Low-dose Computed Tomography Screening Research Articles

EE114 Cost-Utility Analysis of Lung Cancer Screening Combined With Cardiovascular Disease and Chronic Obstructive Pulmonary Disease Screening

The cost-effectiveness of low-dose computed tomography (LDCT) screening for lung cancer depends on contextual factors and the target screening program. Combined LDCT screening for the Big-3 diseases (lung cancer, cardiovascular disease (CVD) and chronic obstructive pulmonary disease (COPD)) could potentially increase health benefits at marginal additional costs. This study aims to estimate the cost-utility of Big-3 screening compared to usual care (no-screening).

Computed tomography findings, associated factors, and management of pulmonary nodules in 54,326 healthy individuals.

To investigate the pulmonary nodules detected by low-dose computed tomography (LDCT), identified factors affecting the size and number of pulmonary nodules (single or multiple), and the pulmonary nodules diagnosed and management as lung cancer in healthy individuals. A retrospective analysis was conducted on 54,326 healthy individuals who received chest LDCT screening. According to the results of screening, the detection rates of pulmonary nodules, grouped according to the size and number of pulmonary nodules (single or multiple), and the patients' gender, age, history of smoking, hypertension, and diabetes were statistically analyzed to determine the correlation between each factor and the characteristics of the nodules. The pulmonary nodules in healthy individuals diagnosed with lung cancer were managed with differently protocols. The detection rate of pulmonary nodules was 38.8% (21,055/54,326). The baseline demographic characteristics of patients with pulmonary nodules were: 58% male and 42% female patients, 25.7% smoking and 74.3% nonsmoking individuals, 40-60 years old accounted for 49%, 54.8% multiple nodules, and 45.2% single nodules, and ≤5-mm size accounted for 80.4%, 6-10 mm for 18.2%, and 11-30 mm for 1.4%. Multiple pulmonary nodules were more common in hypertensive patients. Diabetes is not an independent risk factor for several pulmonary nodules. Of all patients with lung nodules, 26 were diagnosed with lung cancer, accounting for 0.1% of all patients with pulmonary nodules, 0.6% with nodules ≥5 mm, and 2.2% with nodules ≥8 mm, respectively. Twenty-six patients with lung cancer were treated with surgical resection (57.7%), microwave ablation (MWA, 38.5%), and follow-up (3.8%). LDCT was suitable for large-scale pulmonary nodules screening in healthy individuals, which was helpful for the early detection of suspicious lesions in the lung. In addition to surgical resection, MWA is an option for early lung cancer treatment.

EE575 Cost-Effectiveness of Low-Dose Computed Tomography Screening for Lung Cancer

EE575 Cost-Effectiveness of Low-Dose Computed Tomography Screening for Lung Cancer

Lung CAncer SCreening in French women using low-dose CT and Artificial intelligence for DEtection: the CASCADE study protocol

IntroductionLung cancer screening (LCS) using low-dose CT has been demonstrated to reduce lung cancer-related mortality in large randomised controlled trials. Moving from trials to practice requires answering practical questions about...

The Gap in the Thickness: Estimating Effectiveness of Pulmonary Nodule Detection in Thick- and Thin-Section CT Images with 3D Deep Neural Networks

Background and Objectives.There is a noticeable gap in diagnostic evidence strength between the thick and thin scans of Low-Dose CT (LDCT) for pulmonary nodule detection. When the thin scans are needed is unknown, especially when aided with an artificial intelligence nodule detection system.Methods.A case study is conducted with a set of 1,000 pulmonary nodule screening LDCT scans with both thick (5.0mm), and thin (1.0mm) section scans available. Pulmonary nodule detection is performed by human and artificial intelligence models for nodule detection developed using 3D convolutional neural networks (CNNs). The intra-sample consistency is evaluated with thick and thin scans, for both clinical doctor and NN (neural network) models. Free receiver operating characteristic (FROC) is used to measure the accuracy of humans and NNs.Results.Trained NNs outperform humans with small nodules < 6.0mm, which is a good complement to human ability. For nodules > 6.0mm, human and NNs perform similarly while human takes a fractional advantage. By allowing a few more FPs, a significant sensitivity improvement can be achieved with NNs.Conclusions.There is a performance gap between the thick and thin scans for pulmonary nodule detection regarding both false negatives and false positives. NNs can help reduce false negatives when the nodules are small and trade off the false negatives for sensitivity. A combination of human and trained NNs is a promising way to achieve a fast and accurate diagnosis.

