Rates of Downstream Procedures and Complications Associated With Lung Cancer Screening in Routine Clinical Practice : A Retrospective Cohort Study

Real-world first round results from a charity lung cancer screening program in East Asia

Background

Screening with low-dose computed tomography (LDCT) has been proven to potentially reduce the rate of mortality of lung cancer. Lack of real-world data outside of protocolized trials has been cited as an impediment to its more widespread implementation, especially in Asia. This report aims to provide such real-world data.

Methods

A single round of LDCT was provided through a community-based charity program in Hong Kong, China to asymptomatic adults with a family history of lung cancer and/or smoking history. Anonymized data from this program were analyzed.

Results

LDCT was performed for 99 participants, including 98 (99%) who had one or more family members with history of lung cancer, and 70 (71%) who were never-smokers. After a single round of screening, a positive LDCT was noted in 47 participants (47%). A sister with a history of lung cancer (28% vs. 8%, P=0.01) and a multiplex family (MF) (47% vs. 23%, P=0.02) were factors associated with a positive LDCT. After a median period of 10 months (range, 5-16 months) following LDCT, lung cancer (all adenocarcinoma) was diagnosed as a direct consequence of positive LDCT findings in six participants (6%), of whom four had stage I disease and five received surgery with curative intent. In the 47 participants with a positive LDCT, having a sister with a history of lung cancer was associated with an increased risk of lung cancer (relative risk =5.23; 95% confidence interval: 1.09-25.21). Detected lesions categorized as Lung Imaging Reporting and Data System (Lung-RADS) 3 or above (odds ratio =12.08; 95% confidence interval: 1.27-114.64) or deemed by an experienced specialist to be suspicious (odds ratio =63.33; 95% confidence interval: 5.48-732.29) were significantly more likely to turn out to be a lung cancer.

Conclusions

This real-world data demonstrates that a single round of LDCT screening at a community level in East Asia can detect potentially curable lung cancer at a rate comparable to those reported by protocolized trials. When considering future LDCT screening programs in East Asia, a family history of lung cancer may be a key factor indicating a person for screening, and how features of a LDCT-detected lesion should trigger further intervention warrant further definition.

Clinical utility of an artificial intelligence radiomics-based tool for risk stratification of pulmonary nodules

BACKGROUND

Clinical utility data on pulmonary nodule (PN) risk stratification biomarkers are lacking. We aimed to determine the incremental predictive value and clinical utility of using an artificial intelligence (AI) radiomics-based computer-aided diagnosis (CAD) tool in addition to routine clinical information to risk stratify PNs among real-world patients.

METHODS

We performed a retrospective cohort study of patients with PNs who underwent lung biopsy. We collected clinical data and used a commercially available AI radiomics-based CAD tool to calculate a Lung Cancer Prediction (LCP) score. We developed logistic regression models to evaluate a well-validated clinical risk prediction model (the Mayo Clinic model) with and without the LCP score (Mayo vs Mayo + LCP) using area under the curve (AUC), risk stratification table, and standardized net benefit analyses.

RESULTS

Among the 134 patients undergoing PN biopsy, cancer prevalence was 61%. Addition of the radiomics-based LCP score to the Mayo model was associated with increased predictive accuracy (likelihood ratio test, P = .012). The AUCs for the Mayo and Mayo + LCP models were 0.58 (95% CI = 0.48 to 0.69) and 0.65 (95% CI = 0.56 to 0.75), respectively. At the 65% risk threshold, the Mayo + LCP model was associated with increased sensitivity (56% vs 38%; P = .019), similar false positive rate (33% vs 35%; P = .8), and increased standardized net benefit (18% vs -3.3%) compared with the Mayo model.

CONCLUSIONS

Use of a commercially available AI radiomics-based CAD tool as a supplement to clinical information improved PN cancer risk prediction and may result in clinically meaningful changes in risk stratification.

Innovations in Early Lung Cancer Detection: Tracing the Evolution and Advancements in Screening

Lung cancer mortality rates, particularly non-small cell lung cancer (NSCLC), continue to present a significant global health challenge, and the adoption of lung cancer screening remains limited, often influenced by inequities in access to healthcare. Despite clinical evidence demonstrating the efficacy of annual screening with low-dose computed tomography (LDCT) and recommendations from medical organizations including the U.S. Preventive Services Task Force (USPSTF), the national lung cancer screening uptake remains around 5% among eligible individuals. Advancements in the clinical management of NSCLC have recently become more personalized with the implementation of blood-based biomarker testing. Extensive research into tumor-derived cell-free DNA (cfDNA) through fragmentation offers a novel method for improving early lung cancer detection. This review assesses the screening landscape, explores obstacles to lung cancer screening, and discusses how a plasma whole genome fragmentome test (pWGFrag-Lung) can improve lung cancer screening participation and adherence.

Estimating the Effects of Cancer Screening in Clinical Practice Settings: The Role of Selective Uptake and Suboptimal Adherence along the Cancer Screening Continuum

Randomized controlled trials (RCT) are the gold standard in determining efficacy of cancer screening tests. Yet, systematic differences between RCT and the general populations eligible for screening raise concerns about the generalizability and relevance of RCT findings to guide the development and dissemination of cancer screening programs. Observational studies from clinical practice settings have documented selective uptake in screening-i.e., variation across subgroups regarding who is screened and not screened-as well as suboptimal adherence to screening recommendations, including follow-up of positive findings with subsequent imaging studies and diagnostic invasive procedures. When the effectiveness of a screening intervention varies across subgroups, and there is selective uptake and suboptimal adherence to screening in clinical practice relative to that in the RCT, the effects of screening reported in RCTs are not expected to generalize to clinical practice settings. Understanding the impacts of selective uptake and suboptimal adherence on estimates of the effectiveness of cancer screening in clinical practice will generate evidence that can be used to inform future screening recommendations and enhance shared decision-making tools.

