Nationwide study on development and validation of a risk prediction model for CIN3+ and cervical cancer in Estonia.

Transitioning to an individualized risk-based approach can significantly enhance cervical cancer screening programs. We aimed to derive and internally validate a prediction model for assessing the risk of cervical intraepithelial neoplasia grade 3 or higher (CIN3+) and cancer in women eligible for screening. This retrospective study utilized data from the Estonian electronic health records, including 517,884 women from the health insurance database and linked health registries. We employed Cox proportional hazard regression, incorporating reproductive and medical history variables (14 covariates), and utilized the least absolute shrinkage and selection operator (LASSO) for variable selection. A 10-fold cross-validation for internal validation of the model was used. The main outcomes were the performance of discrimination and calibration. Over the 8-year follow-up, we identified 1326 women with cervical cancer and 5929 with CIN3+, with absolute risks of 0.3% and 1.1%, respectively. The prediction model for CIN3 + and cervical cancer had good discriminative power and was well calibrated Harrell's C of 0.74 (0.73-0.74) (calibration slope 1.00 (0.97-1.02) and 0.67 (0.66-0.69) (calibration slope 0.92 (0.84-1.00) respectively. A developed model based on nationwide electronic health data showed potential utility for risk stratification to supplement screening efforts. This work was supported through grants number PRG2218 from the Estonian Research Council, and EMP416 from the EEA (European Economic Area) and Norway Grants.

Improving Triage Accuracy in Prehospital Emergency Telemedicine: Scoping Review of Machine Learning-Enhanced Approaches.

BACKGROUND: Prehospital telemedicine triage systems combined with machine learning (ML) methods have the potential to improve triage accuracy and safely redirect low-acuity patients from attending the emergency department. However, research in prehospital settings is limited but needed; emergency department overcrowding and adverse patient outcomes are increasingly common. OBJECTIVE: In this scoping review, we sought to characterize the existing methods for ML-enhanced telemedicine emergency triage. In order to support future research, we aimed to delineate what data sources, predictors, labels, ML models, and performance metrics were used, and in which telemedicine triage systems these methods were applied. METHODS: A scoping review was conducted, querying multiple databases (MEDLINE, PubMed, Scopus, and IEEE Xplore) through February 24, 2023, to identify potential ML-enhanced methods, and for those eligible, relevant study characteristics were extracted, including prehospital triage setting, types of predictors, ground truth labeling method, ML models used, and performance metrics. Inclusion criteria were restricted to the triage of emergency telemedicine services using ML methods on an undifferentiated (disease nonspecific) population. Only primary research studies in English were considered. Furthermore, only those studies using data collected remotely (as opposed to derived from physical assessments) were included. In order to limit bias, we exclusively included articles identified through our predefined search criteria and had 3 researchers (DR, JS, and KS) independently screen the resulting studies. We conducted a narrative synthesis of findings to establish a knowledge base in this domain and identify potential gaps to be addressed in forthcoming ML-enhanced methods. RESULTS: A total of 165 unique records were screened for eligibility and 15 were included in the review. Most studies applied ML methods during emergency medical dispatch (7/15, 47%) or used chatbot applications (5/15, 33%). Patient demographics and health status variables were the most common predictors, with a notable absence of social variables. Frequently used ML models included support vector machines and tree-based methods. ML-enhanced models typically outperformed conventional triage algorithms, and we found a wide range of methods used to establish ground truth labels. CONCLUSIONS: This scoping review observed heterogeneity in dataset size, predictors, clinical setting (triage process), and reported performance metrics. Standard structured predictors, including age, sex, and comorbidities, across articles suggest the importance of these inputs; however, there was a notable absence of other potentially useful data, including medications, social variables, and health system exposure. Ground truth labeling practices should be reported in a standard fashion as the true model performance hinges on these labels. This review calls for future work to form a standardized framework, thereby supporting consistent reporting and performance comparisons across ML-enhanced prehospital triage systems.

From the International Association for the Study of Lung Cancer Early Detection and Screening Committee: Terminology Issues in Screening and Early Detection of Lung Cancer-International Association for the Study of Lung Cancer Early Detection and Screening Committee Expert Group Recommendations.

INTRODUCTION: To facilitate global implementation of lung cancer (LC) screening and early detection in a quality assured and consistent manner, common terminology is needed. Researchers and clinicians within different specialties may use the same terms but with different meanings or different terms for the same intended meanings. METHODS: The Diagnostics Working Group of the International Association for the Study of Lung Cancer Early Detection and Screening Committee has analyzed and discussed relevant terms used on a regular basis and suggests recommendations for consensus definitions of terminology applicable in this setting. We explored how to reach consensus to define relevant and unambiguous terminology for use by health care providers, researchers, patients, screening participants, and family. RESULTS: Terms and definitions for epidemiologic and health-economical purposes included the following: standardized incidence and mortality rates, LC-specific survival, long-term survival and cure rates, overdiagnosis, overtreatment, and undertreatment. Terms and definitions for defining screening findings included the following: positive, false-positive, negative, false-negative, and indeterminate findings and additional and incidental findings. Terms and definitions for describing parameters in screening programs included the following: opportunistic versus programmatic screening, screening rounds, interval or interim diagnoses, and invasive and minimally invasive procedures. Terms and definitions for shared decision-making included the following: LC screening-possible harms and risks and LC risk and modifiers prior and posterior to a measure. CONCLUSIONS: A common set of terminology with standard definitions is recommended for describing clinical LC screening programs, the discussion about effectiveness and outcomes, or the clinical setting. The use of the terms should be clearly defined and explained.

