scholarly journals Lung cancer screening by nodule volume in Lung-RADS v1.1: negative baseline CT yields potential for increased screening interval

2020 ◽  
Author(s):  
Mario Silva ◽  
Gianluca Milanese ◽  
Stefano Sestini ◽  
Federica Sabia ◽  
Colin Jacobs ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Relative Risk ◽  
Cancer Screening ◽  
Low Dose ◽  
Automatic Segmentation ◽  
Delayed Diagnosis ◽  
Lung Cancer Screening ◽  
Screening Interval ◽  
Nodule Volume ◽  
Automatic Software

Abstract Objectives The 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). Methods Subjects 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. Results In 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%. Conclusions Nodule 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.

LOW-DOSE COMPUTED TOMOGRAPHY IN MOSCOW FOR LUNG CANCER SCREENING (LDCT-MLCS): BASELINE RESULTS

2019 ◽  
Vol 65 (2) ◽  
pp. 224-233
Author(s):  
Sergey Morozov ◽  
Viktor Gombolevskiy ◽  
Anton Vladzimirskiy ◽  
Albina Laypan ◽  
Pavel Kononets ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Cancer Screening ◽  
Low Dose ◽  
Early Stage ◽  
Lung Cancer Screening ◽  
Stage I ◽  
Malignant Neoplasms ◽  
Number Needed To Screen ◽  
Low Dose Computed Tomography

Study aim. To justify selective lung cancer screening via low-dose computed tomography and evaluate its effectiveness. Materials and methods. In 2017 we have concluded the baseline stage of “Lowdose computed tomography in Moscow for lung cancer screening (LDCT-MLCS)” trial. The trial included 10 outpatient clinics with 64-detector CT units (Toshiba Aquilion 64 and Toshiba CLX). Special low-dose protocols have been developed for each unit with maximum effective dose of 1 mSv (in accordance with the requirements of paragraph 2.2.1, Sanitary Regulations 2.6.1.1192-03). The study involved 5,310 patients (53% men, 47% women) aged 18-92 years (mean age 62 years). Diagnosis verification was carried out in the specialized medical organizations via consultations, additional instrumental, laboratory as well as pathohistological studies. The results were then entered into the “National Cancer Registry”. Results. 5310 patients (53% men, 47% women) aged 18 to 92 years (an average of 62 years) participated in the LDCT-MLCS. The final cohort was comprised of 4762 (89.6%) patients. We have detected 291 (6.1%) Lung-RADS 3 lesions, 228 (4.8%) Lung- RADS 4A lesions and 196 (4.1%) Lung-RADS 4B/4X lesions. All 4B and 4X lesions were routed in accordance with the project's methodology and legislative documents. Malignant neoplasms were verified in 84 cases (1.76% of the cohort). Stage I-II lung cancer was actively detected in 40.3% of these individuals. For the first time in the Russian Federation we have calculated the number needed to screen (NNS) to identify one lung cancer (NNS=57) and to detect one Stage I lung cancer (NNS=207). Conclusions. Based on the global experience and our own practices, we argue that selective LDCT is the most systematic solution to the problem of early-stage lung cancer screening.

scholarly journals Lung Cancer Screening by Low-Dose Computed Tomography: Part 2 – Key Elements for Programmatic Implementation of Lung Cancer Screening

2020 ◽  
Author(s):  
Stefan Delorme ◽  
Rudolf Kaaks
Keyword(s):  
Lung Cancer ◽  
Cancer Screening ◽  
Low Dose ◽  
Lung Cancer Screening ◽  
Cancer Registries ◽  
Computer Assisted ◽  
Management Algorithm ◽  
Epidemiologic Surveillance ◽  
Set Up ◽  
High Level

Purpose For screening with low-dose CT (LDCT) to be effective, the benefits must outweigh the potential risks. In large lung cancer screening studies, a mortality reduction of approx. 20 % has been reported, which requires several organizational elements to be achieved in practice. Materials and Methods The elements to be set up are an effective invitation strategy, uniform and quality-assured assessment criteria, and computer-assisted evaluation tools resulting in a nodule management algorithm to assign each nodule the needed workup intensity. For patients with confirmed lung cancer, immediate counseling and guideline-compliant treatment in tightly integrated regional expert centers with expert skills are required. First, pulmonology contacts as well as CT facilities should be available in the participant’s neighborhood. IT infrastructure, linkage to clinical cancer registries, quality management as well as epidemiologic surveillance are also required. Results An effective organization of screening will result in an articulated structure of both widely distributed pulmonology offices as the participants’ primary contacts and CT facilities as well as central expert facilities for supervision of screening activities, individual clarification of suspicious findings, and treatment of proven cancer. Conclusion In order to ensure that the benefits of screening more than outweigh the potential harms and that it will be accepted by the public, a tightly organized structure is needed to ensure wide availability of pulmonologists as first contacts and CT facilities with expert skills and high-level equipment concentrated in central facilities. Key Points:  Citation Format