Secondary preventing effect of lung cancer in non-high-risk population: A retrospective investigation of opportunistic screening with low-dose computed tomography in Wuhan.

Given the mortality benefit of low-dose computed tomography (LDCT) screening on high-risk populations, the retrospective investigation intended to identify the benefits of LDCT on lung cancer screening among the general demographic cohorts. We used an opportunistic screening with LDCT implemented during the pandemic in Wuhan to study the impact on subsequent thoracic surgeries, especially surgeries for lung cancer. Patients who received LDCT from October 1, 2019, to July 31, 2020, in three Triple-A accredited hospitals in Wuhan were included in the study. Relative week volumes of both surgeries before and after the chest LDCT screening were compared pairwise. The counts of surgeries for pulmonary nodules or masses, and corresponding pathological results among different gender and age groups were also compared. The relative weekly volumes of thoracic surgery were significantly greater than those of stomach surgery after the opportunistic screening with LDCT. They were 33% (95% CI, 0.20-0.46; p<0. 001) higher than those of stomach surgery. For every 1,000 chest LDCT scans conducted in a given week, on average, 3.52(95% CI,0.56-6.49, p =0.03) thoracic surgeries were performed in the following week. After the implementation of opportunistic screening with LDCT, there was a higher percentage of young females with pulmonary nodule or mass (64.4% vs. 45.8%, p = 0.032). The fraction of lung cancer surgery in the treatment period was significantly greater than that in the control period (74.09% vs. 68.79%, p=0.007). There was a higher percentage of stage I lung cancer surgery in young and mid-age females than in the senior age group (64% vs. 53%, p= 0.05). Opportunistic screening with LDCT can advance the early diagnosis window of lung cancer in non-high-risk populations, especially young women who are easy to be ignored.

Overestimation of screening-related complications in the National Lung Screening Trial

BackgroundLung cancer screening-associated complications are often quoted as one of the major barriers for wider screening adoption. A detailed analysis of the National Lung Screening Trial dataset was performed to extrapolate the safety of lung cancer screening. MethodsLung cancer screening-associated invasive procedures and their related complications were analyzed using the National Lung Screening Trial dataset. Factors associated with 90-day postprocedure complications per patient were analyzed with logistic-regression multivariable analysis. ResultsMajor complications rate in patients undergoing low-dose computed tomography screening who were diagnosed with lung cancer was 10.2% compared with only 0.04% for patients without lung cancer. Low-dose computed tomography screening, compared with chest radiography, led to major complications in an excess of only 3.5 per 10,000 patients without lung cancer. Among 25,633 patients without lung cancer who underwent low-dose computed tomography screening, 45 developed 90-day postprocedure complications (71 total complications). The most common were pneumothorax (n = 29; 41%), postprocedure hospitalization (n = 6; 8.5%), and infection/fever requiring antibiotics (n = 5; 7%). Cardiac/respiratory arrest occurred in less than 1 in 10,000 low-dose computed tomography–screened patients without lung cancer. On multivariable analysis, pulmonary comorbidity (confidence interval, 1.00-3.37) and procedure type (thoracoscopy [confidence interval, 2.04-10.64] or thoracotomy [confidence interval, 2.38-8.93]) were associated with postprocedure complications in patients without lung cancer. Randomization arm (low-dose computed tomography vs chest x-ray) was not a significant factor (confidence interval, 0.89-1.37). ConclusionsIt is more informative to report procedural complications in patients not found to have cancer as the true screening-associated risk. Only 4 in 10,000 of patients undergoing low-dose computed tomography screening but not found to have lung cancer will have major complications. Permanent or debilitating complications are exceedingly rare.

Protocol for a randomized controlled trial of the Enhanced Smoking Cessation Approach to Promote Empowerment (ESCAPE) digitalized intervention to promote lung health in high-risk individuals who smoke