Estimating sojourn time and sensitivity of screening for ovarian cancer using a Bayesian framework.

BACKGROUND: Ovarian cancer is among the leading causes of gynecologic cancer-related death. Past ovarian cancer screening trials using combination of cancer antigen 125 testing and transvaginal ultrasound failed to yield statistically significant mortality reduction. Estimates of ovarian cancer sojourn time-that is, the period from when the cancer is first screen detectable until clinical detection-may inform future screening programs. METHODS: We modeled ovarian cancer progression as a continuous time Markov chain and estimated screening modality-specific sojourn time and sensitivity using a Bayesian approach. Model inputs were derived from the screening arms (multimodal and ultrasound) of the UK Collaborative Trial of Ovarian Cancer Screening and the Prostate, Lung, Colorectal and Ovarian cancer screening trials. We assessed the quality of our estimates by using the posterior predictive P value. We derived histology-specific sojourn times by adjusting the overall sojourn time based on the corresponding histology-specific survival from the Surveillance, Epidemiology, and End Results Program. RESULTS: The overall ovarian cancer sojourn time was 2.1 years (posterior predictive P value = .469) in the Prostate, Lung, Colorectal and Ovarian studies, with 65.7% screening sensitivity. The sojourn time was 2.0 years (posterior predictive P value = .532) in the United Kingdom Collaborative Trial of Ovarian Cancer Screening's multimodal screening arm and 2.4 years (posterior predictive P value = .640) in the ultrasound screening arm, with sensitivities of 93.2% and 64.5%, respectively. Stage-specific screening sensitivities in the Prostate, Lung, Colorectal and Ovarian studies were 39.1% and 82.9% for early-stage and advanced-stage disease, respectively. The histology-specific sojourn times ranged from 0.8 to 1.8 years for type II ovarian cancer and 2.9 to 6.6 years for type I ovarian cancer. CONCLUSIONS: Annual screening is not effective for all ovarian cancer subtypes. Screening sensitivity for early-stage ovarian cancers is not sufficient for substantial mortality reduction.

Recommendations to Improve Management of Incidental Pulmonary Nodules in Canada: Expert Panel Consensus.

Introduction: Incidental pulmonary nodules (IPN) are common radiologic findings, yet management of IPNs is inconsistent across Canada. This study aims to improve IPN management based on multidisciplinary expert consensus and provides recommendations to overcome patient and system-level barriers. Methods: A modified Delphi consensus technique was conducted. Multidisciplinary experts with extensive experience in lung nodule management in Canada were recruited to participate in the panel. A survey was administered in 3 rounds, using a 5-point Likert scale to determine the level of agreement (1 = extremely agree, 5 = extremely disagree). Results: Eleven experts agreed to participate in the panel; 10 completed all 3 rounds. Consensus was achieved for 183/217 (84.3%) statements. Panellists agreed that radiology reports should include a standardized summary of findings and follow-up recommendations for all nodule sizes (ie, <6, 6-8, and >8 mm). There was strong consensus regarding the importance of an automated system for patient follow-up and that leadership support for organizational change at the administrative level is of utmost importance in improving IPN management. There was no consensus on the need for standardized national referral pathways, development of new guidelines, or establishing a uniform picture archiving and communication system. Conclusion: Canadian IPN experts agree that improved IPN management should include standardized radiology reporting of IPNs, standardized and automated follow-up of patients with IPNs, guideline adherence and implementation, and leadership support for organizational change. Future research should focus on the implementation and long-term effectiveness of these recommendations in clinical practice.

Risk-based lung cancer screening performance in a universal healthcare setting.

Globally, lung cancer is the leading cause of cancer death. Previous trials demonstrated that low-dose computed tomography lung cancer screening of high-risk individuals can reduce lung cancer mortality by 20% or more. Lung cancer screening has been approved by major guidelines in the United States, and over 4,000 sites offer screening. Adoption of lung screening outside the United States has, until recently, been slow. Between June 2017 and May 2019, the Ontario Lung Cancer Screening Pilot successfully recruited 7,768 individuals at high risk identified by using the PLCOm2012noRace lung cancer risk prediction model. In total, 4,451 participants were successfully screened, retained and provided with high-quality follow-up, including appropriate treatment. In the Ontario Lung Cancer Screening Pilot, the lung cancer detection rate and the proportion of early-stage cancers were 2.4% and 79.2%, respectively; serious harms were infrequent; and sensitivity to detect lung cancers was 95.3% or more. With abnormal scans defined as ones leading to diagnostic investigation, specificity was 95.5% (positive predictive value, 35.1%), and adherence to annual recall and early surveillance scans and clinical investigations were high (>85%). The Ontario Lung Cancer Screening Pilot provides insights into how a risk-based organized lung screening program can be implemented in a large, diverse, populous geographic area within a universal healthcare system.

