A multiscale 3D network for lung nodule detection using flexible nodule modeling.

BACKGROUND: Lung cancer is the most common type of cancer. Detection of lung cancer at an early stage can reduce mortality rates. Pulmonary nodules may represent early cancer and can be identified through computed tomography (CT) scans. Malignant risk can be estimated based on attributes like size, shape, location, and density. PURPOSE: Deep learning algorithms have achieved remarkable advancements in this domain compared to traditional machine learning methods. Nevertheless, many existing anchor-based deep learning algorithms exhibit sensitivity to predefined anchor-box configurations, necessitating manual adjustments to obtain optimal outcomes. Conversely, current anchor-free deep learning-based nodule detection methods normally adopt fixed-size nodule models like cubes or spheres. METHODS: To address these technical challenges, we propose a multiscale 3D anchor-free deep learning network (M3N) for pulmonary nodule detection, leveraging adjustable nodule modeling (ANM). Within this framework, ANM empowers the representation of target objects in an anisotropic manner, with a novel point selection strategy (PSS) devised to accelerate the learning process of anisotropic representation. We further incorporate a composite loss function that combines the conventional L2 loss and cosine similarity loss, facilitating M3N to learn nodules' intensity distribution in three dimensions. RESULTS: Experiment results show that the M3N achieves 90.6% competitive performance metrics (CPM) with seven predefined false positives per scan on the LUNA 16 dataset. This performance appears to exceed that of other state-of-the-art deep learning-based networks reported in their respective publications. Individual test results also demonstrate that M3N excels in providing more accurate, adaptive bounding boxes surrounding the contours of target nodules. CONCLUSIONS: The newly developed nodule detection system reduces reliance on prior knowledge, such as the general size of objects in the dataset, thus it should enhance overall robustness and versatility. Distinct from traditional nodule modeling techniques, the ANM approach aligns more closely with the morphological characteristics of nodules. Time consumption and detection results demonstrate promising efficiency and accuracy which should be validated in clinical settings.

The International Association for the Study of Lung Cancer Lung Cancer Staging Project: Proposals for Revision of the TNM Stage Groups in the Forthcoming (Ninth) Edition of the TNM Classification for Lung Cancer.

INTRODUCTION: The TNM classification of lung cancer is periodically revised. The International Association for the Study of Lung Cancer collected and analyzed a new database to inform the forthcoming ninth edition of the TNM classification. The results are herewith presented. METHODS: After exclusions, 76,518 patients from a total of 124,581 registered patients were available for analyses: 58,193 with clinical stage, 39,192 with pathologic stage, and 62,611 with best stage NSCLC. The proposed new N2 subcategories (N2a, involvement of single ipsilateral mediastinal or subcarinal nodal station, and N2b, involvement of multiple ipsilateral mediastinal nodal stations with or without involvement of the subcarinal nodal station) and the new M1c subcategories (M1c1, multiple extrathoracic metastases in one organ system, and M1c2, multiple extrathoracic metastases in multiple organ systems) were considered in the survival analyses. Several potential stage groupings were evaluated, using multiple analyses, including recursive partitioning, assessment of homogeneity within and discrimination between potential groups, clinical and statistical significance of survival differences, multivariable regression, and broad assessment of generalizability. RESULTS: T1N1, T1N2a, and T3N2a subgroups are assigned to IIA, IIB, and IIIA stage groups, respectively. T2aN2b and T2bN2b subgroups are assigned to IIIB. M1c1 and M1c2 remain in stage group IVB. Analyses reveal consistent ordering, discrimination of prognosis, and broad generalizability of the proposed ninth edition stage classification of lung cancer. CONCLUSIONS: The proposed stages for the ninth edition TNM improve the granularity of nomenclature about anatomic extent that has benefits as treatment approaches become increasingly differentiated and complex.

A Prospective Observational Study of Physical Activity Levels and Physical Fitness of People at High Risk for Lung Cancer.

