Comparison of discrimination performance of 11 lung cancer risk models for predicting lung cancer in a prospective cohort of screening-age adults from Germany followed over 17 years

A systematic review and meta-analysis of lung cancer risk prediction models

BACKGROUND

Lung cancer (LC) remains the leading cause of cancer-related mortality worldwide. Early detection through targeted screening significantly improves patient outcomes. However, identifying high-risk individuals remains a critical challenge.

PURPOSE

This systematic review evaluates externally validated LC risk prediction models to assess their performance and potential applicability in screening strategies.

METHODS

Of the 11,805 initial studies, 66 met inclusion criteria and 38 published mainly between 2020 and 2024 were included in the final analysis. Model methodologies, validation approaches, and performance metrics were extracted and compared.

RESULTS

The review identified 18 models utilising conventional machine learning, six employing neural networks, and 14 comparing different predictive frameworks. The Prostate Lung Colorectal and Ovarian Cancer Screening Trial (PLCOm2012) demonstrated superior sensitivity across diverse populations, while newer models, such as Optimized Early Warning model for Lung cancer risk (OWL) and CanPredict, showed promising results. However, differences in population demographics and healthcare systems may limit the generalisability of these models.

INTERPRETATION

While LC risk prediction models have advanced, their applicability to specific healthcare systems, such as Finland's, requires further adaptation and validation. Future research should focus on optimising these models for local contexts to improve clinical impact and cost-effectiveness in targeted screening programmes.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42022321391.

The Value of Clinical Decision Support in Healthcare: A Focus on Screening and Early Detection

In a rapidly changing technology landscape, "Clinical Decision Support" (CDS) has become an important tool to improve patient management. CDS systems offer medical professionals new insights to improve diagnostic accuracy, therapy planning, and personalized treatment. In addition, CDS systems provide cost-effective options to augment conventional screening for secondary prevention. This review aims to (i) describe the purpose and mechanisms of CDS systems, (ii) discuss different entities of algorithms, (iii) highlight quality features, and (iv) discuss challenges and limitations of CDS in clinical practice. Furthermore, we (v) describe contemporary algorithms in oncology, acute care, cardiology, and nephrology. In particular, we consolidate research on algorithms across diseases that imply a significant disease and economic burden, such as lung cancer, colorectal cancer, hepatocellular cancer, coronary artery disease, traumatic brain injury, sepsis, and chronic kidney disease.

Smoking-independent DNA methylation markers for lung cancer risk: External validation in a large population-based cohort study

Smoking-associated epigenetic changes have been linked to lung cancer (LC) risk; however, the role of epigenetic alterations independent of smoking is yet to be fully understood. This study aimed to validate 16 previously reported CpG sites that are independent of smoking yet associated with LC risk within a population-based prospective cohort. Using the Infinium Methylation EPIC BeadChip kit or the Infinium HumanMethylation450K BeadChip Assay, DNA methylation (DNAm) in whole blood was assessed in four subsets (n = 736, 1027, 997, and 312) of a population-based cohort from Germany. The DNAm levels of the 16 smoking-independent CpG sites were analyzed. Hazard ratios (HRs) and their 95% confidence intervals (95% CIs) were calculated to assess associations of DNAm at the 16 CpG sites with LC risk, adjusting for multiple covariates, including smoking habits and a smoking-associated DNAm score. Over 17 years of follow-up, a total of 199 LCs were observed. Among the 16 CpGs, cg02211449 showed a negative association with LC risk (HR [95% CI] per SD increase, = 0.70 [0.63-0.78]), while cg11385536 (1.04 [1.01-1.07]), cg09736286 (1.64 [1.10-2.44]), cg19907023 (1.64 [1.01-2.66]), and cg22032485 (1.52 [1.04-2.21]) displayed positive associations with LC risk. Five of the 16 suggested smoking-independent CpGs could be externally validated as predictors of LC risk. Further research should address their potential contribution to enhanced LC risk stratification.

Head-to-head comparisons of risk discrimination by questionnaire-based lung cancer risk prediction models: a systematic review and meta-analysis

Background

While different lung cancer risk prediction models have been established as essential tools to identify high-risk participants for lung cancer screening programs, evaluations of their risk discriminatory performances have reported heterogenous findings in different research cohorts. We therefore aimed to summarise results of head-to-head comparisons of the predictive performance of various lung cancer risk models performed within the same study population.

