol by estimating the 3-year lung cancer risk after two screening CT scans using deep learning of radiologists' CT readings and other universally available clinical information. Methods A deep learning algorithm (referred to as DeepLR) was developed using data from participants who had received at least two CT screening scans up to 2 years apart in the National Lung Screening Trial (NLST; training cohort). Double-blinded validation was done using data from participants in the Pan-Canadian Early Detection of Lung Cancer (PanCan) study (validation cohort). The primary analysis was to compare accuracy of DeepLR scores to predict lung cancer incidence at 1 year, 2 years, and 3 years with the Lung CT Screening Reporting & Data System (Lung-RADS) and volume doubling time, using time-dependent area under the receiver operating characteristic curve (AUC) analysis. Findings The training cohort consisted of 25 097 participants from NLST and the validation cohort comprised 2294 individuals from PanCan. In the validation cohort, DeepLR showed good discrimination, with 1-year, 2-year, and 3-year time-dependent AUC values for cancer diagnosis of 0•968 (SD 0•013), 0•946 (0•013), and 0•899 (0•017), respectively. Among individuals deemed high risk by DeepLR, 94%, 85%, and 71% of incident and interval lung cancers diagnosed within 1 year, 2 years, and 3 years, respectively, after the second screening CT scan were identified. Furthermore, individuals with high DeepLR scores had a significantly higher risk of mortality (hazard ratio 16•07, 95% CI 10•15-25•44; p<0•0001) among people with high scores on Lung-RADS. Interpretation DeepLR recognises patterns in both temporal and spatial changes and synergy among changes in nodule and non-nodule features. DeepLR scores could be used to accurately guide clinical management after the next scheduled repeat screening CT scan.

BackgroundIdiopathic pulmonary fibrosis (IPF) is an adult-onset Idiopathic Interstitial Pneumonia (IIP) usually diagnosed between age 50 to 70 years. Individuals with Familial Pulmonary Fibrosis (FPF) have at least one affected first or second-degree relative and account for 0.5-20% of cases.MethodsWe ascertained and collected DNA samples from a large population-based cohort of IPF patients from Newfoundland, Canada. For each proband, a family history was documented and medical records were reviewed. Each proband was classified as familial (28 patients) or sporadic (50 patients) and all 78 probands were screened for variants in four highly penetrant, adult-onset PF genes (SFTPC, SFTPA2, TERT,TERC).ResultsSeventy-eight IPF probands were enrolled of whom 28 (35.9%) had a positive family history. These 28 familial patients led to the recruitment of an additional 49 affected relatives (total of 77 FPF patients). By age 60 years, 42% of the familial cohort had been diagnosed with PF compared with only 16% of the sporadic patient collection (χ2 = 8.77, p = 0.003). Mean age of diagnosis in the familial group was significantly younger than the sporadic group (61.4 years vs. 66.6 yrs, p = 0.012) with a wider age range of diagnosis (19–92 years compared with 47–82 years). Thirty-three of 77 (42.8%) FPF patients had a tissue diagnosis and all but five had usual interstitial pneumonia histology. Compared with other published case series, the familial IIP histologies were more homogeneous. Three of 28 familial probands (10.7%) and none of the 50 sporadic probands had pathogenic variants in the four genes tested. All three familial probands had mutations in TERT. Other phenotypes associated with telomerase deficiency were present in these families including cirrhosis, bone marrow hypoplasia and premature graying. Telomere length assays were performed on mutation carriers from two families and confirmed telomere-related deficiency.ConclusionThe proportion of familial cases in our cohort is higher than any previously reported estimate and we suggest that this is due to the fact that Newfoundland cohort is ethnically homogeneous and drawn from a founder population. In our patient collection, diagnosis with IPF prior to age 45 years predicted familial disease. In two of the three TERT mutation families, the pedigree appearance is consistent with genetic anticipation. In the other 25 FPF families negative for mutations in known PF genes, we did not identify other telomerase associated medical problems (bone marrow dysfunction, cirrhosis) and we hypothesize that there are novel PF genes segregating in our population.