Low dose computed tomography (LDCT) is an effective screening test to decrease lung cancer deaths. Lung cancer screening may be a teachable moment helping people who smoke to quit, which may result in increased benefit of screening. Innovative strategies are needed to engage high-risk individuals in learning about LDCT screening. More precise methods such as polygenic risk scores quantify genetic predisposition to tobacco use, and optimize lung health interventions. We present the ESCAPE (Enhanced Smoking Cessation Approach to Promote Empowerment) protocol. This study will test a smoking cessation intervention using personal stories and a lung cancer screening decision-aide compared to standard care (brief advice, referral to a quit line, and a lung cancer screening decision-aide), examine the relationship between a polygenic risk score and smoking abstinence, and describe perceptions about integration of genomic information into smoking cessation treatment. A randomized controlled trial followed by a sequential explanatory mixed methods approach will compare the efficacy of the interventions. Interviews will add insight into the use of genomic information and risk perceptions to tailor smoking cessation treatment. Two-hundred and fifty individuals will be recruited from primary care, community-based organizations, mailing lists and through social media. Data will be collected at baseline, 1, 3 and 6-months. The primary outcomes are 7-day point prevalence smoking abstinence and stage of lung cancer screening at 6-months. The results from this study will provide information to refine the ESCAPE intervention and facilitate integration of precision health into future lung health interventions.Clinical trial registration number: NCT0469129T.

Development and testing of "Is Lung Cancer Screening for You?" A computer-based decision aid.

To reduce lung cancer mortality, individuals at high-risk should receive a low-dose computed tomography screening annually. To increase the likelihood of screening, interventions that promote shared decision-making are needed. The goal of this study was to investigate the feasibility, acceptability, usability, and preliminary effectiveness of a computer-based decision aid. Thirty-three participants were recruited through primary-care clinics in a small southeastern-US city. Participants used a computer-based decision aid ("Is Lung Cancer Screening for You?") during a clinic appointment. Paper surveys collected self-reported feasibility, acceptability, and usability data. A research coordinator was present to observe each patient's and health-care provider's interactions, and to assess the fidelity of shared decision-making. The decision aid was feasible, acceptable for use in a clinic setting, and easy for participants to use. Patients had low decisional conflict following use of the decision aid and had high screening intention and actual screening rates. Shared decision-making discussions using the decision aid were nearly 6min on average. Computer-based decision aids are feasible for promoting shared lung cancer-screening decisions. A more robust study is warranted to measure the added value of a computer-based version of this aid versus a paper-based aid.

Pulmonary nodule volumetric accuracy of a deep learning-based reconstruction algorithm in low-dose computed tomography: A phantom study

PurposeTo compare the image properties and pulmonary nodule volumetric accuracies among deep learning-based reconstruction (DLR), filtered back projection (FBP), and hybrid iterative reconstruction (hybrid IR) in low-dose computed tomography (LDCT). MethodsA multipurpose chest phantom containing artificial spherical pulmonary nodules with 5-, 8-, 10-, and 12-mm diameters and Hounsfield units (HUs) of −630 and +100 HU was scanned 20 times at a standard dose, based on a low-dose screening CT trial, and at 1/2, 1/6, and 1/12 of the standard dose. To assess noise reduction performance and volumetric accuracy, the standard deviations (SDs) of the pixel values and volumetric percentage errors (PEs) were compared among FBP, hybrid IR, and DLR. The noise non-stationarity index (NNSI) was calculated from 20 image replicates and compared among FBP, hybrid IR, and DLR to evaluate noise stationarity. ResultsThe SD reduction rates for FBP in hybrid IR and DLR were 62 %–85 % and 79 %–90 %, respectively. For the four nodules with +100 HU, the PE of all reconstruction methods was <±25 % (not clinically relevant). For the four nodules with −630 HU, the PEs were equivalent or lower for hybrid IR and DLR than for FBP, and the PE difference between hybrid IR and DLR ranged from 0 % to 7%. The NNSI was significantly higher for DLR than for FBP and hybrid IR (p < 0.01). ConclusionsGreater noise suppression was achieved with DLR than with hybrid IR without compromising nodule volumetric accuracy in LDCT despite the representative noise non-stationarity.

Impact of Low-dose Chest CT Screening on the Association Between Rurality and Lung Cancer Outcomes.

Lung cancer mortality is higher among rural United States populations compared with nonrural ones. Little is known about screening low-dose chest computed tomography (LDCT) outcomes in rural settings. This retrospective cohort study examined all patients (n=1805) who underwent screening LDCT in a prospective registry from March 1, 2015, through December 31, 2019, in a majority-rural health care system. We assessed the proportion of early-stage lung cancers (American Joint Committee on Cancer stage I-II) diagnosed among LDCT-screened patients, and analyzed overall survival after early-stage lung cancer diagnosis according to residency location. The screening cohort had a median age of 63 and median 40-pack-year smoking history; 62.4% had a rural residence, 51.2% were female, and 62.7% completed only 1 LDCT scan. Thirty-eight patients were diagnosed with lung cancer (2.1% of the cohort), of which 65.8% were early-stage. On multivariable analysis, rural (vs nonrural) residency was not associated with a lung cancer diagnosis (adjusted hazard ratio 1.59; 95% CI, 0.74-3.40; P =0.24). At a median follow-up of 37.1 months (range, 3.3 to 67.2 months), 88.2% of rural versus 87.5% of nonrural patients with screen-diagnosed early-stage lung cancer were alive ( P =0.93). In a majority-rural United States population undergoing LDCT, most screen-detected lung cancers were early-stage. There were no significant differences observed between rural and nonrural patients in lung cancer diagnosis rate or early-stage lung cancer survival. Increased implementation of LDCT might blunt the historical association between rural United States populations and worse lung cancer outcomes.