Methods

In this systematic review and meta-analysis, we performed a systematic search of PubMed and Web of Science databases for primary studies published from inception to Oct 16, 2024. Articles comparing the performance of questionnaire-based lung cancer risk models in an independent, external validation cohort of participants with previous or current smoking exposure were included. The main reasons for exclusion of studies were if only one model was assessed in the external population or risk discrimination was not evaluated. Random-effects meta-analyses were conducted to synthesize differences in the area under the curve (AUC) of two models compared in multiple populations. To assess the risk of bias, PROBAST (the Prediction model Risk of Bias Assessment Tool) was used. The study was registered with PROSPERO, CRD42023427911.

Findings

The systematic search yielded 5568 records. In total, 15 eligible studies were included in the meta-analysis, comprising 4,134,648 individuals with previous or current smoking exposure, of whom 45,448 (1.10%) developed LC within 5-7 years. Among the nine models that were compared, AUC differences reached up to 0.050 between two models. The Lung Cancer Risk Assessment Tool (LCRAT), Bach model and PLCOm2012 model consistently had a higher AUC when compared to any other model, with AUC differences ranging between 0.018 (95% CI 0.011, 0.026) and 0.044 (95% CI 0.038, 0.049). The risk of bias and applicability concerns were deemed low in eight, and high in seven of the included studies. Results excluding studies with high risk of bias were mostly consistent. Among eight of the 24 model pairs that were compared, there was notable between-study heterogeneity (I2 ≥50%).

Interpretation

Our systematic review and meta-analyses of head-to-head comparisons disclose major differences in predictive performance of widely used lung cancer risk models. Although our review is limited to the availability of head-to-head comparisons, evidence from current cohort-based model comparisons indicates that the LCRAT, Bach and PLCOm2012 consistently outperformed alternative questionnaire-based risk prediction tools.

Funding

Funded by the European Union.

Overdiagnosis and overtreatment of ground-glass nodule-like lung cancer

Lung cancer has had one of the highest incidences and mortality in the world over the last few decades, which has aided in the promotion and popularization of screening for lung ground-glass nodules (GGNs). People have great psychological anxiety about GGN because of the chance that it will develop into lung cancer, which makes clinical treatment of GGN a generally excessive phenomenon. Overdiagnosis in screening has recently been mentioned in the literature. An important research emphasis of screening is how to reduce the incidence of overdiagnosis and overtreatment. This paper discusses from different aspects how to characterize the occurrence of overdiagnosis and overtreatment, how to reduce overdiagnosis and overtreatment, and future screening, follow-up, and treatment approaches.

Identifying the population to be targeted in a lung cancer screening programme in Denmark

INTRODUCTION

We assessed the impact of recruitment criteria on lung cancer detection in a future Danish screening programme with low-dose CT.

METHODS

We combined data from two Danish population-based health examination surveys with eligibility criteria from seven randomised controlled trials on lung cancer screening. Incident lung cancers were identified by linkage with the National Pathology Data Bank (Patobank). For an average of 4.4 years of follow-up, we calculated sensitivity, specificity, efficient frontier and number needed to screen (NNS) for lung cancer detection.

RESULTS

When applying the different eligibility criteria to the 48 171 persons invited to the two surveys, the number of lung cancer cases identified in the target groups varied from 46 to 68. The National Lung Screening Trial (NLST) criteria had the highest sensitivity of 62.6% (95% CI 52.7 to 71.8) and the Dutch-Belgian NEderlands-Leuvens Screening ONderzoek (NELSON) criteria had the highest specificity 81.6% (95% CI 81.0 to 82.1). Sensitivity was higher for men than for women (NLST criteria 71.7% (95% CI 57.7 to 83.2) and 53.7% (95% CI 39.6 to 67.4), respectively). The NLST criteria identified the target population obtaining the lowest NNS with 46.3. The application of the NLST criteria showed that the higher the sensitivity, the lower the number of false-negative cases and, thus, the lower the NNS.

CONCLUSIONS

This study highlights the impact of the definition of the at-risk population on lung cancer screening efficacy. We found lower sensitivity among women regardless of screening criteria used. This should be carefully addressed in a possible screening programme.

Clinical Adjuncts to Lung Cancer Screening: A Narrative Review

The updated US Preventive Services Task Force guidelines on lung cancer screening have significantly expanded the population of screening eligible adults, among whom the balance of benefits and harms associated with lung cancer screening vary considerably. Clinical adjuncts are additional information and tools that can guide decision-making to optimally screen individuals who are most likely to benefit. Proposed adjuncts include integration of clinical history, risk prediction models, shared-decision-making tools, and biomarker tests at key steps in the screening process. Although evidence regarding their clinical utility and implementation is still evolving, they carry significant promise in optimizing screening effectiveness and efficiency for lung cancer.