Dataset Information

National lung screening trial: variability in nodule detection rates in chest CT studies.

ABSTRACT: To characterize the variability in radiologists' interpretations of computed tomography (CT) studies in the National Lung Screening Trial (NLST) (including assessment of false-positive rates [FPRs] and sensitivity), to examine factors that contribute to variability, and to evaluate trade-offs between FPRs and sensitivity among different groups of radiologists.The HIPAA-compliant NLST was approved by the institutional review board at each screening center; all participants provided informed consent. NLST radiologists reported overall screening results, nodule-specific findings, and recommendations for diagnostic follow-up. A noncalcified nodule of 4 mm or larger constituted a positive screening result. The FPR was defined as the rate of positive screening examinations in participants without a cancer diagnosis within 1 year. Descriptive analyses and mixed-effects models were utilized. The average odds ratio (OR) for a false-positive result across all pairs of radiologists was used as a measure of variability.One hundred twelve radiologists at 32 screening centers each interpreted 100 or more NLST CT studies, interpreting 72 160 of 75 126 total NLST CT studies in aggregate. The mean FPR for radiologists was 28.7% ± 13.7 (standard deviation), with a range of 3.8%-69.0%. The model yielded an average OR of 2.49 across all pairs of radiologists and an OR of 1.83 for pairs within the same screening center. Mean FPRs were similar for academic versus nonacademic centers (27.9% and 26.7%, respectively) and for centers inside (25.0%) versus outside (28.7%) the U.S. "histoplasmosis belt." Aggregate sensitivity was 96.5% for radiologists with FPRs higher than the median (27.1%), compared with 91.9% for those with FPRs lower than the median (P = .02).There was substantial variability in radiologists' FPRs. Higher FPRs were associated with modestly higher sensitivity.

SUBMITTER: Pinsky PF

PROVIDER: S-EPMC3750416 | biostudies-other | 2013 Sep

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

Project description:Our previous study indicated that multiprojection chest radiography could significantly improve radiologists' performance for lung nodule detection in clinical practice. In this study, the authors further verify that multiprojection chest radiography can greatly improve the performance of a computer-aided diagnostic (CAD) scheme.Our database consisted of 59 subjects, including 43 subjects with 45 nodules and 16 subjects without nodules. The 45 nodules included 7 real and 38 simulated ones. The authors developed a conventional CAD scheme and a new fusion CAD scheme to detect lung nodules. The conventional CAD scheme consisted of four steps for (1) identification of initial nodule candidates inside lungs, (2) nodule candidate segmentation based on dynamic programming, (3) extraction of 33 features from nodule candidates, and (4) false positive reduction using a piecewise linear classifier. The conventional CAD scheme processed each of the three projection images of a subject independently and discarded the correlation information between the three images. The fusion CAD scheme included the four steps in the conventional CAD scheme and two additional steps for (5) registration of all candidates in the three images of a subject, and (6) integration of correlation information between the registered candidates in the three images. The integration step retained all candidates detected at least twice in the three images of a subject and removed those detected only once in the three images as false positives. A leave-one-subject-out testing method was used for evaluation of the performance levels of the two CAD schemes.At the sensitivities of 70%, 65%, and 60%, our conventional CAD scheme reported 14.7, 11.3, and 8.6 false positives per image, respectively, whereas our fusion CAD scheme reported 3.9, 1.9, and 1.2 false positives per image, and 5.5, 2.8, and 1.7 false positives per patient, respectively. The low performance of the conventional CAD scheme may be attributed to the high noise level in chest radiography, and the small size and low contrast of most nodules.This study indicated that the fusion of correlation information in multiprojection chest radiography can markedly improve the performance of CAD scheme for lung nodule detection.