Risk prediction models for lung cancer in people who have never smoked: a protocol of a systematic review.

BACKGROUND: Lung cancer is one of the most commonly diagnosed cancers and the leading cause of cancer-related death worldwide. Although smoking is the primary cause of the cancer, lung cancer is also commonly diagnosed in people who have never smoked. Currently, the proportion of people who have never smoked diagnosed with lung cancer is increasing. Despite this alarming trend, this population is ineligible for lung screening. With the increasing proportion of people who have never smoked among lung cancer cases, there is a pressing need to develop prediction models to identify high-risk people who have never smoked and include them in lung cancer screening programs. Thus, our systematic review is intended to provide a comprehensive summary of the evidence on existing risk prediction models for lung cancer in people who have never smoked. METHODS: Electronic searches will be conducted in MEDLINE (Ovid), Embase (Ovid), Web of Science Core Collection (Clarivate Analytics), Scopus, and Europe PMC and Open-Access Theses and Dissertations databases. Two reviewers will independently perform title and abstract screening, full-text review, and data extraction using the Covidence review platform. Data extraction will be performed based on the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS). The risk of bias will be evaluated independently by two reviewers using the Prediction model Risk-of-Bias Assessment Tool (PROBAST) tool. If a sufficient number of studies are identified to have externally validated the same prediction model, we will combine model performance measures to evaluate the model's average predictive accuracy (e.g., calibration, discrimination) across diverse settings and populations and explore sources of heterogeneity. DISCUSSION: The results of the review will identify risk prediction models for lung cancer in people who have never smoked. These will be useful for researchers planning to develop novel prediction models, and for clinical practitioners and policy makers seeking guidance for clinical decision-making and the formulation of future lung cancer screening strategies for people who have never smoked. SYSTEMATIC REVIEW REGISTRATION: This protocol has been registered in PROSPERO under the registration number CRD42023483824.

Radiomics analysis to predict pulmonary nodule malignancy using machine learning approaches.

BACKGROUND: Low-dose CT screening can reduce lung cancer-related mortality. However, most screen-detected pulmonary abnormalities do not develop into cancer and it often remains challenging to identify malignant nodules, particularly among indeterminate nodules. We aimed to develop and assess prediction models based on radiological features to discriminate between benign and malignant pulmonary lesions detected on a baseline screen. METHODS: Using four international lung cancer screening studies, we extracted 2060 radiomic features for each of 16 797 nodules (513 malignant) among 6865 participants. After filtering out low-quality radiomic features, 642 radiomic and 9 epidemiological features remained for model development. We used cross-validation and grid search to assess three machine learning (ML) models (eXtreme Gradient Boosted Trees, random forest, least absolute shrinkage and selection operator (LASSO)) for their ability to accurately predict risk of malignancy for pulmonary nodules. We report model performance based on the area under the curve (AUC) and calibration metrics in the held-out test set. RESULTS: The LASSO model yielded the best predictive performance in cross-validation and was fit in the full training set based on optimised hyperparameters. Our radiomics model had a test-set AUC of 0.93 (95% CI 0.90 to 0.96) and outperformed the established Pan-Canadian Early Detection of Lung Cancer model (AUC 0.87, 95% CI 0.85 to 0.89) for nodule assessment. Our model performed well among both solid (AUC 0.93, 95% CI 0.89 to 0.97) and subsolid nodules (AUC 0.91, 95% CI 0.85 to 0.95). CONCLUSIONS: We developed highly accurate ML models based on radiomic and epidemiological features from four international lung cancer screening studies that may be suitable for assessing indeterminate screen-detected pulmonary nodules for risk of malignancy.

Risk model-based management for second primary lung cancer among lung cancer survivors through a validated risk prediction model.

