Integrated Biomarkers for the Management of Indeterminate Pulmonary Nodules.

Rationale: Patients with indeterminate pulmonary nodules (IPNs) at risk of cancer undergo high rates of invasive, costly, and morbid procedures. Objectives: To train and externally validate a risk prediction model that combined clinical, blood, and imaging biomarkers to improve the noninvasive management of IPNs. Methods: In this prospectively collected, retrospective blinded evaluation study, probability of cancer was calculated for 456 patient nodules using the Mayo Clinic model, and patients were categorized into low-, intermediate-, and high-risk groups. A combined biomarker model (CBM) including clinical variables, serum high sensitivity CYFRA 21-1 level, and a radiomic signature was trained in cohort 1 (n = 170) and validated in cohorts 2-4 (total n = 286). All patients were pooled to recalibrate the model for clinical implementation. The clinical utility of the CBM compared with current clinical care was evaluated in 2 cohorts. Measurements and Main Results: The CBM provided improved diagnostic accuracy over the Mayo Clinic model with an improvement in area under the curve of 0.124 (95% bootstrap confidence interval, 0.091-0.156; P < 2 × 10-16). Applying 10% and 70% risk thresholds resulted in a bias-corrected clinical reclassification index for cases and control subjects of 0.15 and 0.12, respectively. A clinical utility analysis of patient medical records estimated that a CBM-guided strategy would have reduced invasive procedures from 62.9% to 50.6% in the intermediate-risk benign population and shortened the median time to diagnosis of cancer from 60 to 21 days in intermediate-risk cancers. Conclusions: Integration of clinical, blood, and image biomarkers improves noninvasive diagnosis of patients with IPNs, potentially reducing the rate of unnecessary invasive procedures while shortening the time to diagnosis.

Using User-Centered Design to Facilitate Adherence to Annual Lung Cancer Screening: Protocol for a Mixed Methods Study for Intervention Development.

BACKGROUND: Lung cancer is the leading cause of cancer-related death in the United States, with the majority of lung cancer occurrence diagnosed after the disease has already metastasized. Lung cancer screening (LCS) with low-dose computed tomography can diagnose early-stage disease, especially when eligible individuals participate in screening on a yearly basis. Unfortunately, annual adherence has emerged as a challenge for academic and community screening programs, endangering the individual and population health benefits of LCS. Reminder messages have effectively increased adherence rates in breast, colorectal, and cervical cancer screenings but have not been tested with LCS participants who experience unique barriers to screening associated with the stigma of smoking and social determinants of health. OBJECTIVE: This research aims to use a theory-informed, multiphase, and mixed methods approach with LCS experts and participants to develop a set of clear and engaging reminder messages to support LCS annual adherence. METHODS: In aim 1, survey data informed by the Cognitive-Social Health Information Processing model will be collected to assess how LCS participants process health information aimed at health protective behavior to develop content for reminder messages and pinpoint options for message targeting and tailoring. Aim 2 focuses on identifying themes for message imagery through a modified photovoice activity that asks participants to identify 3 images that represent LCS and then participate in an interview about the selection, likes, and dislikes of each photo. A pool of candidate messages for multiple delivery platforms will be developed in aim 3, using results from aim 1 for message content and aim 2 for imagery selection. The refinement of message content and imagery combinations will be completed through iterative feedback from LCS experts and participants. RESULTS: Data collection began in July 2022 and will be completed by May 2023. The final reminder message candidates are expected to be completed by June 2023. CONCLUSIONS: This project proposes a novel approach to facilitate adherence to annual LCS through the development of reminder messages that embrace content and imagery representative of the target population directly in the design process. Developing effective strategies to increase LCS adherence is instrumental in achieving optimal LCS outcomes at individual and population health levels. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/46657.

Results of a pilot risk-based lung cancer screening study: outcomes and comparisons to a Medicare eligible cohort.