Modeling the impact of novel systemic treatments on lung cancer screening benefits.

Since low-dose computed tomography (LDCT) screening was shown to be effective in the National Lung Screening Trial (NLST), novel targeted therapies and immunotherapies for advanced lung cancer have become available. This study investigated the impact of these treatment advances on the expected benefits of LDCT screening. A microsimulation model of LDCT screening for high-risk individuals under standard systemic treatments (chemotherapy and radiation therapy) and novel treatments (immunotherapy and targeted therapy) was used. The model assumed a reduction in advanced-stage disease consistent with the NLST, and given the stage at diagnosis, it projected survival. The disease-specific relative mortality reduction (MR) due to LDCT screening was projected in the trial setting and in a population eligible for LDCT screening under the current US Preventive Services Task Force (USPSTF) recommendations. The availability of novel treatments reduced the MR in the LDCT arm of the NLST from 15% to 13.5% and the number of lung cancer deaths prevented from 310 to 224 per 100,000 persons screened. Over 10years, population LDCT screening based on USPSTF recommendations prevented 374 lung cancer deaths per 100,000 under standard treatments (13.3% MR) and 236 per 100,000 under fully adopted novel treatments (10.6% MR). The number needed to screen to avert one death over 10 years was 270 under standard treatments and 440 under novel treatments. The transition from standard systemic treatments to novel treatments is expected to reduce the relative and absolute mortality benefits of LDCT screening. Benefit-harm tradeoffs of LDCT screening are likely to change as novel treatments become widespread.

Design and methodological considerations for biomarker discovery and validation in the Integrative Analysis of Lung Cancer Etiology and Risk (INTEGRAL) Program

The Integrative Analysis of Lung Cancer Etiology and Risk (INTEGRAL) program is an NCI-funded initiative with an objective to develop tools to optimize low-dose CT (LDCT) lung cancer screening. Here, we describe the rationale and design for the Risk Biomarker and Nodule Malignancy projects within INTEGRAL. The overarching goal of these projects is to systematically investigate circulating protein markers to include on a panel for use (i) pre-LDCT, to identify people likely to benefit from screening, and (ii) post-LDCT, to differentiate benign versus malignant nodules. To identify informative proteins, the Risk Biomarker project measured 1161 proteins in a nested-case control study within 2 prospective cohorts (n=252 lung cancer cases and 252 controls) and replicated associations for a subset of proteins in 4 cohorts (n=479 cases and 479 controls). Eligible participants had a current or former history of smoking and cases were diagnosed up to 3 years following blood draw. The Nodule Malignancy project measured 1078 proteins among participants with a heavy smoking history within four LDCT screening studies (n=425 cases diagnosed up to 5 years following blood draw, 430 benign-nodule controls, and 398 nodule-free controls). The INTEGRAL panel will enable absolute quantification of 21 proteins. We will evaluate its performance in the Risk Biomarker project using a case-cohort study including 14 cohorts (n=1696 cases and 2926 subcohort representatives), and in the Nodule Malignancy project within five LDCT screening studies (n=675 cases, 680 benign-nodule controls, and 648 nodule-free controls). Future progress to advance lung cancer early detection biomarkers will require carefully designed validation, translational, and comparative studies.

CORRELATING MALIGNANCY RISK FROM AN ARTIFICIAL INTELLIGENCE (AI) ALGORITHM AND LUNG-RADS-BASED CLASSIFICATION FROM SCREENING LOW-DOSE CT IMAGING

CORRELATING MALIGNANCY RISK FROM AN ARTIFICIAL INTELLIGENCE (AI) ALGORITHM AND LUNG-RADS-BASED CLASSIFICATION FROM SCREENING LOW-DOSE CT IMAGING

Improving lung cancer screening in a community healthcare system by electronic health record (EHR) optimization.