Overwhelming Support Among Urban Irish COPD Patients for Lung Cancer Screening by Low-Dose CT Scan

Lung ◽  
2012 ◽  
Vol 190 (6) ◽  
pp. 621-628 ◽  
Author(s):  
M. Pallin ◽  
S. Walsh ◽  
M. F. O’Driscoll ◽  
C. Murray ◽  
A. Cahalane ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Screening ◽  
Ct Scan ◽  
Low Dose ◽  
Lung Cancer Screening ◽  
Low Dose Ct ◽  
Copd Patients

Radiomic Phenotyping of the Lung Parenchyma: Feasibility Study in a Screening Cohort

2021 ◽  
Author(s):  
Babak Haghighi ◽  
Hannah Horng ◽  
Peter B Noël ◽  
Eric Cohen ◽  
Lauren Pantalone ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Screening ◽  
Low Dose ◽  
Lung Cancer Screening ◽  
Lung Parenchyma ◽  
Ct Scans ◽  
Lung Field ◽  
Reconstruction Algorithms ◽  
Low Dose Ct ◽  
Significant Difference

Abstract Rationale: High-throughput extraction of radiomic features from low-dose CT scans can characterize the heterogeneity of the lung parenchyma and potentially aid in identifying subpopulations that may have higher risk of lung diseases, such as COPD, and lung cancer due to inflammation or obstruction of the airways. We aim to determine the feasibility a lung radiomics phenotyping approach in a lung cancer screening cohort, while quantifying the effect of different CT reconstruction algorithms on phenotype robustness. Methods: We identified low-dose CT scans (n = 308) acquired with Siemens Healthineers scanners from patients who completed low-dose CT within our lung cancer screening program between 2015-2018 and had two different sets of image reconstructions kernel available (i.e., medium (I30f), sharp (I50f)) for the same acquisition. Following segmentation of the lung field, a total of 26 radiomic features were extracted from the entire 3D lung-field using a previously validated fully-automated lattice-based software pipeline, adapted for low-dose CT scans. The features extracted included gray-level histogram, co-occurrence, and run-length descriptors. Each feature was averaged for each scan within a range of lattice window sizes (W) ranging from 4-20mm. The extracted imaging features from both datasets were harmonized to correct for differences in image acquisition parameters. Subsequently, unsupervised hierarchal clustering was applied on the extracted features to identify distinct phenotypic patterns of the lung parenchyma, where consensus clustering was used to identify the optimal number of clusters (K = 2). Differences between? phenotypes for demographic and clinical covariates including sex, age, BMI, pack-years of smoking, Lung-RADS and cancer diagnosis were assessed for each phenotype cluster, and then compared across clusters for the two different CT reconstruction algorithms using the cluster entanglement metric, where a lower entanglement coefficient corresponds to good cluster alignment. Furthermore, an independent set of low-dose CT scans (n = 88) from patients with available pulmonary function data on lung obstruction were analyzed using the identified optimal clusters to assess associations to lung obstruction and validate the lung phenotyping paradigm. Results: Heatmaps generated by radiomic features identified two distinct lung parenchymal phenotype patterns across different feature extraction window sizes, for both reconstruction algorithms (P < 0.05 with K = 2). Associations of radiomic-based clusters with clinical covariates showed significant difference for BMI and pack-years of smoking (P < 0.05) for both reconstruction kernels. Radiomic phenotype patterns where similar across the two reconstructed kernels, specifically when smaller window sizes (W=4 and 8mm) were used for radiomic feature extraction, as deemed by their entanglement coefficient. Validation of clustering approaches using cluster mapping for the independent sample with lung obstruction also showed two statistically significant phenotypes (P < 0.05) with significant difference for BMI and smoking pack-years.ConclusionsRadiomic analysis can be used to characterize lung parenchymal phenotypes from low-dose CT scans, which appear reproducible for different reconstruction kernels. Further work should seek to evaluate the effect of additional CT acquisition parameters and validate these phenotypes in characterizing lung cancer screening populations, to potentially better stratify disease patterns and cancer risk.

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