Introduction: Physical activity (PA) is a potentially modifiable risk factor for lung cancer, with previous research revealing that people who engage in more PA have lower risk of developing lung cancer. PA levels of lung cancer screening participants have not previously been explored. Methods: Participants at a single Australian International Lung Screen Trial site were eligible for assessment of self-reported PA levels (International Physical Activity Questionnaire and Physical Activity Scale for the Elderly) and physical assessments (6-min walk distance, hand grip muscle strength, daily step count, and body composition) at a single time point during lung cancer screening. Statistics were predominantly descriptive, with parametric data presented as mean and SD and nonparametric data presented as median and interquartile range (IQR). Results: A total of 178 participants were enrolled in this study, with a median age of 61 years. Of the participants, 61% were men and 51% were people who currently smoke. The median total International Physical Activity Questionnaire score was 1756 MET/min/wk (IQR 689, 4049). Mean total Physical Activity Scale for the Elderly score was 160 (SD 72), higher than described in healthy sedentary adults. The median daily step count was 7237 steps (IQR 5353, 10,038) and mean 6-minute walk distance was 545 m (SD 92). Median grip strengths were within predicted normal range, with an elevated median percentage body fat and low skeletal muscle mass found on body composition. Conclusion: Almost a quarter of International Lung Screen Trial participants assessed reported low levels of PA and have a potentially modifiable risk factor to improve health outcomes. Larger studies are needed to characterize the burden of inactivity among high-risk lung cancer screening populations.

Faecal microbial transfer and complex carbohydrates mediate protection against COPD.

OBJECTIVE: Chronic obstructive pulmonary disease (COPD) is a major cause of global illness and death, most commonly caused by cigarette smoke. The mechanisms of pathogenesis remain poorly understood, limiting the development of effective therapies. The gastrointestinal microbiome has been implicated in chronic lung diseases via the gut-lung axis, but its role is unclear. DESIGN: Using an in vivo mouse model of cigarette smoke (CS)-induced COPD and faecal microbial transfer (FMT), we characterised the faecal microbiota using metagenomics, proteomics and metabolomics. Findings were correlated with airway and systemic inflammation, lung and gut histopathology and lung function. Complex carbohydrates were assessed in mice using a high resistant starch diet, and in 16 patients with COPD using a randomised, double-blind, placebo-controlled pilot study of inulin supplementation. RESULTS: FMT alleviated hallmark features of COPD (inflammation, alveolar destruction, impaired lung function), gastrointestinal pathology and systemic immune changes. Protective effects were additive to smoking cessation, and transfer of CS-associated microbiota after antibiotic-induced microbiome depletion was sufficient to increase lung inflammation while suppressing colonic immunity in the absence of CS exposure. Disease features correlated with the relative abundance of Muribaculaceae, Desulfovibrionaceae and Lachnospiraceae family members. Proteomics and metabolomics identified downregulation of glucose and starch metabolism in CS-associated microbiota, and supplementation of mice or human patients with complex carbohydrates improved disease outcomes. CONCLUSION: The gut microbiome contributes to COPD pathogenesis and can be targeted therapeutically.

The International Association for the Study of Lung Cancer Staging Project for Lung Cancer: Proposals for the Revision of the M Descriptors in the Forthcoming Ninth Edition of the TNM Classification for Lung Cancer.

INTRODUCTION: This study analyzed all metastatic categories of the current TNM classification of NSCLC to propose modifications of the M component in the next edition (ninth) of the classification. METHODS: A database of 124,581 patients diagnosed between 2011 and 2019 was established; of these, 14,937 with NSCLC in stages IVA to IVB were available for this analysis. Overall survival was calculated using the Kaplan-Meier method, and prognosis was assessed using multivariable-adjusted Cox proportional hazards regression. RESULTS: The eighth edition M categories revealed good discrimination in the ninth edition data set. Assessments revealed that an increasing number of metastatic lesions were associated with decreasing prognosis; because this seems to be a continuum and adjustment for confounders was not possible, no specific lesion number was deemed appropriate for stage classification. Among tumors involving multiple metastases, decreasing prognosis was found with an increasing number of organ systems involved. Multiple assessments, including after adjustment for potential confounders, revealed that M1c patients who had metastases to a single extrathoracic organ system were prognostically distinct from M1c patients who had involvement of multiple extrathoracic organ systems. CONCLUSIONS: These data validate the eighth edition M1a and M1b categories, which are recommended to be maintained. We propose the M1c category be divided into M1c1 (involvement of a single extrathoracic organ system) and M1c2 (involvement of multiple extrathoracic organ systems).

Validation of the psychosocial consequences of screening in lung cancer questionnaire in the international lung screen trial Australian cohort.

