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Shared decision making (SDM) between health care professionals and patients is essential to help patients make well informed choices about lung cancer screening (LCS). Patients who participate in SDM have greater LCS knowledge, reduced decisional conflict, and improved adherence to annual screening compared with patients who do not participate in SDM. SDM tools are acceptable to patients and clinicians. The importance of SDM in LCS is emphasized in recommendations from professional organizations and highlighted as a priority in the 2022 President's Cancer Panel Report. The updated 2022 national coverage determination from the Centers for Medicare & Medicaid Services reaffirms the value of SDM in offering LCS to eligible beneficiaries. The Shared Decision-Making Task Group of the American Cancer Society National Lung Cancer Roundtable undertook a group consensus process to identify priorities for research and implementation related to SDM for LCS and then evaluated current knowledge in these areas. Priority areas included: (1) developing feasible, adaptable SDM training programs for health care professionals; (2) understanding the impact of alternative health system LCS models on SDM practice and outcomes; (3) developing and evaluating new patient decision aids for use with diverse populations and in varied settings; (4) offering conceptual clarity about what constitutes a high-quality decision and developing appropriate quality measures; and (5) studying the use of prediction-augmented screening to support SDM in practice. Gaps in current research in all areas were observed. The authors conclude with a research and implementation agenda to advance the quality and implementation of SDM for persons who might benefit from LCS., (© 2024 American Cancer Society. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

Multicancer detection (MCD) tests use a single, easily obtainable biospecimen, such as blood, to screen for more than one cancer concurrently. MCD tests can potentially be used to improve early cancer detection, including cancers that currently lack effective screening methods. However, these tests have unknown and unquantified benefits and harms. MCD tests differ from conventional cancer screening tests in that the organ responsible for a positive test is unknown, and a broad diagnostic workup may be necessary to confirm the location and type of underlying cancer. Among two prospective studies involving greater than 16,000 individuals, MCD tests identified those who had some cancers without currently recommended screening tests, including pancreas, ovary, liver, uterus, small intestine, oropharyngeal, bone, thyroid, and hematologic malignancies, at early stages. Reported MCD test sensitivities range from 27% to 95% but differ by organ and are lower for early stage cancers, for which treatment toxicity would be lowest and the potential for cure might be highest. False reassurance from a negative MCD result may reduce screening adherence, risking a loss in proven public health benefits from standard-of-care screening. Prospective clinical trials are needed to address uncertainties about MCD accuracy to detect different cancers in asymptomatic individuals, whether these tests can detect cancer sufficiently early for effective treatment and mortality reduction, the degree to which these tests may contribute to cancer overdiagnosis and overtreatment, whether MCD tests work equally well across all populations, and the appropriate diagnostic evaluation and follow-up for patients with a positive test., (Published 2024. This article is a U.S. Government work and is in the public domain in the USA. CA: A Cancer Journal for Clinicians published by Wiley Periodicals LLC on behalf of American Cancer Society.)

Background: This report quantifies counteracting effects of quit-years and concomitant aging on lung cancer risk, especially on exceeding 15 quit-years, when the US Preventive Services Task Force (USPSTF) recommends curtailing lung-cancer screening., Methods: Cox models were fitted to estimate absolute lung cancer risk among Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO) and National Lung Screening Trial (NLST) participants who ever smoked. Absolute lung cancer risk and gainable years of life from screening for individuals aged 50 to 80 in the US-representative National Health Interview Survey (NHIS) 2015-2018 who ever smoked were projected. Relaxing USPSTF recommendations to 20/25/30 quit-years versus augmenting USPSTF criteria with individuals whose estimated gain in life expectancy from screening exceeded 16.2 days according to the Life Years From Screening-CT (LYFS-CT) prediction model was compared., Results: Absolute lung cancer risk increased by 8.7%/year (95% CI, 7.7%-9.7%; p < .001) as individuals aged beyond 15 quit-years in the PLCO, with similar results in NHIS and NLST. For example, mean 5-year lung cancer risk for those aged 65 years with 15 quit-years = 1.47% (95% CI, 1.35%-1.59%) versus 1.76% (95% CI, 1.62%-1.90%) for those aged 70 years with 20 quit-years in the PLCO. Removing the quit-year criterion would make 4.9 million more people eligible and increase the proportion of preventable lung cancer deaths prevented (sensitivity) from 63.7% to 74.2%. Alternatively, augmentation using LYFS-CT would make 1.7 million more people eligible while increasing the lung cancer death sensitivity to 74.0%., Conclusions: Because of aging, absolute lung cancer risk increases beyond 15 quit-years, which does not support exemption from screening or curtailing screening once it has been initiated. Compared with relaxing the USPSTF quit-year criterion, augmentation using LYFS-CT could prevent most of the deaths at substantially superior efficiency, while also preventing deaths among individuals who currently smoke with low intensity or long duration., (Published 2023. This article is a U.S. Government work and is in the public domain in the USA. Cancer published by Wiley Periodicals LLC on behalf of American Cancer Society.)

Estimates of absolute risks and risk differences are necessary for evaluating the clinical and population impact of biomedical research findings. We have developed a linear-expit regression model (LEXPIT) to incorporate linear and nonlinear risk effects to estimate absolute risk from studies of a binary outcome. The LEXPIT is a generalization of both the binomial linear and logistic regression models. The coefficients of the LEXPIT linear terms estimate adjusted risk differences, whereas the exponentiated nonlinear terms estimate residual odds ratios. The LEXPIT could be particularly useful for epidemiological studies of risk association, where adjustment for multiple confounding variables is common. We present a constrained maximum likelihood estimation algorithm that ensures the feasibility of risk estimates of the LEXPIT model and describe procedures for defining the feasible region of the parameter space, judging convergence, and evaluating boundary cases. Simulations demonstrate that the methodology is computationally robust and yields feasible, consistent estimators. We applied the LEXPIT model to estimate the absolute 5-year risk of cervical precancer or cancer associated with different Pap and human papillomavirus test results in 167,171 women undergoing screening at Kaiser Permanente Northern California. The LEXPIT model found an increased risk due to abnormal Pap test in human papillomavirus-negative that was not detected with logistic regression. Our R package blm provides free and easy-to-use software for fitting the LEXPIT model., (Published 2012. This article is a US Government work and is in the public domain in the USA.)

We focus on the efficient usage of specimen repositories for the evaluation of new diagnostic tests and for comparing new tests with existing tests. Typically, all pre-existing diagnostic tests will already have been conducted on all specimens. However, we propose retesting only a judicious subsample of the specimens by the new diagnostic test. Subsampling minimizes study costs and specimen consumption, yet estimates of agreement or diagnostic accuracy potentially retain adequate statistical efficiency. We introduce methods to estimate agreement statistics and conduct symmetry tests when the second test is conducted on only a subsample and no gold standard exists. The methods treat the subsample as a stratified two-phase sample and use inverse-probability weighting. Strata can be any information available on all specimens and can be used to oversample the most informative specimens. The verification bias framework applies if the test conducted on only the subsample is a gold standard. We also present inverse-probability-weighting-based estimators of diagnostic accuracy that take advantage of stratification. We present three examples demonstrating that adequate statistical efficiency can be achieved under subsampling while greatly reducing the number of specimens requiring retesting. Naively using standard estimators that ignore subsampling can lead to drastically misleading estimates. Through simulation, we assess the finite-sample properties of our estimators and consider other possible sampling designs for our examples that could have further improved statistical efficiency. To help promote subsampling designs, our R package CompareTests computes all of our agreement and diagnostic accuracy statistics., (Copyright © 2011 John Wiley & Sons, Ltd.)