Risk stratification of lung cancer patients at initial presentation: A retrospective cohort study.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e20062-e20062
Author(s):  
Jean-Michel Lavoie ◽  
Cheryl Ho ◽  
Sophie Sun
Keyword(s):  
Lung Cancer ◽  
Weight Loss ◽  
Risk Stratification ◽  
Retrospective Cohort ◽  
Low Dose ◽  
Initial Presentation ◽  
Smoking History ◽  
Clinical Factors ◽  
Screening Criteria ◽  
Low Dose Ct

e20062 Background: Early detection and treatment of non-small cell lung cancer (NSCLC) has been shown to improve survival. Current screening guidelines focus on at-risk populations, overlooking a significant proportion of patients (pts) who will develop NSCLC. There is a need for further risk stratification in this group. Methods: A retrospective cohort analysis was conducted on pts referred to the BC Cancer Agency – Vancouver Centre for NSCLC. Records were reviewed for the date of first abnormal imaging and 6 clinical factors (CF) noted by the referring clinician at initial presentation. CFs were: ECOG PS > 2, new-onset dyspnea > MRC 3, chest pain, hemoptysis, weight loss > 10% and systemic symptoms (seizure, bone pain, or paraneoplastic syndrome). Individuals meeting current low-dose CT screening criteria (age 55-74, 30 pack-year smoking history within the last 15 years) were also identified. Results: 435 cases were identified from Jan 1 to Dec 31, 2013; 308 had sufficient information to be included for analysis. Median age: 69; smoking history: 69%; stage: I = 5%, II = 9%, III = 26%, IV = 60%. Multivariate analysis identified 4 of 6 CF were associated with worse overall survival (OS, p < 0.05); hemoptysis and weight loss were not significant predictors and were not retained for analysis. Cases were stratified based by the number of CFs. Pts with no CF had significantly improved OS (median 30.5 mo) compared to those with 1 (12.1 mo), 2 (8.1 mo) or 3-4 (2.5 mo; p < 0.001 for all comparisons) CF. Screening criteria were met for 94 pts (31%). For the other 214 pts (69%), number of CF was 0 = 29%, 1 = 29%, 2 = 33%, 3-4 = 9%. OS was similar whether or not pts were eligible for screening. In the subset of ineligible pts, CFs retained their predictive value (p < 0.05). Conclusions: Four clinical factors predict poor outcomes in pts presenting with abnormal imaging suspicious for NSCLC. In this population, 31% of patients would have been eligible for low-dose CT screening. An additional 49% of patients with abnormal imaging had at least one CF identifiable upon initial contact with a healthcare provider. Determination of key clinical factors may assist in risk stratification of pts ineligible for screening who warrant further investigation for lung cancer.

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

Low-dose CT screening in an Asian population with diverse risk for lung cancer: A retrospective cohort study

2015 ◽  
Vol 25 (8) ◽  
pp. 2335-2345 ◽  
Author(s):  
Chin A. Yi ◽  
Kyung Soo Lee ◽  
Myung-Hee Shin ◽  
Yun Yung Cho ◽  
Yoon-Ho Choi ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cohort Study ◽  
Retrospective Cohort Study ◽  
Retrospective Cohort ◽  
Low Dose ◽  
Asian Population ◽  
Ct Screening ◽  
Low Dose Ct

Application “Lung Nodule" Software With Lungrads On Early Detection And Follow Up The Pulmonary Nodules By Lung Low Dose CT Findings

2020 ◽  
Author(s):  
Ha Hoang Thi Ngoc
Keyword(s):  
Lung Cancer ◽  
Early Detection ◽  
Pulmonary Nodule ◽  
Low Dose ◽  
False Negative ◽  
Lung Nodule ◽  
Pulmonary Nodules ◽  
Lung Cancer Mortality ◽  
Smoking History ◽  
Low Dose Ct

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. Content: 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). ACR-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. “Lung nodule” was applied as a smart phone application in order to have a quickly evaluation, especially the malignancy and management face on a pulmonary nodule.

Extent of implementation of low-dose CT in lung cancer screening.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e19177-e19177
Author(s):  
Merin Jose ◽  
Rajesh Desai
Keyword(s):  
Lung Cancer ◽  
Cancer Screening ◽  
High Risk ◽  
Low Dose ◽  
Lung Cancer Screening ◽  
The United States ◽  
Smoking History ◽  
Community Clinic ◽  
Low Dose Ct ◽  
The Impact

e19177 Background: Lung cancer is the leading cause of cancer deaths in the United States with only 15% alive 5 years after diagnosis. In 2013, USPSTF recommended annual screening for LDCT in high risk individuals. Studies had shown a 20% lower mortality (NELSON trial showed significantly lower lung cancer mortality) with LDCT screening. We aimed to assess the extent to which the guideline for lung cancer screening is being adopted in a community clinic. Methods: A retrospective review of electronic medical record of patients aged 55-80 years with no history of lung cancer who visited a primary care provider in a community clinic in New Jersey from October 2014- December 2019 was done. All records with any form of documentation of smoking were identified electronically. The records of those meeting the criteria (30 pack-year smoking history and currently smoking or have quit within the past 15 years) were reviewed manually to check 1) whether they are eligible for screening, 2) if eligible whether low dose CT has been recommended by the provider and 3) once recommended has it been done and followed by the patients. Results: 359 individuals with documented smoking history were identified. Of those 38.8 % (139/359) had a proper documentation (includes both PPD and number of years of smoking) of smoking history based on which high risk individuals could be identified. Of those 37 individuals met the criteria for lung cancer screening. 62% (23/37) had CT chest ordered at some point of time (16.2% for a different indication and the rest for lung cancer screening). Only 52.2% (12/23) of the patients followed the recommendations and got a LDCT done. Among those 50% (6/12) had follow up CT, 50 % (3/6) of those did it on a regular annual basis while the rest 50% (3/6) did it irregularly. 3 patients followed the annual CT screening for lung cancer. Conclusions: Based on these we note that almost half a decade since the recommendation has been established only a small proportion received the care and a still smaller minority followed it. It reflects the dearth of information regarding the guideline among providers and the lack of awareness of the need to follow among patients. This puts forward need for further interventions for implementation of the guidelines at all levels of care for lung cancer prevention. Measures include analyzing the areas of deficiency through questionnaires for patients and providers. Creating awareness on the need for accurate documentation of smoking history and the impact it can have on care delivered. Educating patients about the benefits in health outcome by following the recommendations.

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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