BACKGROUND: Recent therapeutic advances and screening technologies have improved survival among patients with lung cancer, who are now at high risk of developing second primary lung cancer (SPLC). Recently, an SPLC risk-prediction model (called SPLC-RAT) was developed and validated using data from population-based epidemiological cohorts and clinical trials, but real-world validation has been lacking. The predictive performance of SPLC-RAT was evaluated in a hospital-based cohort of lung cancer survivors. METHODS: The authors analyzed data from 8448 ever-smoking patients diagnosed with initial primary lung cancer (IPLC) in 1997-2006 at Mayo Clinic, with each patient followed for SPLC through 2018. The predictive performance of SPLC-RAT and further explored the potential of improving SPLC detection through risk model-based surveillance using SPLC-RAT versus existing clinical surveillance guidelines. RESULTS: Of 8448 IPLC patients, 483 (5.7%) developed SPLC over 26,470 person-years. The application of SPLC-RAT showed high discrimination area under the receiver operating characteristics curve: 0.81). When the cohort was stratified by a 10-year risk threshold of ≥5.6% (i.e., 80th percentile from the SPLC-RAT development cohort), the observed SPLC incidence was significantly elevated in the high-risk versus low-risk subgroup (13.1% vs. 1.1%, p < 1 × 10-6 ). The risk-based surveillance through SPLC-RAT (≥5.6% threshold) outperformed the National Comprehensive Cancer Network guidelines with higher sensitivity (86.4% vs. 79.4%) and specificity (38.9% vs. 30.4%) and required 20% fewer computed tomography follow-ups needed to detect one SPLC (162 vs. 202). CONCLUSION: In a large, hospital-based cohort, the authors validated the predictive performance of SPLC-RAT in identifying high-risk survivors of SPLC and showed its potential to improve SPLC detection through risk-based surveillance. PLAIN LANGUAGE SUMMARY: Lung cancer survivors have a high risk of developing second primary lung cancer (SPLC). However, no evidence-based guidelines for SPLC surveillance are available for lung cancer survivors. Recently, an SPLC risk-prediction model was developed and validated using data from population-based epidemiological cohorts and clinical trials, but real-world validation has been lacking. Using a large, real-world cohort of lung cancer survivors, we showed the high predictive accuracy and risk-stratification ability of the SPLC risk-prediction model. Furthermore, we demonstrated the potential to enhance efficiency in detecting SPLC using risk model-based surveillance strategies compared to the existing consensus-based clinical guidelines, including the National Comprehensive Cancer Network.

Risk Model-Based Lung Cancer Screening and Racial and Ethnic Disparities in the US.

Importance: The revised 2021 US Preventive Services Task Force (USPSTF) guidelines for lung cancer screening have been shown to reduce disparities in screening eligibility and performance between African American and White individuals vs the 2013 guidelines. However, potential disparities across other racial and ethnic groups in the US remain unknown. Risk model-based screening may reduce racial and ethnic disparities and improve screening performance, but neither validation of key risk prediction models nor their screening performance has been examined by race and ethnicity. Objective: To validate and recalibrate the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial 2012 (PLCOm2012) model-a well-established risk prediction model based on a predominantly White population-across races and ethnicities in the US and evaluate racial and ethnic disparities and screening performance through risk-based screening using PLCOm2012 vs the USPSTF 2021 criteria. Design, Setting, and Participants: In a population-based cohort design, the Multiethnic Cohort Study enrolled participants in 1993-1996, followed up through December 31, 2018. Data analysis was conducted from April 1, 2022, to May 19. 2023. A total of 105 261 adults with a smoking history were included. Exposures: The 6-year lung cancer risk was calculated through recalibrated PLCOm2012 (ie, PLCOm2012-Update) and screening eligibility based on a 6-year risk threshold greater than or equal to 1.3%, yielding similar eligibility as the USPSTF 2021 guidelines. Outcomes: Predictive accuracy, screening eligibility-incidence (E-I) ratio (ie, ratio of the number of eligible to incident cases), and screening performance (sensitivity, specificity, and number needed to screen to detect 1 lung cancer). Results: Of 105 261 participants (60 011 [57.0%] men; mean [SD] age, 59.8 [8.7] years), consisting of 19 258 (18.3%) African American, 27 227 (25.9%) Japanese American, 21 383 (20.3%) Latino, 8368 (7.9%) Native Hawaiian/Other Pacific Islander, and 29 025 (27.6%) White individuals, 1464 (1.4%) developed lung cancer within 6 years from enrollment. The PLCOm2012-Update showed good predictive accuracy across races and ethnicities (area under the curve, 0.72-0.82). The USPSTF 2021 criteria yielded a large disparity among African American individuals, whose E-I ratio was 53% lower vs White individuals (E-I ratio: 9.5 vs 20.3; P < .001). Under the risk-based screening (PLCOm2012-Update 6-year risk ≥1.3%), the disparity between African American and White individuals was substantially reduced (E-I ratio: 15.9 vs 18.4; P < .001), with minimal disparities observed in persons of other minoritized groups, including Japanese American, Latino, and Native Hawaiian/Other Pacific Islander. Risk-based screening yielded superior overall and race and ethnicity-specific performance to the USPSTF 2021 criteria, with higher overall sensitivity (67.2% vs 57.7%) and lower number needed to screen (26 vs 30) at similar specificity (76.6%). Conclusions: The findings of this cohort study suggest that risk-based lung cancer screening can reduce racial and ethnic disparities and improve screening performance across races and ethnicities vs the USPSTF 2021 criteria.

Sensitivity of US Preventive Services Task Force and PLCOm2012 lung cancer screening eligibility criteria in individuals with lung cancer in South Dakota self-reporting as Indigenous and non-Indigenous.

BACKGROUND: Lung cancer is the leading cause of cancer deaths. Screening individuals who are at elevated risk using low-dose computed tomography reduces lung cancer mortality by ≥20%. Individuals who have community-based factors that contribute to an increased risk of developing lung cancer have high lung cancer rates and are diagnosed at younger ages. In this study of lung cancer in South Dakota, the authors compared the sensitivity of screening eligibility criteria for self-reported Indigenous race and evaluated the need for screening at younger ages. METHODS: US Preventive Services Task Force (USPSTF) 2013 and 2021 (USPSTF2013 and USPSTF2021) criteria and two versions of the PLCOm2012 risk-prediction model (based on the 2012 Prostate, Lung, Colorectal, and Ovarian [PLCO] Cancer Screening Trial), one with a predictor for race and one without, were applied at USPSTF-equivalent thresholds of ≥1.7% in 6 years and ≥1.0% in 6 years to 1565 individuals who were sequentially diagnosed with lung cancer (of whom 12.7% self-reported as Indigenous) at the Monument Health Cancer Care Institute in South Dakota (2010-2019). RESULTS: Eligibility sensitivities of USPSTF criteria did not differ significantly between individuals who self-reported their race as Indigenous and those who did not (p > .05). Sensitivities of both PLCOm2012 models were significantly higher than comparable USPSTF criteria. The sensitivity of USPSTF2021 criteria was 66.1% and, for comparable PLCOm2012 models with and without race, sensitivity was 90.7% and 89.6%, respectively (both p < .001); 1.4% of individuals were younger than 50 years, and proportions did not differ by Indigenous classification (p = .518). CONCLUSIONS: Disparities in screening eligibility were not observed for individuals who self-reported their race as Indigenous. USPSTF criteria had lower sensitivities for lung cancer eligibility. Both PLCOm2012 models had high sensitivities, with higher sensitivity for the model that included race. The PLCOm2012noRace model selected effectively in this population, and screening individuals younger than 50 years did not appear to be justified. PLAIN LANGUAGE SUMMARY: Lung cancer is the leading cause of cancer deaths. Studies show that using low-dose computed tomography scans to screen people who smoke or who used to smoke and are at elevated risk for lung cancer reduces lung cancer deaths. This study of 1565 individuals with lung cancer in South Dakota compared screening eligibility using US Preventive Services Task Force (USPSTF) criteria and a lung cancer risk-prediction model (PLCOm2012; from the 2012 Prostate, Lung, Colorectal, and Ovarian [PLCO] Cancer Screening Trial). The model had higher sensitivity and picked more people with lung cancer to screen compared with USPSTF criteria. Eligibility sensitivities were similar for individuals who self-reported as Indigenous versus those who did not between USPSTF criteria and the model.

Invitation methods for Indigenous New Zealand Maori in lung cancer screening: Protocol for a pragmatic cluster randomized controlled trial.

Lung cancer screening can significantly reduce mortality from lung cancer. Further evidence about how to optimize lung cancer screening for specific populations, including Aotearoa New Zealand (NZ)'s Indigenous Maori (who experience disproportionately higher rates of lung cancer), is needed to ensure it is equitable. This community-based, pragmatic cluster randomized trial aims to determine whether a lung cancer screening invitation from a patient's primary care physician, compared to from a centralized screening service, will optimize screening uptake for Maori. Participating primary care practices (clinics) in Auckland, Aotearoa NZ will be randomized to either the primary care-led or centralized service for delivery of the screening invitation. Clinic patients who meet the following criteria will be eligible: Maori; aged 55-74 years; enrolled in participating clinics in the region; ever-smokers; and have at least a 2% risk of developing lung cancer within six years (determined using the PLCOM2012 risk prediction model). Eligible patients who respond positively to the invitation will undertake shared decision-making with a nurse about undergoing a low dose CT scan (LDCT) and an assessment for Chronic Obstructive Pulmonary Disease (COPD). The primary outcomes are: 1) the proportion of eligible population who complete a risk assessment and 2) the proportion of people eligible for a CT scan who complete the CT scan. Secondary outcomes include evaluating the contextual factors needed to inform the screening process, such as including assessment for Chronic Obstructive Pulmonary Disease (COPD). We will also use the RE-AIM framework to evaluate specific implementation factors. This study is a world-first, Indigenous-led lung cancer screening trial for Maori participants. The study will provide policy-relevant information on a key policy parameter, invitation method. In addition, the trial includes a nested analysis of COPD in the screened Indigenous population, and it provides baseline (T0 screen round) data using RE-AIM implementation outcomes.

The tobacco quitline setting as a teachable moment: The Educating Quitline Users About Lung (EQUAL) cancer screening randomized trial.