PURPOSE: Risk-based lung cancer screening holds potential to detect more cancers and avert more cancer deaths than screening based on age and smoking history alone, but has not been widely assessed or implemented in the United States. The purpose of this study was to prospectively identify patients for lung cancer screening based on lung cancer risk using the PLCOm2012 model and to compare characteristics, risk profiles, and screening outcomes to a traditionally eligible screening cohort. METHODS: Participants who had a 6 year lung cancer risk score ≥ 1.5% calculated by the PLCOm2012 model and were ineligible for screening under 2015 Medicare guidelines were recruited from a lung cancer screening clinic. After informed consent, participants completed shared decision-making counseling and underwent a low-dose CT (LDCT). Characteristics and screening outcomes of the study population were compared to the traditionally eligible Medicare cohort with Fisher's Exact, t-tests, or Brown Mood tests, as appropriate. RESULTS: From August 2016 to July 2019, the study completed 48 baseline LDCTs. 10% of LDCTs recommended further pulmonary nodule evaluation (Lung-RADs 3 or 4) with two early-stage lung cancers diagnosed in individuals that had quit smoking > 15 years prior. The study population was approximately 5 years older (p = 0.001) and had lower pack years (p = 0.002) than the Medicare cohort. CONCLUSION: Prospective application of risk-based screening identifies screening candidates who are similar to a traditionally eligible Medicare cohort and future research should focus on the impact of risk calculators on lung cancer outcomes and optimal usability in clinical environments. This study was retrospectively registered on clinicaltrials.gov (NCT03683940) on 09/25/2018.

Differences in VA and Non-VA Pulmonary Nodules: All Evaluations Are not Created Equal.

BACKGROUND: Indeterminate pulmonary nodules present a common challenge for clinicians who must recommend surveillance or intervention based on an assessed risk of malignancy. PATIENTS AND METHODS: In this cohort study, patients presenting for indeterminate pulmonary nodule evaluation were enrolled at sites participating in the Colorado SPORE in Lung Cancer. They were followed prospectively and included for analysis if they had a definitive malignant diagnosis, benign diagnosis, or radiographic resolution or stability of their nodule for > 2 years. RESULTS: Patients evaluated at the Veterans Affairs (VA) and non-VA sites were equally as likely to have a malignant diagnosis (48%). The VA cohort represented a higher-risk group than the non-VA cohort regarding smoking history and chronic obstructive pulmonary disease (COPD). There were more squamous cell carcinoma diagnoses among VA malignant nodules (25% vs. 10%) and a later stage at diagnosis among VA patients. Discrimination and calibration of risk calculators produced estimates that were wide-ranging and different when comparing between risk score calculators as well as between VA/non-VA cohorts. Application of current American College of Chest Physicians guidelines to our groups could have resulted in inappropriate resection of 12% of benign nodules. CONCLUSION: Comparison of VA with non-VA patients shows important differences in underlying risk, histology of malignant nodules, and stage at diagnosis. This study highlights the challenge in applying risk calculators to a clinical setting, as the model discrimination and calibration were variable between calculators and between our higher-risk VA and lower-risk non-VA groups. MICROABSTRACT: Risk stratification and management of indeterminate pulmonary nodules (IPNs) is a common clinical problem. In this prospective cohort study of 282 patients with IPNs from Veterans Affairs (VA) and non-VA sites, we found differences in patient and nodule characteristics, histology and diagnostic stage, and risk calculator performance. Our findings highlight challenges and shortcomings of current IPN management guidelines and tools.

Determinants Associated With Longitudinal Adherence to Annual Lung Cancer Screening: A Retrospective Analysis of Claims Data.

