The US Preventive Services Task Force Recommendation on Lung Cancer Screening

Squires, Bryan; Levitin, R.; Grills, I.S.

doi:10.1001/jama.2021.8240

Cited by 38 publications

(57 citation statements)

References 4 publications

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“…Regarding the demise of this 75-year-old male farmer who presented with features of advanced bronchogenic carcinoma with secondary liver metastasis and sepsis on his initial visit to the hospital, this shows the falling standard of preventive care in a typical developing country, as this patient did not benefit from the required regular personal interaction from a primary caregiver, his prevailing risk factor was not discussed, smoking cessation interventions were not offered, and periodic screening for bronchogenic carcinoma was not done. This is in contrast with the measures taken in developed countries, especially in the US, where the United States Preventive Services Task Force (USPSTF) recommends screening for lung cancer with low-dose computed tomography (CT) scan every for adults aged 50 to 80 years who have a 20 pack-year smoking history and currently smoke or have quit smoking within the past 15 years, and discontinuation of screening once a person has not smoked for 15 years or has a health problem that limits life expectancy or the ability to have a lung surgery [11].…”

Section: Discussionmentioning

confidence: 99%

Advanced Bronchogenic Carcinoma with Secondary Liver Metastasis and Sepsis as an Initial Hospital Presentation; The Shortcomings of Preventive Care Medicine in Nigeria

Endurance,

Hannah,

Evidence

et al. 2022

International Journal of Scientific Advances

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Chronic diseases are the most prevalent worldwide. Evidence-based medicine points to primary care as the most effective tool to minimize the burden through primary prevention and early detection of diseases. In keeping with this, the primary care arm of medicine can be regarded as the gatekeeper of public health. The rising incidence of chronic diseases indicates that the primary care system may fail to live up to its responsibility. We present a case of an elderly male who came to the hospital with advanced bronchogenic carcinoma, secondary liver metastasis, and sepsis. He presented for the first time in over a year since he started experiencing symptoms. He had no contact with his primary care physicians, nor was he vaccinated against the influenza virus or the coronavirus.

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Section: Discussionmentioning

confidence: 99%

Advanced Bronchogenic Carcinoma with Secondary Liver Metastasis and Sepsis as an Initial Hospital Presentation; The Shortcomings of Preventive Care Medicine in Nigeria

Endurance,

Hannah,

Evidence

et al. 2022

International Journal of Scientific Advances

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“…Generally, lung cancer patients face a poor prognosis, with a five-year survival rate of only 21% [3]. To improve patient outcomes by identifying cancer early, the US Preventative Services Task Force recommends high-risk adults over 50 years old to undergo yearly screening with low dose computed tomography (LDCT) [4]. Survival analysis, or the estimation of time until death, from a lung cancer patient's LDCT scans can help plan patient care and management.…”

Section: Introductionmentioning

confidence: 99%

Survival prediction of NLST lung cancer patients from radiomics and clinical data

Leu

Subramanian

Dasso

et al. 2023

Preprint

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Background: It is critical to accurately predict the survival likelihood of cancer patients to allow the best care and treatment. Publicly available datasets have emerged recently, such as the National Lung Screening Trial (NLST) data with low-dose computed tomography (LDCT) scans of at-risk populations. Recent research focuses primarily on improving survival prediction performance by developing more complex models without sufficient focus on interpretability. In contrast, this study focuses on identifying and analyzing the importance of different radiology features and clinical variables in survival prediction. Methods: This research used the NLST data with two widely-used predictive models - the Cox proportional hazards (CPH) model and the random survival forests (RSF). The first step was to generate semi-automated primary nodule annotations for an NLST subset of adenocarcinoma patients with the close help of a radiologist and extract commonly-used radiomic features to characterize these primary nodules. By coupling the radiomic features with the patient's clinical data, the models predict death by lung cancer from the first LDCT scan. The next step is to construct smaller subsets of influential features and demonstrates that these subsets preserve survival performance. Additionally, this study investigated the potential of combining radiomic features and clinical data in survival prediction. Lastly, to make similar studies on the NLST more feasible, the semi-automated nodule segmentations for the NLST subset used in this study were provided for public use along with the code for the experiments at https://github.com/hleu/survival_nlst. Results: The best result of 67.06 C-index and a mean time-dependent area under the receiver operating characteristic (TD-AUC) of 71.27 was obtained by using CPH models with a combination of a subset of clinical features and shape-based radiomic features. Conclusions: The first contribution is the nodule annotation, segmentation, and exact feature extraction from the NLST dataset. This annotated dataset increases the model performance for lung cancer prognosis. Secondly, this study applied two different survival analysis methods to radiology and clinical features and compared the results obtained from different techniques. The models with a combination of features can outperform the model with only radiomics or clinical features.