BACKGROUND: Evaluation of psychosocial consequences of lung cancer screening with LDCT in high-risk populations has generally been performed using generic psychometric instruments. Such generic instruments have low coverage and low power to detect screening impacts. This study aims to validate an established lung cancer screening-specific questionnaire, Consequences Of Screening Lung Cancer (COS-LC), in Australian-English and describe early results from the baseline LDCT round of the International Lung Screen Trial (ILST). METHODS: The Danish-version COS-LC was translated to Australian-English using the double panel method and field tested in Australian-ILST participants to examine content validity. A random sample of 200 participants were used to assess construct validity using Rasch item response theory models. Reliability was assessed using classical test theory. The COS-LC was administered to ILST participants at prespecified timepoints including at enrolment, dependent of screening results. RESULTS: Minor linguistic alterations were made after initial translation of COS-LC to English. The COS-LC demonstrated good content validity and adequate construct validity using psychometric analysis. The four core scales fit the Rasch model, with only minor issues in five non-core scales which resolved with modification. 1129 Australian-ILST participants were included in the analysis, with minimal psychosocial impact observed shortly after baseline LDCT results. CONCLUSION: COS-LC is the first lung cancer screening-specific questionnaire to be validated in Australia and has demonstrated excellent psychometric properties. Early results did not demonstrate significant psychosocial impacts of screening. Longer-term follow-up is awaited and will be particularly pertinent given the announcement of an Australian National Lung Cancer Screening Program. TRIAL REGISTRATION: NCT02871856.

Pericardial infiltration and constriction due to cardiac actinomycosis-case report.

Background: Constrictive pericarditis associated with actinomycosis infection is a rare and challenging diagnosis due to its insidious manifestation. We describe the successful treatment of pericardial infiltration and constriction due to actinomycosis. Case summary: A 50-year-old Aboriginal man presented with insidious onset of fatigue, dyspnoea, pleuritic chest pain, fever, drenching sweats, severe exercise intolerance to 50 m, and recurrent itchy skin lesions over 8 months. Prior investigations, including serial fluorodeoxyglucose (FDG)-Positron emission tomography scans, found a progressively enlarging, metabolically active anterior mediastinal mass with two biopsies on separate occasions showing no malignancy, granulomas, tuberculosis, or other pathology. Screening for infective, autoimmune, and connective tissue disease was negative. A transthoracic echocardiogram (TTE) showed fibrinous pericarditis with extensive myocardial tethering and constrictive physiology confirmed on heart catheterization. A pericardial biopsy showed inflammatory tissue only. Biopsy of a skin lesion on the buttock showed abscess formation with Splendore Hoeppli phenomenon with Gram-positive and Grocott-positive filamentous bacteria suggestive of actinomyces, confirmed by 16S rRNA gene sequencing. He was diagnosed with cardiac actinomycosis, likely due to mediastinal infiltration from a lung infection, with haematogenous spread and treated with Penicillin G with adjunctive high-dose steroid therapy with resolution of symptoms and marked improvement in TTE features of constriction after 6 weeks. Discussion: Actinomycosis is an extremely rare cause of pericardial infiltration and constriction yet highly sensitive to penicillin, highlighting the importance of accurate diagnosis. Corticosteroids are a useful adjunct to prevent chronic constrictive pericarditis and to avoid the high morbidity and mortality associated with pericardiectomy.

An Improved Anchor-Free Nodule Detection System Using Feature Pyramid Network.

Lung cancer (LC) is the leading cause of cancer death. Detecting LC at the earliest stage facilitates curative treatment options and will improve mortality rates. Computer-aided detection (CAD) systems can help improve LC diagnostic accuracy. In this work, we propose a deep-learning-based lung nodule detection method. The proposed CAD system is a 3D anchor-free nodule detection (AFND) method based on a feature pyramid network (FPN). The deep learning-based CAD system has several novel properties: (1) It achieves region proposal and nodule classification in a single network, forming a one-step detection pipeline and reducing operation time. (2) An adaptive nodule modelling method was designed to detect nodules of various sizes. (3) The proposed AFND also establishes a novel center point selection mechanism for better classification. (4) Based on the new nodule model, a composite loss function integrating cosine similarity (CS) loss and SmoothL1loss was designed to further improve the nodule detection accuracy. Experimental results show that the AFND outperforms other similar nodule detection systems on the LUNA 16 dataset.

Biomarkers of lung cancer for screening and in never-smokers-a narrative review.

Background and Objective: Lung cancer is the leading cause of cancer-related mortality worldwide, partially attributed to late-stage diagnoses. In order to mitigate this, lung cancer screening (LCS) of high-risk patients is performed using low dose computed tomography (CT) scans, however this method is burdened by high false-positive rates and radiation exposure for patients. Further, screening programs focus on individuals with heavy smoking histories, and as such, never-smokers who may otherwise be at risk of lung cancer are often overlooked. To resolve these limitations, biomarkers have been posited as potential supplements or replacements to low-dose CT, and as such, a large body of research in this area has been produced. However, comparatively little information exists on their clinical efficacy and how this compares to current LCS strategies. Methods: Here we conduct a search and narrative review of current literature surrounding biomarkers of lung cancer to supplement LCS, and biomarkers of lung cancer in never-smokers (LCINS). Key Content and Findings: Many potential biomarkers of lung cancer have been identified with varying levels of sensitivity, specificity, clinical efficacy, and supporting evidence. Of the markers identified, multi-target panels of circulating microRNAs, lipids, and metabolites are likely the most clinically efficacious markers to aid current screening programs, as these provide the highest sensitivity and specificity for lung cancer detection. However, circulating lipid and metabolite levels are known to vary in numerous systemic pathologies, highlighting the need for further validation in large cohort randomised studies. Conclusions: Lung cancer biomarkers is a fast-expanding area of research and numerous biomarkers with potential clinical applications have been identified. However, in all cases the level of evidence supporting clinical efficacy is not yet at a level at which it can be translated to clinical practice. The priority now should be to validate existing candidate markers in appropriate clinical contexts and work to integrating these into clinical practice.