OBJECTIVE: Lung cancer screening (LCS) efficacy is highly dependent on adherence to annual screening, but little is known about real-world adherence determinants. We used insurance claims data to examine associations between LCS annual adherence and demographic, comorbidity, health care usage, and geographic factors. MATERIALS AND METHODS: Insurance claims data for all individuals with an LCS low-dose CT scan were obtained from the Colorado All Payer Claims Dataset. Adherence was defined as a second claim for a screening CT 10 to 18 months after the index claim. Cox proportional hazards regression was used to define the relationship between annual adherence and age, gender, insurance type, residence location, outpatient health care usage, and comorbidity burden. RESULTS: After exclusions, the final data set consisted of 9,056 records with 3,072 adherent, 3,570 nonadherent, and 2,414 censored (unclassifiable) individuals. Less adherence was associated with ages 55 to 59 (hazard ratio [HR] = 0.80, 99% confidence interval [CI] = 0.67-0.94), 60 to 64 (HR = 0.83, 99% CI = 0.71-0.97), and 75 to 79 (HR = 0.79, 99% CI = 0.65-0.97); rural residence (HR = 0.56, 99% CI = 0.43-0.73); Medicare fee-for-service (HR = 0.45, 99% CI = 0.39-0.51), and Medicaid (HR = 0.50, 99% CI = 0.40-0.62). A significant interaction between outpatient health care usage and comorbidity was also observed. Increased outpatient usage was associated with increased adherence and was most pronounced for individuals without comorbidities. CONCLUSIONS: This population-based description of LCS adherence determinants provides insight into populations that might benefit from specific interventions targeted toward improving adherence and maximizing LCS benefit. Quantifying population-based adherence rates and understanding factors associated with annual adherence are critical to improving screening adherence and reducing lung cancer death.

Impact of a Hybrid Lung Cancer Screening Model on Patient Outcomes and Provider Behavior.

BACKGROUND: Lung cancer screening (LCS) implementation is complicated by the Centers for Medicare and Medicaid Services reimbursement requirements of shared decision-making and tobacco cessation counseling. LCS programs can utilize different structures to meet these requirements, but the impact of programmatic structure on provider behavior and screening outcomes is poorly described. PATIENTS AND METHODS: In a retrospective chart review of 624 patients in a hybrid structure, academic LCS program, we compared characteristics and outcomes of primary care provider (PCP)- and specialist-screened patients. We also assessed the impact of the availability of an LCS specialty clinic and best practice advisory (BPA) on PCP ordering patterns using electronic medical record generated reports. RESULTS: During the study period of July 1, 2014 through June 30, 2018, 48% of patients were specialist-screened and 52% were PCP-screened; there were no clinically relevant differences in patient characteristics or screening outcomes between these populations. PCPs demonstrate distinct practice patterns when offered the choice of specialist-driven or PCP-driven screening. Increased exposure to a LCS BPA is associated with increased PCP screening orders. The addition of a nurse navigator into the LCS program increased documentation of shared decision-making and tobacco cessation counseling to > 95% and virtually eliminated screening of ineligible patients. CONCLUSIONS: Systematic interventions including a BPA and nurse navigator are associated with increased screening and improved program quality, as evidenced by reduced screening of ineligible patients, increased lung cancer risk of the screened population, and improved compliance with LCS guidelines. Individual PCPs demonstrate clear preferences regarding LCS that should be considered in program design.

Quantifying the incremental value of deep learning: Application to lung nodule detection.

We present a case study for implementing a machine learning algorithm with an incremental value framework in the domain of lung cancer research. Machine learning methods have often been shown to be competitive with prediction models in some domains; however, implementation of these methods is in early development. Often these methods are only directly compared to existing methods; here we present a framework for assessing the value of a machine learning model by assessing the incremental value. We developed a machine learning model to identify and classify lung nodules and assessed the incremental value added to existing risk prediction models. Multiple external datasets were used for validation. We found that our image model, trained on a dataset from The Cancer Imaging Archive (TCIA), improves upon existing models that are restricted to patient characteristics, but it was inconclusive about whether it improves on models that consider nodule features. Another interesting finding is the variable performance on different datasets, suggesting population generalization with machine learning models may be more challenging than is often considered.