437 Background: In March 2021, the United States Preventative Services Taskforce updated its lung cancer screening guidelines to improve overall survival rates, decrease disparities and provide a net cost savings to the healthcare system. With many patients having advanced lung cancer at the time of their diagnosis, it is imperative that screening is performed as soon as applicable. The objective of this study was to improve documentation in EPIC to help prompt low-dose CT (LDCT) scan reminders for providers on the EHR. Methods: This was a prospective study that utilized patient data from EPIC from the Baylor Scott &amp; White Healthcare primary care clinic in Round Rock, Texas. Our version of EPIC used the guidelines prior to the 2021 change. To prompt a reminder on EPIC, there needs to be sufficient numerical data in the smoking history entry form to calculate the pack-years for a patient. EPIC will then automatically generate a notification for providers to order screening LDCT scans for patients that fulfill the screening criteria. We utilized “SlicerDicer” on EPIC to create a custom report of patients that qualified for the study in terms of the inclusion and exclusion criteria. From October 2021 to January 2022, we collected a pre-intervention representative sample to assess how well smoking had been documented during this period. We manually reviewed each medical record number (MRN) listed in the report to verify if proper documentation was used. Our intervention was to contact the clinic manager multiple times with specific instructions on proper smoking history charting. This was then distributed to all ancillary clinic staff via team meetings and flyers. We planned an intervention period from February 2022 to May 2022 that involved data totaling 1178 patients with similar abstraction methods as the pre-intervention data set. Results: We were able to increase lung cancer screening reminders on EPIC for this higher risk patient population. Specifically, 37% of patients pre-intervention had complete data compared to 49% of patients post-intervention. Therefore, an absolute difference of 12% (31.80% increase) of proper documentation was achieved to increase LDCT reminders. The most common mistake by staff was documenting smoking history into the free-text portion of the entry-data forms, which EPIC is not able to calculate pack-years from. Conclusions: Following the intervention at the outpatient clinic, we were able to boost LDCT reminders via an already established process. Increased reminders will eventually lead to more screening orders placed, which we plan to assess for in the next phase of our study. This study can be easily replicated at other centers by instilling similar clinical staff information sessions about documentation. Proper documentation by all healthcare providers is a shared responsibility that has both immediate and longer-lasting impacts on an individual and population level.

Lung cancer screening with low-dose computed tomography: National expenditures and cost-effectiveness.

ObjectiveTo compare the cost-effectiveness of undertaking low-dose computed tomography (LDCT) screening for early detection of lung cancer (LC) with different frequencies within the healthcare system of China, and estimate the additional national healthcare expenditure and five-year LC mortality associated with different screening frequencies.Material and methodsA Markov model was established using national LC epidemiological data from the Chinese Center for Disease Control and Prevention, demographic data from the Chinese Statistical Yearbook, and cost and effectiveness data mainly from the Cancer Screening Program in China. The model included thirty sex-specific screening strategies, which were classified by initial screening age (30, 35, 40, 45, and 50), and screening intervals (intervals at single time point, 1, 2, 5, 10, and 20 years). The main model outputs were incremental cost-effectiveness ratios (ICERs), additional national healthcare expenditure and five-year LC mortality.ResultsThe ICERs for LDCT screening strategies vs. non-screening strategy ranged from $16,086 per quality-adjusted life-year (QALY) to $3,675,491 per QALY in the male cohort, and from $36,624 per QALY to $5,943,556 per QALY in the female cohort. The annual increment national healthcare expenditures related to LDCT screening were varied from $0.25 to $13.39 billion, with the lower cost in the cohort with older screening ages and lower screening frequencies. More frequent screening with LDCT was associated with a greater reduction in LC death: an annual LDCT screening was linked to an estimated reduction in five-year LC death by 27.27–29.07%, while a one-off screening was linked to a reduction by 5.56–5.83%.ConclusionUnder a willingness-to-pay (WTP) threshold of three times the Chinese gross domestic product (GDP) per capita (US $37,654), annual screening with an initiating age at 50 was most cost-effective in both male and female cohorts. By taking into account both the national healthcare expenditures and the effect of LDCT screening, our study results support undertaking LDCT screening annually from 50 years old in general populations.

Single CT Appointment for Double Lung and Colorectal Cancer Screening: Is the Time Ripe?