Prevalence of subclinical lung cancer detected at autopsy: a systematic review.

BACKGROUND: Lung cancer screening in high-risk populations with low-dose computed tomography is supported by international associations and recommendations. Overdiagnosis is considered a risk of screening with associated harms. The aim of this paper is to determine the prevalence of subclinical lung cancer diagnosed post-mortem to better understand the reservoir of subclinical lung cancer. METHODS: We searched EMBASE, PubMed, and MEDLINE databases from inception until March 2022 with no language restrictions. We considered all studies with ≥100 autopsies in adults. Two reviewers independently assessed eligibility of studies, extracted data, and assessed risk of bias of included studies. We performed a meta-analysis using a random-effects model for prevalence of subclinical lung cancer diagnosed post-mortem with sensitivity and subgroup analyses. RESULTS: A total of 13 studies with 16 730 autopsies were included. Pooled prevalence was 0.4% (95% CI 0.20 to 0.82%, I2 = 84%, tau2 = 1.19, low certainty evidence,16 730 autopsies). We performed a sensitivity analysis excluding studies which did not specify exclusion of children in their cohort, with a pooled prevalence of subclinical lung cancer of 0.87% (95% CI 0.48 to 1.57%, I2 = 71%, tau2 = 0.38, 6998 autopsies, 8 studies). CONCLUSIONS: This is the first published systematic review to evaluate the prevalence of post-mortem subclinical lung cancer. Compared to autopsy systematic reviews in breast, prostate and thyroid cancers, the pooled prevalence is lower in lung cancer for subclinical cancer. This result should be interpreted with caution due to the included studies risk of bias and heterogeneity, with further high-quality studies required in target screening populations.

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.

Vertebral CT attenuation outperforms standard clinical fracture risk prediction tools in detecting osteoporotic disease in lung cancer screening participants.

OBJECTIVES: Compare accuracy of vertebral Hounsfield Unit (VHU) attenuation and FRAX and Garvan Fracture Risk Calculators in identifying low bone mineral density (BMD) and prevalent vertebral compression fractures (VF) in lung cancer screening (LCS) participants. METHODS: Baseline CT scans from a single site of the International Lung Screen Trial were analysed. BMD was measured using VHU (of the most caudally imaged vertebra) and quantitative CT (QCT) (low BMD defined as <110 HU and <120 mg/cm3, respectively). Prevalent VF were classified semi-quantitatively. 10-year FRAX and Garvan fracture risks were calculated using dual energy X-ray absorptiometry (DXA) femoral neck T-score where available. Discrimination was assessed by area under receiver-operating characteristic curves (AUC). RESULTS: 535 LCS participants were included; 41% had low VHU-BMD, 56% had low QCT-BMD and 10% had ≥1 VF with ≥25% vertebral height loss. VHU demonstrated 94% specificity and 70% sensitivity in identifying low QCT-BMD. VHU was superior to fracture risk tools in discriminating low QCT-BMD (AUC: VHU 0.94 vs FRAX 0.67, Garvan 0.64 [p < 0.05]). In 64 participants with recent DXA scans, VHU was superior to FRAXT-score and GarvanT-score in discriminating low QCT-BMD (AUC: VHU 0.99, FRAXT-score 0.71, GarvanT-score 0.71 [p < 0.05]). VHU was non-inferior to FRAXT-score and GarvanT-score in discriminating VF (AUC: VHU 0.65, FRAXT-score 0.53, GarvanT-score 0.61). CONCLUSIONS: VHU outperforms clinical risk calculators in detecting low BMD and discriminates prevalent VF equally well as risk calculators with T-scores, yet is significantly simpler to perform. ADVANCES IN KNOWLEDGE: VHU measurement could aid osteoporosis assessment in high-risk smokers undergoing LCS.