Although lung cancer screening (LCS) using low-dose CT is recommended for high-risk individuals, screening adherence remains low. We conducted a randomized trial to compare two methods of providing LCS education to Maryland Tobacco Quitline (MTQ) callers in order to assess whether this setting may serve as a teachable moment for LCS-eligible individuals. MTQ callers (50-80 years, 20+ pack-years, prior LCS ≥12 months) completed the baseline and were randomized to the Print- or Web-based version of ShouldIScreen.com. Participants completed 1- and 4-month follow-up assessments to evaluate intervention engagement and LCS-related outcomes. Participants (Print = 152, Web = 146) were 61.7 (SD = 6.3) years old and reported 63.5 pack-years (SD = 36.0). Most identified as Black (54.2%), female (66.1%), having internet access (78.9%), completing other recommended cancer screenings (86.3%), and that they would undergo LCS if recommended by their provider (91.3%). By 4 months, significantly more Print (75.0%) than Web (61.6%) participants had read the materials (P = .01). Most reported the interventions contained "the right amount" of information (92.6%) and prepared them to talk with their doctor (57.2%). Regarding screening-related outcomes, 42.8% (Print) and 43.8% (Web) had scheduled or completed a low-dose CT scan or a shared decision-making visit (P = .86). In a racially diverse sample of LCS-eligible quitline callers, offering LCS educational materials resulted in high intervention engagement and screening-related appointments. As >20% did not have internet access, providing participants' preferred modality (web/print) may improve intervention engagement and knowledge. Improving LCS awareness represents an important opportunity to increase screening among eligible but unscreened quitline callers.

Although annual lung cancer screening (LCS) using low-dose CT is recommended for high-risk individuals, screening adherence remains low. In partnership with the Maryland Tobacco Quitline (MTQ), we compared Print (N = 152) versus Web (N = 146) methods for educating quitline callers about LCS. MTQ callers (50­80 years, 20+ pack-years) completed the baseline and the 1- and 4-month follow-up assessments to evaluate intervention engagement and LCS-related outcomes. Over half of participants identified as Black (54.4%), female (66.2%), and reported having internet access (78.9%), completing other recommended cancer screenings (86%), and would undergo LCS if recommended by their provider (91%). Significantly more Print (75.0%) than Web (61.9%) participants read the materials. Half of participants reported the interventions prepared them to talk with their doctor (57.4%). Regarding screening-related outcomes, 42.8% (Print) and 43.8% (Web) had scheduled or completed a CT scan or a shared decision-making visit. In a racially diverse sample of LCS-eligible quitline callers, offering LCS educational materials resulted in high intervention engagement and screening-related appointments. As >20% did not have internet access, offering the preferred intervention modality may result in improved intervention engagement and knowledge. Effectively improving awareness represents an opportunity to increase screening among LCS-eligible quitline callers.

Integrating Smoking Cessation Into Low-Dose Computed Tomography Lung Cancer Screening: Results of the Ontario, Canada Pilot.

INTRODUCTION: Low-dose computed tomography screening in high-risk individuals reduces lung cancer mortality. To inform the implementation of a provincial lung cancer screening program, Ontario Health undertook a Pilot study, which integrated smoking cessation (SC). METHODS: The impact of integrating SC into the Pilot was assessed by the following: rate of acceptance of a SC referral; proportion of individuals who were currently smoking cigarettes and attended a SC session; the quit rate at 1 year; change in the number of quit attempts; change in Heaviness of Smoking Index; and relapse rate in those who previously smoked. RESULTS: A total of 7768 individuals were recruited predominantly through primary care physician referral. Of these, 4463 were currently smoking and were risk assessed and referred to SC services, irrespective of screening eligibility: 3114 (69.8%) accepted referral to an in-hospital SC program, 431 (9.7%) to telephone quit lines, and 50 (1.1%) to other programs. In addition, 4.4% reported no intention to quit and 8.5% were not interested in participating in a SC program. Of the 3063 screen-eligible individuals who were smoking at baseline low-dose computed tomography scan, 2736 (89.3%) attended in-hospital SC counseling. The quit rate at 1 year was 15.5% (95% confidence interval: 13.4%-17.7%; range: 10.5%-20.0%). Improvements were also observed in Heaviness of Smoking Index (p < 0.0001), number of cigarettes smoked per day (p < 0.0001), time to first cigarette (p < 0.0001), and number of quit attempts (p < 0.001). Of those who reported having quit within the previous 6 months, 6.3% had resumed smoking at 1 year. Furthermore, 92.7% of the respondents reported satisfaction with the hospital-based SC program. CONCLUSIONS: On the basis of these observations, the Ontario Lung Screening Program continues to recruit through primary care providers, to assess risk for eligibility using trained navigators, and to use an opt-out approach to referral for cessation services. In addition, initial in-hospital SC support and intensive follow-on cessation interventions will be provided to the extent possible.

Air Pollution and Lung Cancer: A Review by International Association for the Study of Lung Cancer Early Detection and Screening Committee.

INTRODUCTION: The second leading cause of lung cancer is air pollution. Air pollution and smoking are synergistic. Air pollution can worsen lung cancer survival. METHODS: The Early Detection and Screening Committee of the International Association for the Study of Lung Cancer formed a working group to better understand issues in air pollution and lung cancer. These included identification of air pollutants, their measurement, and proposed mechanisms of carcinogenesis. The burden of disease and the underlying epidemiologic evidence linking air pollution to lung cancer in individuals who never and ever smoked were summarized to quantify the problem, assess risk prediction models, and develop recommended actions. RESULTS: The number of estimated attributable lung cancer deaths has increased by nearly 30% since 2007 as smoking has decreased and air pollution has increased. In 2013, the International Agency for Research on Cancer classified outdoor air pollution and particulate matter with aerodynamic diameter less than 2.5 microns in outdoor air pollution as carcinogenic to humans (International Agency for Research on Cancer group 1) and as a cause of lung cancer. Lung cancer risk models reviewed do not include air pollution. Estimation of cumulative exposure to air pollution exposure is complex which poses major challenges with accurately collecting long-term exposure to ambient air pollution for incorporation into risk prediction models in clinical practice. CONCLUSIONS: Worldwide air pollution levels vary widely, and the exposed populations also differ. Advocacy to lower sources of exposure is important. Health care can lower its environmental footprint, becoming more sustainable and resilient. The International Association for the Study of Lung Cancer community can engage broadly on this topic.

Fifty-year forecasts of daily smoking prevalence: can Australia reach 5% by 2030?

OBJECTIVE: To compare 50-year forecasts of Australian tobacco smoking rates in relation to trends in smoking initiation and cessation and in relation to a national target of ≤5% adult daily prevalence by 2030. METHODS: A compartmental model of Australian population daily smoking, calibrated to the observed smoking status of 229 523 participants aged 20-99 years in 26 surveys (1962-2016) by age, sex and birth year (1910-1996), estimated smoking prevalence to 2066 using Australian Bureau of Statistics 50-year population predictions. Prevalence forecasts were compared across scenarios in which smoking initiation and cessation trends from 2017 were continued, kept constant or reversed. RESULTS: At the end of the observation period in 2016, model-estimated daily smoking prevalence was 13.7% (90% equal-tailed interval (EI) 13.4%-14.0%). When smoking initiation and cessation rates were held constant, daily smoking prevalence reached 5.2% (90% EI 4.9%-5.5%) after 50 years, in 2066. When initiation and cessation rates continued their trajectory downwards and upwards, respectively, daily smoking prevalence reached 5% by 2039 (90% EI 2037-2041). The greatest progress towards the 5% goal came from eliminating initiation among younger cohorts, with the target met by 2037 (90% EI 2036-2038) in the most optimistic scenario. Conversely, if initiation and cessation rates reversed to 2007 levels, estimated prevalence was 9.1% (90% EI 8.8%-9.4%) in 2066. CONCLUSION: A 5% adult daily smoking prevalence target cannot be achieved by the year 2030 based on current trends. Urgent investment in concerted strategies that prevent smoking initiation and facilitate cessation is necessary to achieve 5% prevalence by 2030.

Evaluation of the accuracy of the PLCOm2012 6-year lung cancer risk prediction model among smokers in the CARTaGENE population-based cohort.

BACKGROUND: The PLCOm2012 prediction tool for risk of lung cancer has been proposed for a pilot program for lung cancer screening in Quebec, but has not been validated in this population. We sought to validate PLCOm2012 in a cohort of Quebec residents, and to determine the hypothetical performance of different screening strategies. METHODS: We included smokers without a history of lung cancer from the population-based CARTaGENE cohort. To assess PLCOm2012 calibration and discrimination, we determined the ratio of expected to observed number of cases, as well as the sensitivity, specificity and positive predictive values of different risk thresholds. To assess the performance of screening strategies if applied between Jan. 1, 1998, and Dec. 31, 2015, we tested different thresholds of the PLCOm2012 detection of lung cancer over 6 years (1.51%, 1.70% and 2.00%), the criteria of Quebec's pilot program (for people aged 55-74 yr and 50-74 yr) and recommendations from 2021 United States and 2016 Canada guidelines. We assessed shift and serial scenarios of screening, whereby eligibility was assessed annually or every 6 years, respectively. RESULTS: Among 11 652 participants, 176 (1.51%) lung cancers were diagnosed in 6 years. The PLCOm2012 tool underestimated the number of cases (expected-to-observed ratio 0.68, 95% confidence interval [CI] 0.59-0.79), but the discrimination was good (C-statistic 0.727, 95% CI 0.679-0.770). From a threshold of 1.51% to 2.00%, sensitivities ranged from 52.3% (95% CI 44.6%-59.8%) to 44.9% (95% CI 37.4%-52.6%), specificities ranged from 81.6% (95% CI 80.8%-82.3%) to 87.7% (95% CI 87.0%-88.3%) and positive predictive values ranged from 4.2% (95% CI 3.4%-5.1%) to 5.3% (95% CI 4.2%-6.5%). Overall, 8938 participants had sufficient data to test performance of screening strategies. If eligibility was estimated annually, Quebec pilot criteria would have detected fewer cancers than PLCOm2012 at a 2.00% threshold (48.3% v. 50.2%) for a similar number of scans per detected cancer. If eligibility was estimated every 6 years, up to 26 fewer lung cancers would have been detected; however, this scenario led to higher positive predictive values (highest for PLCOm2012 with a 2.00% threshold at 6.0%, 95% CI 4.8%-7.3%). INTERPRETATION: In a cohort of Quebec smokers, the PLCOm2012 risk prediction tool had good discrimination in detecting lung cancer, but it may be helpful to adjust the intercept to improve calibration. The implementation of risk prediction models in some of the provinces of Canada should be done with caution.

Eligibility to lung cancer screening among staffs of a university hospital: A large cross-sectional survey.

INTRODUCTION: Implementation of Lung cancer screening (LCS) programs is challenging. The ILYAD study objectives is to evaluate communication methods to improve participation rate among the Lyon University Hospital employees. In this first part of the study, we aimed to determinate the number of eligible individuals among our population of employees. METHOD: In November 2020, we conducted a questionnaire based cross sectional survey among the Lyon University Hospital employees (N = 26,954). We evaluated the PLCO m2012 risk prediction model and the eligibility criteria recommended by French guidelines. We assessed the proportion of eligible individuals among the responders and calculated the total eligible individuals in our hospital. RESULTS: Overall, 4,526 questionnaires were available for analysis. 16.0% were current smokers, and 28.2% were former smokers. Among the 50-75yo ever-smoker employees, 27% were eligible according to the French guidelines, 2.7% of all eversmokers according to a PLCO m2012 score ≥ 1.51%, and thus, 3.8% of the surveyed population were eligible to the combined criteria. The factors associated with higher eligibility among 50-75yo ever-smokers were educational level, feeling symptoms related to tobacco smoking, personal history of COPD and family history of lung cancer. Using the French guidelines criteria only, we estimated the total number of eligible individuals in the hospital at 838. CONCLUSION: In this study, we determined a theoretical number of eligible employees to LCS in our institution and the factors associated to eligibility. Secondly, we will propose LCS to all eligible employees of Lyon University Hospital with incremented information actions.

Risk Model-Based Lung Cancer Screening : A Cost-Effectiveness Analysis.

BACKGROUND: In their 2021 lung cancer screening recommendation update, the U.S. Preventive Services Task Force (USPSTF) evaluated strategies that select people based on their personal lung cancer risk (risk model-based strategies), highlighting the need for further research on the benefits and harms of risk model-based screening. OBJECTIVE: To evaluate and compare the cost-effectiveness of risk model-based lung cancer screening strategies versus the USPSTF recommendation and to explore optimal risk thresholds. DESIGN: Comparative modeling analysis. DATA SOURCES: National Lung Screening Trial; Surveillance, Epidemiology, and End Results program; U.S. Smoking History Generator. TARGET POPULATION: 1960 U.S. birth cohort. TIME HORIZON: 45 years. PERSPECTIVE: U.S. health care sector. INTERVENTION: Annual low-dose computed tomography in risk model-based strategies that start screening at age 50 or 55 years, stop screening at age 80 years, with 6-year risk thresholds between 0.5% and 2.2% using the PLCOm2012 model. OUTCOME MEASURES: Incremental cost-effectiveness ratio (ICER) and cost-effectiveness efficiency frontier connecting strategies with the highest health benefit at a given cost. RESULTS OF BASE-CASE ANALYSIS: Risk model-based screening strategies were more cost-effective than the USPSTF recommendation and exclusively comprised the cost-effectiveness efficiency frontier. Among the strategies on the efficiency frontier, those with a 6-year risk threshold of 1.2% or greater were cost-effective with an ICER less than $100 000 per quality-adjusted life-year (QALY). Specifically, the strategy with a 1.2% risk threshold had an ICER of $94 659 (model range, $72 639 to $156 774), yielding more QALYs for less cost than the USPSTF recommendation, while having a similar level of screening coverage (person ever-screened 21.7% vs. USPSTF's 22.6%). RESULTS OF SENSITIVITY ANALYSES: Risk model-based strategies were robustly more cost-effective than the 2021 USPSTF recommendation under varying modeling assumptions. LIMITATION: Risk models were restricted to age, sex, and smoking-related risk predictors. CONCLUSION: Risk model-based screening is more cost-effective than the USPSTF recommendation, thus warranting further consideration. PRIMARY FUNDING SOURCE: National Cancer Institute (NCI).