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“…The US Preventative Services Task Force (USPTF) has recommended routine low-dose computed tomography (LDTC) LC screening of a defined population of high risk individuals ( 4 ). The USPTF however found that LDTC screening was associated with harms which included high false positive rates resulting in unnecessary tests and invasive procedures, incidental non-cancerous findings, overdiagnosis and radiation exposure.…”

Section: Introductionmentioning

confidence: 99%

“…The USPTF however found that LDTC screening was associated with harms which included high false positive rates resulting in unnecessary tests and invasive procedures, incidental non-cancerous findings, overdiagnosis and radiation exposure. They therefore called for more research to develop biomarkers to improve the detection rate and lower the false positive rate of LDTC screening ( 4 ).…”

Section: Introductionmentioning

confidence: 99%

Precision Oncology: Artificial Intelligence and DNA Methylation Analysis of Circulating Cell-Free DNA for Lung Cancer Detection

et al. 2022

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BackgroundLung cancer (LC) is a leading cause of cancer-deaths globally. Its lethality is due in large part to the paucity of accurate screening markers. Precision Medicine includes the use of omics technology and novel analytic approaches for biomarker development. We combined Artificial Intelligence (AI) and DNA methylation analysis of circulating cell-free tumor DNA (ctDNA), to identify putative biomarkers for and to elucidate the pathogenesis of LC.MethodsIllumina Infinium MethylationEPIC BeadChip array analysis was used to measure cytosine (CpG) methylation changes across the genome in LC. Six different AI platforms including support vector machine (SVM) and Deep Learning (DL) were used to identify CpG biomarkers and for LC detection. Training set and validation sets were generated, and 10-fold cross validation performed. Gene enrichment analysis using g:profiler and GREAT enrichment was used to elucidate the LC pathogenesis.ResultsUsing a stringent GWAS significance threshold, p-value <5x10-8, we identified 4389 CpGs (cytosine methylation loci) in coding genes and 1812 CpGs in non-protein coding DNA regions that were differentially methylated in LC. SVM and three other AI platforms achieved an AUC=1.00; 95% CI (0.90-1.00) for LC detection. DL achieved an AUC=1.00; 95% CI (0.95-1.00) and 100% sensitivity and specificity. High diagnostic accuracies were achieved with only intragenic or only intergenic CpG loci. Gene enrichment analysis found dysregulation of molecular pathways involved in the development of small cell and non-small cell LC.ConclusionUsing AI and DNA methylation analysis of ctDNA, high LC detection rates were achieved. Further, many of the genes that were epigenetically altered are known to be involved in the biology of neoplasms in general and lung cancer in particular.

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The US Preventive Services Task Force Recommendation on Lung Cancer Screening

Abstract: and receiving personal fees and equity from Nava Health. No other disclosures were reported.

Cited by 38 publications

References 4 publications

Advanced Bronchogenic Carcinoma with Secondary Liver Metastasis and Sepsis as an Initial Hospital Presentation; The Shortcomings of Preventive Care Medicine in Nigeria

Advanced Bronchogenic Carcinoma with Secondary Liver Metastasis and Sepsis as an Initial Hospital Presentation; The Shortcomings of Preventive Care Medicine in Nigeria

Survival prediction of NLST lung cancer patients from radiomics and clinical data

Precision Oncology: Artificial Intelligence and DNA Methylation Analysis of Circulating Cell-Free DNA for Lung Cancer Detection

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