Development and Validation of txSim: A Model of Advanced Lung Cancer Treatment in Australia.

BACKGROUND AND OBJECTIVE: Since 2016, new therapies have transformed the standard of care for lung cancer, creating a need for up-to-date evidence for health economic modelling. We developed a discrete event simulation of advanced lung cancer treatment to provide estimates of survival outcomes and healthcare costs in the Australian setting that can be updated as new therapies are introduced. METHODS: Treatment for advanced lung cancer was modelled under a clinician-specified treatment algorithm for Australia in 2022. Prevalence of lung cancer subpopulations was extracted from cBioPortal and the Sax Institute's 45 and Up Study, a large prospective cohort linked to cancer registrations. All costs were from the health system perspective for the year 2020. Pharmaceutical and molecular diagnostic costs were obtained from public reimbursement fees, while other healthcare costs were obtained from health system costs in the 45 and Up Study. Treatment efficacy was obtained from clinical trials and observational study data. Costs and survival were modelled over a 10-year horizon. Uncertainty intervals were generated with probabilistic sensitivity analyses. Overall survival predictions were validated against real-world studies. RESULTS: Under the 2022 treatment algorithm, estimated mean survival and costs for advanced lung cancer 10 years post-diagnosis were 16.4 months (95% uncertainty interval [UI]: 14.7-18.1) and AU$116,069 (95% UI: $107,378-$124,933). Survival and costs were higher assuming optimal treatment utilisation rates (20.5 months, 95% UI: 19.1-22.5; $154,299, 95% UI: $146,499-$161,591). The model performed well in validation, with good agreement between predicted and observed survival in real-world studies. CONCLUSIONS: Survival improvements for advanced lung cancer have been accompanied by growing treatment costs. The estimates reported here can be used for budget planning and economic evaluations of interventions across the spectrum of cancer control.

Update on Biomarkers for the Stratification of Indeterminate Pulmonary Nodules.

Lung cancer is the leading cause of cancer-related deaths. Early detection and diagnosis are critical, as survival decreases with advanced stages. Approximately 1.6 million nodules are incidentally detected every year on chest CT scan images in the United States. This number of nodules identified is likely much larger after accounting for screening-detected nodules. Most of these nodules, whether incidentally or screening detected, are benign. Despite this, many patients undergo unnecessary invasive procedures to rule out cancer because our current stratification approaches are suboptimal, particularly for intermediate probability nodules. Thus, noninvasive strategies are urgently needed. Biomarkers have been developed to assist through the continuum of lung cancer care and include blood protein-based biomarkers, liquid biopsies, quantitative imaging analysis (radiomics), exhaled volatile organic compounds, and bronchial or nasal epithelium genomic classifiers, among others. Although many biomarkers have been developed, few have been integrated into clinical practice as they lack clinical utility studies showing improved patient-centered outcomes. Rapid technologic advances and large network collaborative efforts will continue to drive the discovery and validation of many novel biomarkers. Ultimately, however, randomized clinical utility studies showing improved patient outcomes will be required to bring biomarkers into clinical practice.

Research Priorities for Interventions to Address Health Disparities in Lung Nodule Management: An Official American Thoracic Society Research Statement.

Background: Lung nodules are common incidental findings, and timely evaluation is critical to ensure diagnosis of localized-stage and potentially curable lung cancers. Rates of guideline-concordant lung nodule evaluation are low, and the risk of delayed evaluation is higher for minoritized groups. Objectives: To summarize the existing evidence, identify knowledge gaps, and prioritize research questions related to interventions to reduce disparities in lung nodule evaluation. Methods: A multidisciplinary committee was convened to review the evidence and identify key knowledge gaps in four domains: 1) research methodology, 2) patient-level interventions, 3) clinician-level interventions, and 4) health system-level interventions. A modified Delphi approach was used to identify research priorities. Results: Key knowledge gaps included 1) a lack of standardized approaches to identify factors associated with lung nodule management disparities, 2) limited data evaluating the role of social determinants of health on disparities in lung nodule management, 3) a lack of certainty regarding the optimal strategy to improve patient-clinician communication and information transmission and/or retention, and 4) a paucity of information on the impact of patient navigators and culturally trained multidisciplinary teams. Conclusions: This statement outlines a research agenda intended to stimulate high-impact studies of interventions to mitigate disparities in lung nodule evaluation. Research questions were prioritized around the following domains: 1) need for methodologic guidelines for conducting research related to disparities in nodule management, 2) evaluating how social determinants of health influence lung nodule evaluation, 3) studying approaches to improve patient-clinician communication, and 4) evaluating the utility of patient navigators and culturally enriched multidisciplinary teams to reduce disparities.