The pros and cons of lung cancer screening

Several trials have shown that low-dose computed tomography-based lung cancer screening (LCS) allows a substantial reduction in lung cancer-related mortality, carrying the potential for other clinical benefits. There are, however, some uncertainties to be clarified and several aspects to be implemented to optimize advantages and minimize the potential harms of LCS. This review summarizes current evidence on LCS, discussing some of the well-established and potential benefits, including lung cancer (LC)-related mortality reduction and opportunity for smoking cessation interventions, as well as the disadvantages of LCS, such as overdiagnosis and overtreatment. CLINICAL RELEVANCE STATEMENT: Different perspectives are provided on LCS based on the updated literature. KEY POINTS: Lung cancer is a leading cancer-related cause of death and screening should reduce associated mortality. This review summarizes current evidence related to LCS. Several aspects need to be implemented to optimize benefits and minimize potential drawbacks of LCS.

University of Pennsylvania Telehealth Research Center of Excellence

Drawing from insights from communication science and behavioral economics, the University of Pennsylvania Telehealth Research Center of Excellence (Penn TRACE) is designing and testing telehealth strategies with the potential to transform access to care, care quality, outcomes, health equity, and health-care efficiency across the cancer care continuum, with an emphasis on understanding mechanisms of action. Penn TRACE uses lung cancer care as an exemplar model for telehealth across the care continuum, from screening to treatment to survivorship. We bring together a diverse and interdisciplinary team of international experts and incorporate rapid-cycle approaches and mixed methods evaluation in all center projects. Our initiatives include a pragmatic sequential multiple assignment randomized trial to compare the effectiveness of telehealth strategies to increase shared decision-making for lung cancer screening and 2 pilot projects to test the effectiveness of telehealth to improve cancer care, identify multilevel mechanisms of action, and lay the foundation for future pragmatic trials. Penn TRACE aims to produce new fundamental knowledge and advance telehealth science in cancer care at Penn and nationally.

Validation of a Blood-Based Protein Biomarker Panel for a Risk Assessment of Lethal Lung Cancer in the Physicians' Health Study

This study aimed to assess a four-marker protein panel (4MP)'s performance, including the precursor form of surfactant protein B, cancer antigen 125, carcinoembryonic antigen, and cytokeratin-19, for predicting lung cancer in a cohort enriched with never- and ever-smokers. Blinded pre-diagnostic plasma samples collected within 2 years prior to a lung cancer diagnosis from 25 cases and 100 sex-, age-, and smoking-matched controls were obtained from the Physicians' Health Study (PHS). The 4MP yielded AUC performance estimates of 0.76 (95% CI: 0.61-0.92) and 0.69 (95% CI: 0.56-0.82) for predicting lung cancer within one year and within two years of diagnosis, respectively. When stratifying into ever-smokers and never-smokers, the 4MP had respective AUCs of 0.77 (95% CI: 0.63-0.92) and 0.72 (95% CI: 0.17-1.00) for a 1-year risk of lung cancer. The AUCs of the 4MP for predicting metastatic lung cancer within one year and two years of the blood draw were 0.95 (95% CI: 0.87-1.00) and 0.78 (95% CI: 0.62-0.94), respectively. Our findings indicate that a blood-based biomarker panel may be useful in identifying ever- and never-smokers at high risk of a diagnosis of lung cancer within one-to-two years.

Oncology: What You May Have Missed in 2023

Advances in oncology treatment methods have improved outcomes and quality of life for patients with cancer. However, care of these patients can be complex, and the contribution of physicians from different specialties is crucial. This article highlights important publications from 2023 on topics across a wide spectrum relating to the management of oncology patients. The literature was screened for significant new evidence that is relevant to internal medicine specialists and subspecialists whose focus is not oncology. Two articles address the importance of social interventions targeting end-of-life care for low-income and minority patients and the well-being of caregivers. Two additional articles address screening considerations in patients at risk for colorectal and lung cancer. Two more articles address safe use of hormone-related therapies to treat symptoms of menopause and prevent disease recurrence or progression in patients diagnosed with noninvasive breast neoplasia. Finally, several articles were included on topics related to COVID-19 vaccination in patients with cancer, use of cannabinoids for cancer pain control, chronic autoimmune adverse effects related to use of immune checkpoint inhibitors, and the incidence of second primary neoplasms.

Radiomics and artificial intelligence for risk stratification of pulmonary nodules: Ready for primetime?

Pulmonary nodules are ubiquitously found on computed tomography (CT) imaging either incidentally or via lung cancer screening and require careful diagnostic evaluation and management to both diagnose malignancy when present and avoid unnecessary biopsy of benign lesions. To engage in this complex decision-making, clinicians must first risk stratify pulmonary nodules to determine what the best course of action should be. Recent developments in imaging technology, computer processing power, and artificial intelligence algorithms have yielded radiomics-based computer-aided diagnosis tools that use CT imaging data including features invisible to the naked human eye to predict pulmonary nodule malignancy risk and are designed to be used as a supplement to routine clinical risk assessment. These tools vary widely in their algorithm construction, internal and external validation populations, intended-use populations, and commercial availability. While several clinical validation studies have been published, robust clinical utility and clinical effectiveness data are not yet currently available. However, there is reason for optimism as ongoing and future studies aim to target this knowledge gap, in the hopes of improving the diagnostic process for patients with pulmonary nodules.