Annual screening of lung cancer (LC) with chest low-dose computed tomography (CT) and screening of colorectal cancer (CRC) with CT colonography every 5 years are recommended by the United States Prevention Service Task Force. We review epidemiological and pathological data on LC and CRC, and the features of screening chest low-dose CT and CT colonography comprising execution, reading, radiation exposure and harm, and the cost effectiveness of the two CT screening interventions. The possibility of combining chest low-dose CT and CT colonography examinations for double LC and CRC screening in a single CT appointment is then addressed. We demonstrate how this approach appears feasible and is already reasonable as an opportunistic screening intervention in 50–75-year-old subjects with smoking history and average CRC risk. In addition to the crucial role Computer Assisted Diagnosis systems play in decreasing the test reading times and the need to educate radiologists in screening chest LDCT and CT colonography, in view of a single CT appointment for double screening, the following uncertainties need to be solved: (1) the schedule of the screening CT; (2) the effectiveness of iterative reconstruction and deep learning algorithms affording an ultra-low-dose CT acquisition technique and (3) management of incidental findings. Resolving these issues will imply new cost-effectiveness analyses for LC screening with chest low dose CT and for CRC screening with CT colonography and, especially, for the double LC and CRC screening with a single-appointment CT.

Early Diagnosis and Lung Cancer Screening

Lung cancer remains the most significant cause of cancer death, accounting for about 20% of all cancer-related mortality. A significant reason for this is delayed diagnosis, either due to lack of symptoms in early-stage disease or presentation with non-specific symptoms common with a broad range of alternative diagnoses. More is needed in terms of increasing public awareness, providing adequate healthcare professional education and implementing clinical pathways that improve the earlier diagnosis of symptomatic lung cancer. Low-dose computed tomography screening of high-risk, asymptomatic populations has been shown to reduce lung cancer mortality, with focus now shifting towards how best to implement lung cancer screening on a wider scale in a safe, efficient and cost-effective manner. For maximum benefit, efforts must be made to optimise uptake, especially among high-risk populations with significant socioeconomic deprivation, as well as successfully incorporate tobacco-dependency treatment. Quality assured programme management will be critical to minimising screening-related harms and adequately managing incidental findings. By undertaking the above, there can be optimism that lung cancer outcomes can be improved significantly in the near future.

Recalibrating Risk Prediction Models by Synthesizing Data Sources: Adapting the Lung Cancer PLCO Model for Taiwan.

Methods synthesizing multiple data sources without prospective datasets have been proposed for absolute risk model development. This study proposed methods for adapting risk models for another population without prospective cohorts, which would help alleviate the health disparities caused by advances in absolute risk models. To exemplify, we adapted the lung cancer risk model PLCOM2012, well studied in the west, for Taiwan. Using Taiwanese multiple data sources, we formed an age-matched case-control study of ever-smokers (AMCCSE), estimated the number of ever-smoking lung cancer patients in 2011-2016 (NESLP2011), and synthesized a dataset resembling the population of cancer-free ever-smokers in 2010 regarding the PLCOM2012 risk factors (SPES2010). The AMCCSE was used to estimate the overall calibration slope, and the requirement that NESLP2011 equals the estimated total risk of individuals in SPES2010 was used to handle the calibration-in-the-large problem. The adapted model PLCOT-1 (PLCOT-2) had an AUC of 0.78 (0.75). They had high performance in calibration and clinical usefulness on subgroups of SPES2010 defined by age and smoking experience. Selecting the same number of individuals for low-dose computed tomography screening using PLCOT-1 (PLCOT-2) would have identified approximately 6% (8%) more lung cancers than the US Preventive Services Task Forces 2021 criteria. Smokers having 40+ pack-years had an average PLCOT-1 (PLCOT-2) risk of 3.8% (2.6%). The adapted PLCOT models had high predictive performance. The PLCOT models could be used to design lung cancer screening programs in Taiwan. The methods could be applicable to other cancer models.

The 50-Year Journey of Lung Cancer Screening: A Narrative Review.

Early diagnosis and treatment are associated with better outcomes in oncology. We reviewed the existing literature using the search terms “low dose computed tomography” and “lung cancer screening” for systematic reviews, metanalyses, and randomized as well as non-randomized clinical trials in PubMed from January 1, 1963 to April 30, 2022. The studies were heterogeneous and included people with different age groups, smoking histories, and other specific risk scores for lung cancer screening. Based on the available evidence, almost all the guidelines recommend screening for lung cancer by annual low dose CT (LDCT) in populations over 50 to 55 years of age, who are either current smokers or have left smoking less than 15 years back with more than 20 to 30 pack-years of smoking. “LDCT screening” can reduce lung cancer mortality if carried out judiciously in countries with adequate resources and infrastructure.