scholarly journals Squamous cell carcinomas of the peripheral lung detected at membership system mass screening for lung cancer-Five cases with abnormal sputum cytology found prior to low-dose CT detection-

2004 ◽  
Vol 43 (3) ◽  
pp. 171-177 ◽  
Author(s):  
Kumiko KAMATA ◽  
Kikue TSUCHIYA ◽  
Takao SUGIYAMADA ◽  
Akikazu TAKECHI ◽  
Saburo HARASHIMA ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Mass Screening ◽  
Squamous Cell ◽  
Low Dose ◽  
Squamous Cell Carcinomas ◽  
Sputum Cytology ◽  
Low Dose Ct ◽  
Peripheral Lung

scholarly journals The detection rate of early squamous cell lung cancer from results of sputum cytology in mass screening

2009 ◽  
Vol 48 (4) ◽  
pp. 166-169 ◽  
Author(s):  
Junko KAMIO ◽  
Takeharu SATO ◽  
Yoshie MUROI ◽  
Shinichi SHIBATA ◽  
Takashi ISHIDA ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Mass Screening ◽  
Squamous Cell ◽  
Cell Lung Cancer ◽  
Detection Rate ◽  
Squamous Cell Lung Cancer ◽  
Sputum Cytology

LUNGRADS AND UPDATE OF LUNG NODULES SCREENING BY LOW DOSE COMPUTED TOMOGRAPHY

2015 ◽  
pp. 12-19
Author(s):  
Thi Ngoc Ha Hoang ◽  
Trong Khoan Le
Keyword(s):  
Lung Cancer ◽  
Low Dose ◽  
False Negative ◽  
Lung Nodule ◽  
Lung Cancer Mortality ◽  
Smoking History ◽  
Low Dose Ct ◽  
History Of ◽  
Chest X Ray ◽  
Low Dose Computed Tomography

Background: A pulmonary nodule is defined as a rounded or irregular opacity, well or poorly defined, measuring up to 3 cm in diameter. Early detection the malignancy of nodules has a significant role in decreasing the mortality, increasing the survival time and consider as early diagnosis lung cancer. The main risk factors are those of current or former smokers, aged 55 to 74 years with a smoking history of at least 1 pack-day. Low dose CT: screening individuals with high risk of lung cancer by low dose CT scans could reduce lung cancer mortality by 20 percent compared to chest X-ray. Radiation dose has to maximum reduced but respect the rule of ALARA (As Low as Resonably Archivable). LungRADS 2014: Classification of American College of Radiology, LungRADS, is a newly application but showed many advantages in comparison with others classification such as increasing positive predict value (PPV), no result of false negative and cost effectiveness. Key words: LungRADS, screening lung nodule, low dose CT, lung cancer

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

scholarly journals Screening for lung cancer with low-dose CT scans

2013 ◽  
Vol 51 (4) ◽  
pp. 205-206 ◽  
Author(s):  
James R. Jett
Keyword(s):  
Lung Cancer ◽  
Low Dose ◽  
Ct Scans ◽  
Low Dose Ct

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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