Project description:ObjectivesThe 2019 Lung CT Screening Reporting & Data System version 1.1 (Lung-RADS v1.1) introduced volumetric categories for nodule management. The aims of this study were to report the distribution of Lung-RADS v1.1 volumetric categories and to analyse lung cancer (LC) outcomes within 3 years for exploring personalized algorithm for lung cancer screening (LCS).MethodsSubjects from the Multicentric Italian Lung Detection (MILD) trial were retrospectively selected by National Lung Screening Trial (NLST) criteria. Baseline characteristics included selected pre-test metrics and nodule characterization according to the volume-based categories of Lung-RADS v1.1. Nodule volume was obtained by segmentation with dedicated semi-automatic software. Primary outcome was diagnosis of LC, tested by univariate and multivariable models. Secondary outcome was stage of LC. Increased interval algorithms were simulated for testing rate of delayed diagnosis (RDD) and reduction of low-dose computed tomography (LDCT) burden.ResultsIn 1248 NLST-eligible subjects, LC frequency was 1.2% at 1 year, 1.8% at 2 years and 2.6% at 3 years. Nodule volume in Lung-RADS v1.1 was a strong predictor of LC: positive LDCT showed an odds ratio (OR) of 75.60 at 1 year (p < 0.0001), and indeterminate LDCT showed an OR of 9.16 at 2 years (p = 0.0068) and an OR of 6.35 at 3 years (p = 0.0042). In the first 2 years after negative LDCT, 100% of resected LC was stage I. The simulations of low-frequency screening showed a RDD of 13.6-21.9% and a potential reduction of LDCT burden of 25.5-41%.ConclusionsNodule volume by semi-automatic software allowed stratification of LC risk across Lung-RADS v1.1 categories. Personalized screening algorithm by increased interval seems feasible in 80% of NLST eligible.Key points• Using semi-automatic segmentation of nodule volume, Lung-RADS v1.1 selected 10.8% of subjects with positive CT and 96.87 relative risk of lung cancer at 1 year, compared to negative CT. • Negative low-dose CT by Lung-RADS v1.1 was found in 80.6% of NLST eligible and yielded 40 times lower relative risk of lung cancer at 2 years, compared to positive low-dose CT; annual screening could be preference sensitive in this group. • Semi-automatic segmentation of nodule volume and increased screening interval by volumetric Lung-RADS v1.1 could retrospectively suggest a 25.5-41% reduction of LDCT burden, at the cost of 13.6-21.9% rate of delayed diagnosis.

Project description:We propose a deep learning-assisted overscan decision algorithm in chest low-dose computed tomography (LDCT) applicable to the lung cancer screening. The algorithm reflects the radiologists' subjective evaluation criteria according to the Korea institute for accreditation of medical imaging (KIAMI) guidelines, where it judges whether a scan range is beyond landmarks' criterion. The algorithm consists of three stages: deep learning-based landmark segmentation, rule-based logical operations, and overscan determination. A total of 210 cases from a single institution (internal data) and 50 cases from 47 institutions (external data) were utilized for performance evaluation. Area under the receiver operating characteristic (AUROC), accuracy, sensitivity, specificity, and Cohen's kappa were used as evaluation metrics. Fisher's exact test was performed to present statistical significance for the overscan detectability, and univariate logistic regression analyses were performed for validation. Furthermore, an excessive effective dose was estimated by employing the amount of overscan and the absorbed dose to effective dose conversion factor. The algorithm presented AUROC values of 0.976 (95% confidence interval [CI]: 0.925-0.987) and 0.997 (95% CI: 0.800-0.999) for internal and external dataset, respectively. All metrics showed average performance scores greater than 90% in each evaluation dataset. The AI-assisted overscan decision and the radiologist's manual evaluation showed a statistically significance showing a p-value less than 0.001 in Fisher's exact test. In the logistic regression analysis, demographics (age and sex), data source, CT vendor, and slice thickness showed no statistical significance on the algorithm (each p-value > 0.05). Furthermore, the estimated excessive effective doses were 0.02 ± 0.01 mSv and 0.03 ± 0.05 mSv for each dataset, not a concern within slight deviations from an acceptable scan range. We hope that our proposed overscan decision algorithm enables the retrospective scan range monitoring in LDCT for lung cancer screening program, and follows an as low as reasonably achievable (ALARA) principle.

Dataset Information

National lung screening trial: variability in nodule detection rates in chest CT studies.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets