ESR/ERS statement paper on lung cancer screening

Pros and cons of reporting incidental findings in lung cancer screening

Incidental findings (IFs) are common in lung cancer screening (LCS). While the detection of some of these findings can lead to early diagnosis and treatment of clinically significant conditions, it also carries the risks of overdiagnosis and overtreatment, causing anxiety for patients and increased economic costs for health systems. Effective management of IFs requires a balanced approach guided by clear guidelines, standardized reporting, and participants-centered communication. As the field of LCS evolves, continued research and innovation will be essential in refining the strategies for managing IFs, ensuring that the benefits of screening are maximized while minimizing potential harm. Evidence-based guidelines on reporting and management of IFs, however, are still lacking. This narrative review explores the pros and cons of reporting IFs in LCS, focusing on key controversies. KEY POINTS: Reporting and managing incidental findings in lung cancer screening is largely debated. The detection of incidental findings can lead to early diagnosis of clinically significant conditions but carries the risks of overdiagnosis and overtreatment. A balance must be found to have a positive impact on the population while not placing a burden on healthcare systems.

CT acquisition protocols for lung cancer screening-current landscape and the urgent need for consistency

KEY POINTS

Standardizing CT acquisition protocols reduces radiation exposure in lung cancer screening. Cross-continent collaboration will enhance understanding of diverse clinical practices. Survey results will inform future advancements in radiology sustainability efforts.

Low Dose Computed Tomography for Lung Cancer Screening in Tuberculosis Endemic Countries: A Systematic Review and Meta-Analysis

INTRODUCTION

Lung cancer screening (LCS) using low-dose computed tomography (LDCT) reduces mortality. Nevertheless, in high tuberculosis-burden countries (HTBC), there are concerns about high false-positive rates due to persistent lung lesions from prior tuberculosis (TB) infections. This study aims to evaluate the screen-positive rate (SPR) of LDCT screening in HTBC.

METHODS

We conducted a systematic review and meta-analysis to identify studies utilizing LDCT for LCS in HTBC and reported SPR from inception to December 6, 2023. The primary outcome was the SPR, and the secondary outcome was the lung cancer detection rate (LCDR). The summary data was pooled using a random-effects model, and factors influencing the SPR were analyzed using multivariable meta-regression analysis.

RESULTS

A total of 44 studies with 477,424 individuals (59.3% men) were included in the systematic review. Lung Imaging Reporting and Data System (Lung-RADS) (31%, 14 studies) and National Lung Screening Trial (NLST) criteria (non-calcified nodule ≥ 4 mm; 10 studies) were the most common criteria used for assessing SPR. The pooled SPR and LCDR were 18.36% (95% confidence interval [CI]: 14.6-22.1) and 0.94% (95% confidence interval: 0.75-1.15), respectively. Although SPR was significantly higher with NLST criteria than Lung-RADS criteria (25.6% versus 10.4%, p < 0.0001), the LCDR remained similar (0.91% versus 0.95%, p = 0.92). Studies using NLST criteria had a higher SPR in multivariable meta-regression analysis. Nevertheless, the analysis is limited by significant statistical heterogeneity and publication bias.

CONCLUSION

Lung cancer screening by LDCT in HTBC demonstrates comparable SPR and LCDR to regions with lower TB incidence rates. Lung-RADS criteria are preferable to NLST criteria for LCS in HTBC to mitigate false-positive rates.

Lung Cancer Screening and Incidental Findings: A Research Agenda: An Official American Thoracic Society Research Statement

Background: Lung cancer screening with low-dose computed tomography (LDCT) may uncover incidental findings (IFs) unrelated to lung cancer. There may be potential benefits from identifying clinically significant IFs that warrant intervention and potential harms related to identifying IFs that are not clinically significant but may result in additional evaluation, clinician effort, patient anxiety, complications, and excess cost. Objectives: To identify knowledge and research gaps and develop and prioritize research questions to address the approach to and management of IFs. Methods: We convened a multidisciplinary panel to review the available literature on IFs detected in lung cancer screening LDCT examinations, focusing on variability and standardizing reporting, management of IFs, and evaluation of the benefits and harms of IFs, particularly cardiovascular-related IFs. We used a three-round modified Delphi process to prioritize research questions. Results: This statement identifies knowledge gaps in 1) reporting of IFs, 2) management of IFs, and 3) identifying and reporting coronary artery calcification found on lung cancer screening LDCT. Finally, we present the panel's initial 36 research questions and the final 20 prioritized questions. Conclusions: This statement provides a prioritized research agenda to further efforts focused on evaluating, managing, and increasing awareness of IFs in lung cancer screening.

Population-based systematic enrolment of individuals ensures high lung cancer screening uptake

INTRODUCTION

Lung cancer screening (LCS) is recommended by international societies, yet it is still debated how to efficiently enrol participants. The aim of this study was to evaluate systematic enrolment of patients through family physicians in a regional LCS pilot study in Estonia.

MATERIAL AND METHODS

This study was conducted in one county (with approximately 10 % of the country's population), where all family physicians were approached. In each participating practice, all 55- to 74-year-old individuals were identified and evaluated by the family physician or nurse. Two LCS inclusion criteria were used in parallel - individuals with elevated lung cancer (LC) risk, according to either smoking status (≥20 pack-years; quit <15 years ago) and/or a PLCOm2012noRace risk score (>1.5 %/6 years), underwent low-dose computed tomography (LDCT). The scans were evaluated and participants managed according to LungRADS 1.1 protocol.

RESULTS

Seventy-four participating family physician practices had 26 759 patients in the target age group. During the inclusion period 24 413 individuals were evaluated, of whom 17 215 were excluded. Of the remaining 7198 individuals, 3708 had higher LC risk and were referred for LDCT. Of the 3444 individuals who underwent LDCT, 30 were diagnosed with LC. Considering the total LCS target age group, an estimated participation rate of 79.3 % (95 %CI 78.1 %-80.5 %) was achieved.

CONCLUSION

Population-based systematic enrolment of participants for LCS by family physicians and nurses ensured very high uptake in the target group, providing a valuable reference for planning LCS programs in countries with family physicians on board.

The added value of an AI-based body composition analysis in a lung cancer screening population: preliminary results

BACKGROUND AND AIMS

Body composition has been linked with clinical and prognostic outcomes in patients with cancer and cardiovascular diseases. Body composition analysis in lung cancer screening (LCS) is very limited. This study aimed at assessing the association of subcutaneous fat volume (SFV) and subcutaneous fat density (SFD), measured on chest ultra-low dose computed tomography (ultra-LDCT) images by a fully automated artificial intelligence (AI)-based software, with clinical and anthropometric characteristics in a LCS population.

METHODS AND RESULTS

Demographic, clinical, and dietary data were obtained from the written questionnaire completed by each participant at the first visit, when anthropometric measurements, blood sample collection and chest ultra-LDCT were performed. Images were analyzed for automated 3D segmentation of subcutaneous fat and muscle. The analysis included 938 volunteers (372 females); men with a smoking history of ≥40 pack-years had higher SFV (p = 0.0009), while former smokers had lower SFD (p = 0.0019). In female participants, SFV and SFD differed significantly according to age. SFV increased with rising BMI, waist circumference, waist-hip ratio, and CRP levels ≥2 mg/L (p < 0.0001), whereas SFD decreased with rising BMI, waist circumference, waist-hip ratio, and CRP levels ≥2 mg/L (p < 0.001) in both sexes. SFV was associated with glycemia and triglycerides levels (p = 0.0067 and p=<0.0001 in males, p = 0.0074 and p < 0.0001 in females, respectively), while SFD with triglycerides levels (p < 0.0001).

CONCLUSION

We observed different associations of SFV and SFD with age and smoking history between men and women, whereas the association with anthropometric data, CRP, glycemia and triglycerides levels was similar in the two sexes.

Quality assurance in lung cancer screening

The effectiveness of screening programmes is critically dependent on the accuracy of the screening test. Where this relies on clinical expertise, there is an imperative to assure that the level of expertise meets expected standards. In cancer screening involving images, the focus is on the reader. Auditing of results is fraught with difficulty because of the time taken to accumulate enough data with confirmed outcomes to identify underperformance before any harm is done. Late recognition can lead to the need for reanalysis and recall of screening participants with loss of confidence in the programme. External quality assurance (EQA) is a method that enables clinical expertise to be tested rapidly by using test datasets with confirmed clinical outcome. In the UK, the breast cancer screening programme has had EQA in place for over 30 years. This article describes the development of the first EQA process in lung cancer screening, using the experience gained from running the breast cancer EQA, and the proposed future developments.

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.

The Saudi Thoracic Society Evidence-based guidelines for the diagnosis and management of chronic obstructive pulmonary disease

The Saudi Thoracic Society (STS) developed an updated evidence-based guideline for diagnosing and managing chronic obstructive pulmonary disease (COPD) in Saudi Arabia. This guideline aims to provide a comprehensive and unbiased review of current evidence for assessing, diagnosing, and treating COPD. While epidemiological data on COPD in Saudi Arabia are limited, the STS panel believes that the prevalence is increasing due to rising rates of tobacco smoking. The key objectives of the guidelines are to facilitate accurate diagnosis of COPD, identify the risk for COPD exacerbations, and provide recommendations for relieving and reducing COPD symptoms in stable patients and during exacerbations. A unique aspect of this guideline is its simplified, practical approach to classifying patients into three classes based on symptom severity using the COPD Assessment Test and the risk of exacerbations and hospitalizations. The guideline provides the reader with an executive summary of recommended COPD treatments based on the best available evidence and also addresses other major aspects of COPD management and comorbidities. This guideline is primarily intended for use by internists and general practitioners in Saudi Arabia.

Coronary calcium score and emphysema extent on different CT radiation dose protocols in lung cancer screening

OBJECTIVES

To assess the consistency of automated measurements of coronary artery calcification (CAC) burden and emphysema extent on computed tomography (CT) images acquired with different radiation dose protocols in a lung cancer screening (LCS) population.

MATERIALS AND METHODS

The patient cohort comprised 361 consecutive screenees who underwent a low-dose CT (LDCT) scan and an ultra-low-dose CT (ULDCT) scan at an incident screening round. Exclusion criteria for CAC measurements were software failure and previous history of CVD, including coronary stenting, whereas for emphysema assessment, software failure only. CT images were retrospectively analyzed by a fully automated AI software for CAC scoring, using three predefined Agatston score categories (0-99, 100-399, and ≥ 400), and emphysema quantification, using the percentage of low attenuation areas (%LAA). Demographic and clinical data were obtained from the written questionnaire completed by each participant at the first visit. Agreement for CAC and %LAA categories was measured by the k-Cohen Index with Fleiss-Cohen weights (Kw) and Intraclass Correlation Coefficient (ICC) with 95% Confidence Interval (CI).

RESULTS

An overlap of CAC strata was observed in 275/327 (84%) volunteers, with an almost perfect agreement (Kw = 0.86, 95% CI 0.82-0.90; ICC = 0.86, 95% CI 0.79-0.90), while an overlap of %LAA strata was found in 204/356 (57%) volunteers, with a moderate agreement (Kw = 0.57, 95% CI 0.51-0.63; ICC = 0.57, 95% CI 0.21-0.75).

CONCLUSION

Automated CAC quantification on ULDCT seems feasible, showing similar results to those obtained on LDCT, while the quantification of emphysema tended to be overestimated on ULDCT images.

KEY POINTS

Question Evidence demonstrating that coronary artery calcification and emphysema can be automatedly quantified on ultra-low-dose chest CT is still awaited. Findings Coronary artery calcification and emphysema measurements were similar among different CT radiation dose protocols; their automated quantification is feasible on ultra-low-dose CT. Clinical relevance Ultra-low-dose CT-based LCS might offer an opportunity to improve the secondary prevention of cardiovascular and respiratory diseases through automated quantification of both CAC burden and emphysema extent.

Advancements in lung cancer: a comprehensive perspective on diagnosis, staging, therapy and follow-up from the SAKK Working Group on Imaging in Diagnosis and Therapy Monitoring

In 2015, around 4400 individuals received a diagnosis of lung cancer, and Switzerland recorded approximately 3200 deaths related to lung cancer. Advances in detection, such as lung cancer screening and improved treatments, have led to increased identification of early-stage lung cancer and higher chances of long-term survival. This progress has introduced new considerations in imaging, emphasising non-invasive diagnosis and characterisation techniques like radiomics. Treatment aspects, such as preoperative assessment and the implementation of immune response evaluation criteria in solid tumours (iRECIST), have also seen advancements. For those undergoing curative treatment for lung cancer, guidelines propose follow-up with computed tomography (CT) scans within a specific timeframe. However, discrepancies exist in published guidelines, and there is a lack of universally accepted recommendations for follow-up procedures. This white paper aims to provide a certain standard regarding the use of imaging on the diagnosis, staging, treatment and follow-up of patients with lung cancer.

The Pivotal Role of Baseline LDCT for Lung Cancer Screening in the Era of Artificial Intelligence

In this narrative review, we address the ongoing challenges of lung cancer (LC) screening using chest low-dose computerized tomography (LDCT) and explore the contributions of artificial intelligence (AI), in overcoming them. We focus on evaluating the initial (baseline) LDCT examination, which provides a wealth of information relevant to the screening participant's health. This includes the detection of large-size prevalent LC and small-size malignant nodules that are typically diagnosed as LCs upon growth in subsequent annual LDCT scans. Additionally, the baseline LDCT examination provides valuable information about smoking-related comorbidities, including cardiovascular disease, chronic obstructive pulmonary disease, and interstitial lung disease (ILD), by identifying relevant markers. Notably, these comorbidities, despite the slow progression of their markers, collectively exceed LC as ultimate causes of death at follow-up in LC screening participants. Computer-assisted diagnosis tools currently improve the reproducibility of radiologic readings and reduce the false negative rate of LDCT. Deep learning (DL) tools that analyze the radiomic features of lung nodules are being developed to distinguish between benign and malignant nodules. Furthermore, AI tools can predict the risk of LC in the years following a baseline LDCT. AI tools that analyze baseline LDCT examinations can also compute the risk of cardiovascular disease or death, paving the way for personalized screening interventions. Additionally, DL tools are available for assessing osteoporosis and ILD, which helps refine the individual's current and future health profile. The primary obstacles to AI integration into the LDCT screening pathway are the generalizability of performance and the explainability.

Lung cancer screening by low-dose CT scan in France: final results of the DEP KP80 study after three rounds

BACKGROUND

In prior randomised controlled trials, lung cancer screening using low-dose computed tomography (LDCT) has been shown to reduce lung cancer mortality and overall mortality. Despite these results, organised screening in France remains a challenge. This study assessed the feasibility and efficacy of lung cancer screening within a real-life context in a French administrative territory.

METHODS

DEP KP80 was a single-arm prospective study. Participants aged between 55 and 74 years, smokers or former smokers of ≥30 pack-years, were recruited. An annual LDCT scan was scheduled and three rounds were performed. Subjects were selected by general practitioners or pulmonologists, who checked the inclusion criteria and prescribed the CT scan.

FINDINGS

Between March 2016 and February 2020, 1254 participants were enrolled. Overall, 945 (75.4%) participants underwent baseline LDCT (T0), 376 (42.8%) completed the first round (T1) and 270 (31%) the second (T2) one. Forty-two lung cancers were diagnosed, 30 cancers (71.4%) were stage I or II and 34 cancers (80.9%) were treated surgically. In this study, the overall positive predictive value for a positive screening was 48% (95% CI 37-59) and the negative predictive value 100% (95% CI 100-100).

INTERPRETATION

This study demonstrated the feasibility and efficacy of lung cancer screening in a real-life context with most lung cancers diagnosed at an early stage and surgically removed. Our results also highlighted the importance of participation in each round, underlining the fact that optimising organisation is a major goal.

FUNDING

Agence Régionale de Santé de Picardie, La Ligue contre le cancer, le Conseil Départemental de la Somme, and AstraZeneca.

Effect of Exercise and Pulmonary Rehabilitation in Pre- and Post-Surgical Patients with Lung Cancer: Systematic Review and Meta-Analysis

Background and objectives: Lung cancer is a common cancer, and its impact on public health is not only reflected in the 1 million deaths it causes annually but also in the significant implications it has on daily activities and quality of life, resulting in a considerable burden on healthcare systems. This review aims to determine the effects of pulmonary rehabilitation and pre- or post-surgical exercise in patients with lung cancer. Materials and methods: A systematic review with a meta-analysis of randomized controlled trials published between 2010 and 2024 was conducted; the search was carried out in PubMed, Cochrane Clinical Trial, SCOPUS, Science Direct, Web of Science, Scielo, and LILAC. Results: Pulmonary rehabilitation or exercise before surgery was associated with a greater 6 min walking distance (MD: 37.42, 95% CI: 9.68-65.1; p = 0.008); however, it had no implications on hospital stay (MD: -0.91, 95% CI: -1.88-0.055; p = 0.06). When the intervention was performed post-surgery, higher FEV1 (SMD: 0.62, 95% CI: 0.32-0.92; p = 0.0001) and improved 6 min walking distances (60.8, 95% CI: 20.96-100.6; p = 0.0033) were found compared to standard management. Conclusions: This review suggests that, depending on the timing of implementation, pulmonary rehabilitation or exercise could produce positive effects on certain clinical variables in lung cancer patients.

Smartphone application versus written material for smoking reduction and cessation in individuals undergoing low-dose computed tomography (LDCT) screening for lung cancer: a phase II open-label randomised controlled trial

Background

Counseling, nicotine replacement, and other cessation medications have been proven effective in smoking cessation. The wide-scale adoption of smartphones and other mobile devices has opened new possibilities for scalable and personalized smoking cessation approaches. The study investigated whether a smartphone application would be more effective than written material for smoking cessation and reduction in smoking in individuals undergoing low-dose computed tomography (LDCT) screening for lung cancer (NCT05630950).

Methods

This randomized controlled trial enrolled 201 current smokers with marked smoking history (smoked ≥15 cigarettes/day for ≥25 years or smoked ≥10 cigarettes/day for ≥30 years). Participants were stratified by age and pack-years and randomized in 1:1 fashion to the developed smartphone application (experimental arm) or written material (standard of care). All the subjects underwent LDCT screening. Self-reported smoking cessation at three and six months were the primary endpoints of the study. The smoking-related secondary endpoints of the study were the percentage of individuals who had reduced the number of smoked cigarettes/d from the baseline.

Findings

Between Nov 18, 2022, and Apr 14, 2023, 201 patients were screened at Oulu University Hospital, Finland, of whom all were randomly assigned to smartphone application (n = 101) or written cessation material (n = 100); 200 were included in the full analysis set. Study arms were well-balanced for all the studied demographic factors. Subjects randomized to the smartphone application arm had significantly higher rates for self-reported smoking cessation at three (19.8 versus 7.1%; OR 3.175 CI 95% 1.276-7.899) and six months (18.8 versus 7.1%; OR 2.847 CI 95% 1.137-7.128). In the experimental arm, individuals with a frequent use of the application had a higher chance for smoking cessation at three (p < 0.001) and six months (p = 0.003).

Interpretation

The study showed that the developed smartphone application increases the likelihood for smoking cessation in individuals undergoing lung cancer LDCT screening.

Funding

AstraZeneca, Roche, and Cancer Foundation Finland.

Impact of deep learning image reconstruction on volumetric accuracy and image quality of pulmonary nodules with different morphologies in low-dose CT

BACKGROUND

This study systematically compares the impact of innovative deep learning image reconstruction (DLIR, TrueFidelity) to conventionally used iterative reconstruction (IR) on nodule volumetry and subjective image quality (IQ) at highly reduced radiation doses. This is essential in the context of low-dose CT lung cancer screening where accurate volumetry and characterization of pulmonary nodules in repeated CT scanning are indispensable.

MATERIALS AND METHODS

A standardized CT dataset was established using an anthropomorphic chest phantom (Lungman, Kyoto Kaguku Inc., Kyoto, Japan) containing a set of 3D-printed lung nodules including six diameters (4 to 9 mm) and three morphology classes (lobular, spiculated, smooth), with an established ground truth. Images were acquired at varying radiation doses (6.04, 3.03, 1.54, 0.77, 0.41 and 0.20 mGy) and reconstructed with combinations of reconstruction kernels (soft and hard kernel) and reconstruction algorithms (ASIR-V and DLIR at low, medium and high strength). Semi-automatic volumetry measurements and subjective image quality scores recorded by five radiologists were analyzed with multiple linear regression and mixed-effect ordinal logistic regression models.

RESULTS

Volumetric errors of nodules imaged with DLIR are up to 50% lower compared to ASIR-V, especially at radiation doses below 1 mGy and when reconstructed with a hard kernel. Also, across all nodule diameters and morphologies, volumetric errors are commonly lower with DLIR. Furthermore, DLIR renders higher subjective IQ, especially at the sub-mGy doses. Radiologists were up to nine times more likely to score the highest IQ-score to these images compared to those reconstructed with ASIR-V. Lung nodules with irregular margins and small diameters also had an increased likelihood (up to five times more likely) to be ascribed the best IQ scores when reconstructed with DLIR.

CONCLUSION

We observed that DLIR performs as good as or even outperforms conventionally used reconstruction algorithms in terms of volumetric accuracy and subjective IQ of nodules in an anthropomorphic chest phantom. As such, DLIR potentially allows to lower the radiation dose to participants of lung cancer screening without compromising accurate measurement and characterization of lung nodules.

Absolute ground truth-based validation of computer-aided nodule detection and volumetry in low-dose CT imaging

PURPOSE

To validate the performance of computer-aided detection (CAD) and volumetry software using an anthropomorphic phantom with a ground truth (GT) set of 3D-printed nodules.

METHODS

The Kyoto Kaguku Lungman phantom, containing 3D-printed solid nodules including six diameters (4 to 9 mm) and three morphologies (smooth, lobulated, spiculated), was scanned at varying CTDIvol levels (6.04, 1.54 and 0.20 mGy). Combinations of reconstruction algorithms (iterative and deep learning image reconstruction) and kernels (soft and hard) were applied. Detection, volumetry and density results recorded by a commercially available AI-based algorithm (AVIEW LCS + ) were compared to the absolute GT, which was determined through µCT scanning at 50 µm resolution. The associations between image acquisition parameters or nodule characteristics and accuracy of nodule detection and characterization were analyzed with chi square tests and multiple linear regression.

RESULTS

High levels of detection sensitivity and precision (minimal 83 % and 91 % respectively) were observed across all acquisitions. Neither reconstruction algorithm nor radiation dose showed significant associations with detection. Nodule diameter however showed a highly significant association with detection (p < 0.0001). Volumetric measurements for nodules > 6 mm were accurate within 10 % absolute range from volumeGT, regardless of dose and reconstruction. Nodule diameter and morphology are major determinants of volumetric accuracy (p < 0.001). Density assignment was not significantly influenced by any parameters.

CONCLUSIONS

Our study confirms the software's accurate performance in nodule volumetry, detection and density characterization with robustness for variations in CT imaging protocols. This study suggests the incorporation of similar phantom setups in quality assurance of CAD tools.

Application of rapid on-site evaluation combined with flexible bronchoscopy in the diagnosis of lung lesions

BACKGROUND

Pathology is considered the gold standard for the diagnosis of lung lesions, but the pathological result is relatively lagging and cannot provide real-time guidance for the biopsy procedure.

OBJECTIVE

To investigate the potential application of rapid on-site evaluation (ROSE) during flexible bronchoscopy (FB) in the evaluation and diagnosis of lung lesions.

PATIENTS AND METHODS

Consecutive patients who underwent FB for the diagnosis of lung lesions between August 2022 and February 2023 were included in this retrospective study. 294 patients underwent FB with ROSE, while 304 patients underwent FB without ROSE. The final pathological results and the number of patients undergoing repeat biopsies were recorded in both groups. Specifically, we conducted separate statistical analysis for patients undergoing different biopsy methods, including the endobronchial biopsy (EBB), radial probe endobronchial ultrasound transbronchial lung biopsy with guide sheath (r-EBUS-GS-TBLB), and the endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) to study the detailed roles that ROSE plays under different biopsy methods.

RESULTS

The adequacy rate of biopsy specimens from the non-ROSE group was significantly lower than that of the ROSE group (259/281 = 92.17 % vs. 263/268 = 98.13 %, p = 0.001). Meanwhile, fewer patients underwent repeat biopsies in the ROSE group compared to the non-ROSE group (2/294 = 0.68 % vs. 10/304 = 3.29 %, p = 0.023). For the ROSE group, the consistency between ROSE diagnoses and final pathological diagnoses was 94.40 % (κ = 0.886), with 95.58 % for benign diseases and 93.55 % for malignant diseases.

CONCLUSION

The utility of ROSE during FB increases the adequacy rate of biopsy specimens and thus decreases the need for repeat biopsies in patients with lung lesions to get a definite diagnosis. Moreover, the high consistency between ROSE diagnoses and final pathological diagnoses suggests that ROSE is a reliable tool for optimizing the diagnosis of lung lesions.

Cardiac troponin I as predictor for cardiac and other mortality in the German randomized lung cancer screening trial (LUSI)

Cardiac Troponin I (cTnI) could be used to identify individuals at elevated risk of cardiac death in lung cancer (LC) screening settings. In a population-based, randomized LC screening trial in Germany ("LUSI" study) serum cTnI was measured by high-sensitivity assay in blood samples collected at baseline, and categorized into unquantifiable/low (< 6 ng/L), intermediate (≥ 6-15 ng/L), and elevated (≥ 16 ng/L). Cox proportional-hazard models were used to estimate risk of all-cause and cardiac mortality with cTnI levels. After exclusion criteria, 3653 participants were included for our analyses, of which 82.4% had low, 12.8% intermediate and 4.8% elevated cTnI, respectively. Over a median follow up of 11.87 years a total of 439 deaths occurred, including 67 caused by cardiac events. Within the first 5 years after cTnI measurement, intermediate or elevated cTnI levels showed approximately 1.7 (HR = 1.69 [95% CI 0.57-5.02) and 4.7-fold (HR = 4.66 [1.73-12.50]) increases in risk of cardiac death relative to individuals with unquantifiable/low cTnI, independently of age, sex, smoking and other risk factors. Within this time interval, a risk model based on age, sex, BMI, smoking history and cTnI showed a combined area under the ROC curve (AUC) of 73.6 (58.1-87.3), as compared to 70.4 (53.3-83.5) for a model without cTnI. Over the time interval of > 5-10 years after blood donation, the relative risk associations with cTnI and were weaker. cTnI showed no association with mortality from any other (non-cardiac) cause. Our findings show that cTnI may be of use for identifying individuals at elevated risk specifically of short-term cardiac mortality in the context of LC screening.

Multi-scale representation of surface-enhanced Raman spectroscopy data for deep learning-based liver cancer detection

The early detection of liver cancer greatly improves survival rates and allows for less invasive treatment options. As a non-invasive optical detection technique, Surface-Enhanced Raman Spectroscopy (SERS) has shown significant potential in early cancer detection, providing multiple advantages over conventional methods. The majority of existing cancer detection methods utilize multivariate statistical analysis to categorize SERS data. However, these methods are plagued by issues such as information loss during dimensionality reduction and inadequate ability to handle nonlinear relationships within the data. To overcome these problems, we first use wavelet transform with its multi-scale analysis capability to extract multi-scale features from SERS data while minimizing information loss compared to traditional methods. Moreover, deep learning is employed for classification, leveraging its strong nonlinear processing capability to enhance accuracy. In addition, the chosen neural network incorporates a data augmentation method, thereby enriching our training dataset and mitigating the risk of overfitting. Moreover, we acknowledge the significance of selecting the appropriate wavelet basis functions in SERS data processing, prompting us to choose six specific ones for comparison. We employ SERS data from serum samples obtained from both liver cancer patients and healthy volunteers to train and test our classification model, enabling us to assess its performance. Our experimental results demonstrate that our method achieved outstanding and healthy volunteers to train and test our classification model, enabling us to assess its performance. Our experimental results demonstrate that our method achieved outstanding performance, surpassing the majority of multivariate statistical analysis and traditional machine learning classification methods, with an accuracy of 99.38 %, a sensitivity of 99.8 %, and a specificity of 97.0 %. These results indicate that the combination of SERS, wavelet transform, and deep learning has the potential to function as a non-invasive tool for the rapid detection of liver cancer.

Study rationale and design of the PEOPLHE trial

PURPOSE

Lung cancer screening (LCS) by low-dose computed tomography (LDCT) demonstrated a 20-40% reduction in lung cancer mortality. National stakeholders and international scientific societies are increasingly endorsing LCS programs, but translating their benefits into practice is rather challenging. The "Model for Optimized Implementation of Early Lung Cancer Detection: Prospective Evaluation Of Preventive Lung HEalth" (PEOPLHE) is an Italian multicentric LCS program aiming at testing LCS feasibility and implementation within the national healthcare system. PEOPLHE is intended to assess (i) strategies to optimize LCS workflow, (ii) radiological quality assurance, and (iii) the need for dedicated resources, including smoking cessation facilities.

METHODS

PEOPLHE aims to recruit 1.500 high-risk individuals across three tertiary general hospitals in three different Italian regions that provide comprehensive services to large populations to explore geographic, demographic, and socioeconomic diversities. Screening by LDCT will target current or former (quitting < 10 years) smokers (> 15 cigarettes/day for > 25 years, or > 10 cigarettes/day for > 30 years) aged 50-75 years. Lung nodules will be volumetric measured and classified by a modified PEOPLHE Lung-RADS 1.1 system. Current smokers will be offered smoking cessation support.

CONCLUSION

The PEOPLHE program will provide information on strategies for screening enrollment and smoking cessation interventions; administrative, organizational, and radiological needs for performing a state-of-the-art LCS; collateral and incidental findings (both pulmonary and extrapulmonary), contributing to the LCS implementation within national healthcare systems.

Immediate smoking cessation support during lung cancer screening: long-term outcomes from two randomised controlled trials

BACKGROUND

Immediate smoking cessation interventions delivered alongside targeted lung health checks (TLHCs) to screen for lung cancer increase self-reported abstinence at 3 months. The impact on longer term, objectively confirmed quit rates remains to be established.

METHODS

We followed up participants from two clinical trials in people aged 55-75 years who smoked and took part in a TLHC. These randomised participants in the TLHC by day of attendance to either usual care (UC) (signposting to smoking cessation services) or an offer of immediate smoking cessation support including pharmacotherapy. In the QuLIT1 trial, this was delivered face to face and in QuLIT2, it was delivered remotely. Follow-up was conducted 12 months after the TLHC by telephone interview with subsequent biochemical verification of smoking cessation using exhaled CO.

RESULTS

430 people were enrolled initially (115 in QuLIT1 and 315 in QuLIT2), with 4 deaths before 12 months leaving 426 (62.1±5.27 years old and 48% women) participants for analysis. At 12 months, those randomised to attend on smoking cessation support intervention days had higher quit rates compared with UC adjusted for age, gender, deprivation, and which trial they had been in; self-reported 7-day point prevalence (20.0% vs 12.8%; adjusted OR (AOR)=1.78; 95% CI 1.04 to 2.89) and CO-verified quits (12.1% vs 4.7%; AOR=2.97; 95% CI 1.38 to 6.90). Those in the intervention arm were also more likely to report having made a quit attempt (30.2% vs UC 18.5%; AOR 1.90; 95% CI 1.15 to 3.15).

CONCLUSION

Providing immediate smoking cessation support alongside TLHC increases long term, biochemically confirmed smoking abstinence.

TRIAL REGISTRATION NUMBER

ISRCTN12455871.

[Statement Paper on the Implementation of a National Organized Program in Germany for the Early Detection of Lung Cancer in Risk Populations Using Low-dose CT Screening Including Management of Screening Findings]

The process of implementing early detection of lung cancer with low-dose CT (LDCT) in Germany has gained significant momentum in recent years. It is expected that the ordinance of the Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protection (BMUV) on early detection of lung cancer, which has been commented on by the professional societies, will come into effect by the end of 2023. Based on this regulation, the Federal Joint Committee (G-BA) will set up a program for early lung cancer detection with LDCT in the near future. In this position paper, the specialist societies involved in lung cancer screening present concrete cornerstones for a uniform, structured and quality-assured early detection program for lung cancer in Germany to make a constructive contribution to this process.

[Statement paper on the implementation of a national organized program in Germany for the early detection of lung cancer in risk populations using low-dose CT screening including management of screening findings]

The process of implementing early detection of lung cancer with low-dose CT (LDCT) in Germany has gained significant momentum in recent years. It is expected that the ordinance of the Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protection (BMUV) on the early detection of lung cancer, which has been commented on by the professional societies, will come into effect by the end of 2023. Based on this regulation, the Federal Joint Committee (G-BA) will set up a program for early lung cancer detection with LDCT in the near future. In this position paper, the specialist societies involved in lung cancer screening present key points for a uniform, structured and quality-assured early detection program for lung cancer in Germany to make a constructive contribution to this process. CITATION FORMAT: · Vogel-Claussen J, Blum TG, Andreas S et al. Position paper on the implementation of a nationally organized program in Germany for the early detection of lung cancer in high-risk populations using low-dose CT screening including the management of screening findings requiring further workup. Fortschr Röntgenstr 2024; 196: DOI 10.1055/a-2178-2846.

The Smokers Health Multiple ACtions (SMAC-1) Trial: Study Design and Results of the Baseline Round

BACKGROUND

Lung cancer screening with low-dose helical computed tomography (LDCT) reduces mortality in high-risk subjects. Cigarette smoking is linked to up to 90% of lung cancer deaths. Even more so, it is a key risk factor for many other cancers and cardiovascular and pulmonary diseases. The Smokers health Multiple ACtions (SMAC-1) trial aimed to demonstrate the feasibility and effectiveness of an integrated program based on the early detection of smoking-related thoraco-cardiovascular diseases in high-risk subjects, combined with primary prevention. A new multi-component screening design was utilized to strengthen the framework on conventional lung cancer screening programs. We report here the study design and the results from our baseline round, focusing on oncological findings.

METHODS

High-risk subjects were defined as being >55 years of age and active smokers or formers who had quit within 15 years (>30 pack/y). A PLCOm2012 threshold >2% was chosen. Subject outreach was streamlined through media campaign and general practitioners' engagement. Eligible subjects, upon written informed consent, underwent a psychology consultation, blood sample collection, self-evaluation questionnaire, spirometry, and LDCT scan. Blood samples were analyzed for pentraxin-3 protein levels, interleukins, microRNA, and circulating tumor cells. Cardiovascular risk assessment and coronary artery calcium (CAC) scoring were performed. Direct and indirect costs were analyzed focusing on the incremental cost-effectiveness ratio per quality-adjusted life years gained in different scenarios. Personalized screening time-intervals were determined using the "Maisonneuve risk re-calculation model", and a threshold <0.6% was chosen for the biennial round.

RESULTS

In total, 3228 subjects were willing to be enrolled. Out of 1654 eligible subjects, 1112 participated. The mean age was 64 years (M/F 62/38%), with a mean PLCOm2012 of 5.6%. Former and active smokers represented 23% and 77% of the subjects, respectively. At least one nodule was identified in 348 subjects. LDCTs showed no clinically significant findings in 762 subjects (69%); thus, they were referred for annual/biennial LDCTs based on the Maisonneuve risk (mean value = 0.44%). Lung nodule active surveillance was indicated for 122 subjects (11%). Forty-four subjects with baseline suspicious nodules underwent a PET-FDG and twenty-seven a CT-guided lung biopsy. Finally, a total of 32 cancers were diagnosed, of which 30 were lung cancers (2.7%) and 2 were extrapulmonary cancers (malignant pleural mesothelioma and thymoma). Finally, 25 subjects underwent lung surgery (2.25%). Importantly, there were zero false positives and two false negatives with CT-guided biopsy, of which the patients were operated on with no stage shift. The final pathology included lung adenocarcinomas (69%), squamous cell carcinomas (10%), and others (21%). Pathological staging showed 14 stage I (47%) and 16 stage II-IV (53%) cancers.

CONCLUSIONS

LDCTs continue to confirm their efficacy in safely detecting early-stage lung cancer in high-risk subjects, with a negligible risk of false-positive results. Re-calculating the risk of developing lung cancer after baseline LDCTs with the Maisonneuve model allows us to optimize time intervals to subsequent screening. The Smokers health Multiple ACtions (SMAC-1) trial offers solid support for policy assessments by policymakers. We trust that this will help in developing guidelines for the large-scale implementation of lung cancer screening, paving the way for better outcomes for lung cancer patients.

Evaluation of Conventional Cardiovascular Risk Factors and Ordinal Coronary Artery Calcium Scoring in a Lung Cancer Screening Cohort

(1) Background: Lung cancer screening (LCS) consists of low-dose computed tomography (LDCT) results to reduce lung cancer-related mortality. The LCS program has a unique opportunity to impact CVD mortality by providing tools for CVD risk assessment and implementing preventative strategies. In this study, we estimated standardized CVD risk (SCORE) and assessed the prevalence of coronary artery calcium (CAC) in a Polish LCS cohort. (2) Methods: In this observational study, 494 LCS participants aged 50-79 years with a cigarette smoking history of at least 30 pack-years were included. Medical history, anthropometric measurements, blood pressure measurements, serum glucose, and cholesterol levels were assessed in one visit. CVD risk assessment using SCORE tables was performed. The results were compared to the general population (NATPOL 2011 study). On LDCT scans, CAC was classified using an Ordinal Score ranging from 0 to 12. (3) Results: The prevalence of classic cardiovascular risk factors was very high. Among study participants, 83.7% of men and 40.7% of women were classified with a very high CVD SCORE risk (>10%). CAC was reported in 190 (47%) participants. Calcification was categorized as severe (CAC ≥ 4) in 84 (21%) participants. (4) Conclusions: Due to the high cardiovascular risk, intensive preventive strategies are recommended for LCS participants.

[Statement paper on the implementation of a national organized program in Germany for the early detection of lung cancer in risk populations using low-dose CT screening including management of screening findings]

The process of implementing early detection of lung cancer with low-dose CT (LDCT) in Germany has gained significant momentum in recent years. It is expected that the ordinance of the Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protection (BMUV) on early detection of lung cancer, which has been commented on by the professional societies, will come into effect by the end of 2023. Based on this regulation, the Federal Joint Committee (G-BA) will set up a program for early lung cancer detection with LDCT in the near future. In this position paper, the specialist societies involved in lung cancer screening present concrete cornerstones for a uniform, structured and quality-assured early detection program for lung cancer in Germany to make a constructive contribution to this process.

Screening approaches for lung cancer by blood-based biomarkers: Challenges and opportunities

Lung cancer (LC) is one of the leading causes for cancer-related deaths in the world, accounting for 28% of all cancer deaths in Europe. Screening for lung cancer can enable earlier detection of LC and reduce lung cancer mortality as was demonstrated in several large image-based screening studies such as the NELSON and the NLST. Based on these studies, screening is recommended in the US and in the UK a targeted lung health check program was initiated. In Europe lung cancer screening (LCS) has not been implemented due to limited data on cost-effectiveness in the different health care systems and questions on for example the selection of high-risk individuals, adherence to screening, management of indeterminate nodules, and risk of overdiagnosis. Liquid biomarkers are considered to have a high potential to address these questions by supporting pre- and post- Low Dose CT (LDCT) risk-assessment thereby improving the overall efficacy of LCS. A wide variety of biomarkers, including cfDNA, miRNA, proteins and inflammatory markers have been studied in the context of LCS. Despite the available data, biomarkers are currently not implemented or evaluated in screening studies or screening programs. As a result, it remains an open question which biomarker will actually improve a LCS program and do this against acceptable costs. In this paper we discuss the current status of different promising biomarkers and the challenges and opportunities of blood-based biomarkers in the context of lung cancer screening.

Lung Cancer Screening: The European Perspective

Several randomized and observational studies on lung cancer screening held in Europe significantly contributed to the knowledge on low-dose computed tomography screening targets in high-risk individuals with smoking history and older than 50 years. In particular, steps forward have been made in the field of risk modeling, screening interval, diagnostic protocol with volumetry, optimization, overdiagnosis estimation, oncological outcome, oncological risk due to radiation exposure, recruitment, and communication strategy.

Assessing eligibility for lung cancer screening using parsimonious ensemble machine learning models: A development and validation study

BACKGROUND

Risk-based screening for lung cancer is currently being considered in several countries; however, the optimal approach to determine eligibility remains unclear. Ensemble machine learning could support the development of highly parsimonious prediction models that maintain the performance of more complex models while maximising simplicity and generalisability, supporting the widespread adoption of personalised screening. In this work, we aimed to develop and validate ensemble machine learning models to determine eligibility for risk-based lung cancer screening.

METHODS AND FINDINGS

For model development, we used data from 216,714 ever-smokers recruited between 2006 and 2010 to the UK Biobank prospective cohort and 26,616 high-risk ever-smokers recruited between 2002 and 2004 to the control arm of the US National Lung Screening (NLST) randomised controlled trial. The NLST trial randomised high-risk smokers from 33 US centres with at least a 30 pack-year smoking history and fewer than 15 quit-years to annual CT or chest radiography screening for lung cancer. We externally validated our models among 49,593 participants in the chest radiography arm and all 80,659 ever-smoking participants in the US Prostate, Lung, Colorectal and Ovarian (PLCO) Screening Trial. The PLCO trial, recruiting from 1993 to 2001, analysed the impact of chest radiography or no chest radiography for lung cancer screening. We primarily validated in the PLCO chest radiography arm such that we could benchmark against comparator models developed within the PLCO control arm. Models were developed to predict the risk of 2 outcomes within 5 years from baseline: diagnosis of lung cancer and death from lung cancer. We assessed model discrimination (area under the receiver operating curve, AUC), calibration (calibration curves and expected/observed ratio), overall performance (Brier scores), and net benefit with decision curve analysis. Models predicting lung cancer death (UCL-D) and incidence (UCL-I) using 3 variables-age, smoking duration, and pack-years-achieved or exceeded parity in discrimination, overall performance, and net benefit with comparators currently in use, despite requiring only one-quarter of the predictors. In external validation in the PLCO trial, UCL-D had an AUC of 0.803 (95% CI: 0.783, 0.824) and was well calibrated with an expected/observed (E/O) ratio of 1.05 (95% CI: 0.95, 1.19). UCL-I had an AUC of 0.787 (95% CI: 0.771, 0.802), an E/O ratio of 1.0 (95% CI: 0.92, 1.07). The sensitivity of UCL-D was 85.5% and UCL-I was 83.9%, at 5-year risk thresholds of 0.68% and 1.17%, respectively, 7.9% and 6.2% higher than the USPSTF-2021 criteria at the same specificity. The main limitation of this study is that the models have not been validated outside of UK and US cohorts.

CONCLUSIONS

We present parsimonious ensemble machine learning models to predict the risk of lung cancer in ever-smokers, demonstrating a novel approach that could simplify the implementation of risk-based lung cancer screening in multiple settings.

Artificial Intelligence in Lung Cancer Screening: The Future Is Now

Lung cancer has one of the worst morbidity and fatality rates of any malignant tumour. Most lung cancers are discovered in the middle and late stages of the disease, when treatment choices are limited, and patients' survival rate is low. The aim of lung cancer screening is the identification of lung malignancies in the early stage of the disease, when more options for effective treatments are available, to improve the patients' outcomes. The desire to improve the efficacy and efficiency of clinical care continues to drive multiple innovations into practice for better patient management, and in this context, artificial intelligence (AI) plays a key role. AI may have a role in each process of the lung cancer screening workflow. First, in the acquisition of low-dose computed tomography for screening programs, AI-based reconstruction allows a further dose reduction, while still maintaining an optimal image quality. AI can help the personalization of screening programs through risk stratification based on the collection and analysis of a huge amount of imaging and clinical data. A computer-aided detection (CAD) system provides automatic detection of potential lung nodules with high sensitivity, working as a concurrent or second reader and reducing the time needed for image interpretation. Once a nodule has been detected, it should be characterized as benign or malignant. Two AI-based approaches are available to perform this task: the first one is represented by automatic segmentation with a consequent assessment of the lesion size, volume, and densitometric features; the second consists of segmentation first, followed by radiomic features extraction to characterize the whole abnormalities providing the so-called "virtual biopsy". This narrative review aims to provide an overview of all possible AI applications in lung cancer screening.

Lung Cancer Screening with Low-Dose CT: What We Have Learned in Two Decades of ITALUNG and What Is Yet to Be Addressed

The ITALUNG trial started in 2004 and compared lung cancer (LC) and other-causes mortality in 55-69 years-aged smokers and ex-smokers who were randomized to four annual chest low-dose CT (LDCT) or usual care. ITALUNG showed a lower LC and cardiovascular mortality in the screened subjects after 13 years of follow-up, especially in women, and produced many ancillary studies. They included recruitment results of a population-based mimicking approach, development of software for computer-aided diagnosis (CAD) and lung nodules volumetry, LDCT assessment of pulmonary emphysema and coronary artery calcifications (CAC) and their relevance to long-term mortality, results of a smoking-cessation intervention, assessment of the radiations dose associated with screening LDCT, and the results of biomarkers assays. Moreover, ITALUNG data indicated that screen-detected LCs are mostly already present at baseline LDCT, can present as lung cancer associated with cystic airspaces, and can be multiple. However, several issues of LC screening are still unaddressed. They include the annual vs. biennial pace of LDCT, choice between opportunistic or population-based recruitment. and between uni or multi-centre screening, implementation of CAD-assisted reading, containment of false positive and negative LDCT results, incorporation of emphysema. and CAC quantification in models of personalized LC and mortality risk, validation of ultra-LDCT acquisitions, optimization of the smoking-cessation intervention. and prospective validation of the biomarkers.

National lung cancer screening program feasibility study in Estonia

OBJECTIVES

The main aim of the lung cancer screening (LCS) feasibility study was to investigate the plausibility of and bottlenecks to systematic enrolment in family physician practices by evaluating all their patients.

METHODS

In 3 family physician practices, for each individual born in 1947-1966 (target age group 55-74 years), information on ever smoking was gathered by a family physician/nurse. All current and ex-smokers were invited to an 'LCS visit'. In parallel, 2 inclusion criteria were used: (1) current smoker (≥20 pack-years) or ex-smoker (quit <15 years ago and smoking history ≥20 pack-years) and (2) PLCOm2012noRace risk score >1.5. All individuals with elevated lung cancer risk were assigned low-dose computed tomography.

RESULTS

Among the total 7035 individuals in the 3 family physician practices, the LCS target age group comprised 1208 individuals, including 649 (46.3-57.1%) males and 559 (42.9-53.7%) females. Of the 1208 applicable age group individuals, 395 (all current or ex-smokers) were invited to the 'LCS visit'. According to either 1 or both the LCS inclusion criteria, 206 individuals were referred to low-dose computed tomography, and 201 (97.6% of those referred) ended up taking it. The estimated participation rate in LCS, based on data from our feasibility study, would have been 87.4%.

CONCLUSIONS

In LCS, systematic enrolment of individuals by family physicians results in high uptake, and thus, effectiveness of the LCS in the setting of a well-functioning family physician system like in Estonia. Also, the feasibility study provided excellent input to the currently ongoing regional LCS pilot study in Estonia.

Establishing a Pulmonary Nodule Clinic Service for Early Diagnosis of Lung Cancer - Review of International Options and Considerations for Greece

Early diagnosis of lung cancer in pulmonary nodules identified by computed tomography (CT) may be critical in reducing the epidemiological burden of the disease, particularly in countries where such a burden is considerably high and risk factors for lung cancer very prevalent. The establishment and operation of pulmonary nodule clinics (PNCs), ie, multidisciplinary services that watch and evaluate nodules found through deliberate screening efforts or as incidental findings, is increasingly becoming a key tool to implement such early-intervention, cancer-risk management policies elsewhere in the world. This review aims to research and present in a structured manner findings from published sources on options and considerations for setting up a PNC in a country such as Greece. These refer to the type of services a PNC would provide to optimize diagnosis of suspect pulmonary nodules, its structure and organization, including processes, human resources and technology infrastructure, its target audience, ie, who would be eligible to use its services, and the expected outcomes of its operation, in terms of a set of key performance indicators. Our review also revealed critical key success factors that should be considered when designing the introduction of a PNC in a health care setting, including optimal referral pathways, aligned clinical decision making and patient preferences and participation/empowerment. Our findings may inform health care systems with a high lung cancer burden and no available PNC service on options and considerations before introducing such a service in their respective settings.

GM, Sasidharan B, Thangakunam B, Pavamani S, Isiah R, Joel A, Bhat TA. Imaging Recommendations for Diagnosis, Staging, and Management of Lung Cancer

Globally and in India, lung cancer is one of the leading malignancies in terms of incidence and mortality. Smoking and environmental pollution are the common risk factors for developing lung cancer. Traditionally, lung cancer is divided into small cell and nonsmall cell types, with nonsmall cell carcinomas including squamous cell carcinoma, adenocarcinoma, and large cell carcinoma.

In this review article, we describe the imaging recommendations and findings in the diagnosis, staging, and management of lung cancer, including the imaging of treatment-related complications.

Artificial intelligence in lung cancer: current applications and perspectives

Artificial intelligence (AI) has been a very active research topic over the last years and thoracic imaging has particularly benefited from the development of AI and in particular deep learning. We have now entered a phase of adopting AI into clinical practice. The objective of this article was to review the current applications and perspectives of AI in thoracic oncology. For pulmonary nodule detection, computer-aided detection (CADe) tools have been commercially available since the early 2000s. The more recent rise of deep learning and the availability of large annotated lung nodule datasets have allowed the development of new CADe tools with fewer false-positive results per examination. Classical machine learning and deep-learning methods were also used for pulmonary nodule segmentation allowing nodule volumetry and pulmonary nodule characterization. For pulmonary nodule characterization, radiomics and deep-learning approaches were used. Data from the National Lung Cancer Screening Trial (NLST) allowed the development of several computer-aided diagnostic (CADx) tools for diagnosing lung cancer on chest computed tomography. Finally, AI has been used as a means to perform virtual biopsies and to predict response to treatment or survival. Thus, many detection, characterization and stratification tools have been proposed, some of which are commercially available.

European Groundshot-addressing Europe's cancer research challenges: a Lancet Oncology Commission

Cancer research is a crucial pillar for countries to deliver more affordable, higher quality, and more equitable cancer care. Patients treated in research-active hospitals have better outcomes than patients who are not treated in these settings. However, cancer in Europe is at a crossroads. Cancer was already a leading cause of premature death before the COVID-19 pandemic, and the disastrous effects of the pandemic on early diagnosis and treatment will probably set back cancer outcomes in Europe by almost a decade. Recognising the pivotal importance of research not just to mitigate the pandemic today, but to build better European cancer services and systems for patients tomorrow, the Lancet Oncology European Groundshot Commission on cancer research brings together a wide range of experts, together with detailed new data on cancer research activity across Europe during the past 12 years. We have deployed this knowledge to help inform Europe's Beating Cancer Plan and the EU Cancer Mission, and to set out an evidence-driven, patient-centred cancer research roadmap for Europe. The high-resolution cancer research data we have generated show current activities, captured through different metrics, including by region, disease burden, research domain, and effect on outcomes. We have also included granular data on research collaboration, gender of researchers, and research funding. The inclusion of granular data has facilitated the identification of areas that are perhaps overemphasised in current cancer research in Europe, while also highlighting domains that are underserved. Our detailed data emphasise the need for more information-driven and data-driven cancer research strategies and planning going forward. A particular focus must be on central and eastern Europe, because our findings emphasise the widening gap in cancer research activity, and capacity and outcomes, compared with the rest of Europe. Citizens and patients, no matter where they are, must benefit from advances in cancer research. This Commission also highlights that the narrow focus on discovery science and biopharmaceutical research in Europe needs to be widened to include such areas as prevention and early diagnosis; treatment modalities such as radiotherapy and surgery; and a larger concentration on developing a research and innovation strategy for the 20 million Europeans living beyond a cancer diagnosis. Our data highlight the important role of comprehensive cancer centres in driving the European cancer research agenda. Crucial to a functioning cancer research strategy and its translation into patient benefit is the need for a greater emphasis on health policy and systems research, including implementation science, so that the innovative technological outputs from cancer research have a clear pathway to delivery. This European cancer research Commission has identified 12 key recommendations within a call to action to reimagine cancer research and its implementation in Europe. We hope this call to action will help to achieve our ambitious 70:35 target: 70% average 10-year survival for all European cancer patients by 2035.

Quantitative Proteomics Indicate Radical Removal of Non-Small Cell Lung Cancer and Predict Outcome

Non-small cell lung cancer (NSCLC) is associated with low survival rates, often due to late diagnosis and lack of personalized medicine. Diagnosing and monitoring NSCLC using blood samples has lately gained interest due to its less invasive nature. In the present study, plasma was collected at three timepoints and analyzed using proximity extension assay technology and quantitative real-time polymerase chain reaction in patients with primary NSCLC stages IA-IIIA undergoing surgery. Results were adjusted for patient demographics, tumor, node, metastasis (TNM) stage, and multiple testing. Major histocompatibility (MHC) class 1 polypeptide-related sequence A/B (MIC-A/B) and tumor necrosis factor ligand superfamily member 6 (FASLG) were significantly increased post-surgery, suggesting radical removal of cancerous cells. Levels of hepatocyte growth factor (HGF) initially increased postoperatively but were later lowered, potentially indicating radical removal of malignant cells. The levels of FASLG in patients who later died or had a relapse of NSCLC were lower at all three timepoints compared to surviving patients without relapse, indicating that FASLG may be used as a prognostic biomarker. The biomarkers were confirmed using microarray data. In conclusion, quantitative proteomics could be used for NSCLC identification but may also provide information on radical surgical removal of NSCLC and post-surgical prognosis.

Innovations in thoracic imaging: CT, radiomics, AI and x-ray velocimetry

In recent years, pulmonary imaging has seen enormous progress, with the introduction, validation and implementation of new hardware and software. There is a general trend from mere visual evaluation of radiological images to quantification of abnormalities and biomarkers, and assessment of ‘non visual’ markers that contribute to establishing diagnosis or prognosis. Important catalysts to these developments in thoracic imaging include new indications (like computed tomography [CT] lung cancer screening) and the COVID‐19 pandemic. This review focuses on developments in CT, radiomics, artificial intelligence (AI) and x‐ray velocimetry for imaging of the lungs. Recent developments in CT include the potential for ultra‐low‐dose CT imaging for lung nodules, and the advent of a new generation of CT systems based on photon‐counting detector technology. Radiomics has demonstrated potential towards predictive and prognostic tasks particularly in lung cancer, previously not achievable by visual inspection by radiologists, exploiting high dimensional patterns (mostly texture related) on medical imaging data. Deep learning technology has revolutionized the field of AI and as a result, performance of AI algorithms is approaching human performance for an increasing number of specific tasks. X‐ray velocimetry integrates x‐ray (fluoroscopic) imaging with unique image processing to produce quantitative four dimensional measurement of lung tissue motion, and accurate calculations of lung ventilation.

See relatedEditorial

Impact of low-dose computed tomography (LDCT) screening on lung cancer-related mortality

BACKGROUND

Lung cancer is the most common cause of cancer-related death in the world, however lung cancer screening has not been implemented in most countries at a population level. A previous Cochrane Review found limited evidence for the effectiveness of lung cancer screening with chest radiography (CXR) or sputum cytology in reducing lung cancer-related mortality, however there has been increasing evidence supporting screening with low-dose computed tomography (LDCT).  OBJECTIVES: To determine whether screening for lung cancer using LDCT of the chest reduces lung cancer-related mortality and to evaluate the possible harms of LDCT screening.

SEARCH METHODS

We performed the search in collaboration with the Information Specialist of the Cochrane Lung Cancer Group and included the Cochrane Lung Cancer Group Trial Register, Cochrane Central Register of Controlled Trials (CENTRAL, the Cochrane Library, current issue), MEDLINE (accessed via PubMed) and Embase in our search. We also searched the clinical trial registries to identify unpublished and ongoing trials. We did not impose any restriction on language of publication. The search was performed up to 31 July 2021.  SELECTION CRITERIA: Randomised controlled trials (RCTs) of lung cancer screening using LDCT and reporting mortality or harm outcomes.  DATA COLLECTION AND ANALYSIS: Two review authors were involved in independently assessing trials for eligibility, extraction of trial data and characteristics, and assessing risk of bias of the included trials using the Cochrane RoB 1 tool. We assessed the certainty of evidence using GRADE. Primary outcomes were lung cancer-related mortality and harms of screening. We performed a meta-analysis, where appropriate, for all outcomes using a random-effects model. We only included trials in the analysis of mortality outcomes if they had at least 5 years of follow-up. We reported risk ratios (RRs) and hazard ratios (HRs), with 95% confidence intervals (CIs) and used the I2 statistic to investigate heterogeneity.  MAIN RESULTS: We included 11 trials in this review with a total of 94,445 participants. Trials were conducted in Europe and the USA in people aged 40 years or older, with most trials having an entry requirement of ≥ 20 pack-year smoking history (e.g. 1 pack of cigarettes/day for 20 years or 2 packs/day for 10 years etc.). One trial included male participants only. Eight trials were phase three RCTs, with two feasibility RCTs and one pilot RCT. Seven of the included trials had no screening as a comparison, and four trials had CXR screening as a comparator. Screening frequency included annual, biennial and incrementing intervals. The duration of screening ranged from 1 year to 10 years. Mortality follow-up was from 5 years to approximately 12 years.  None of the included trials were at low risk of bias across all domains. The certainty of evidence was moderate to low across different outcomes, as assessed by GRADE. In the meta-analysis of trials assessing lung cancer-related mortality, we included eight trials (91,122 participants), and there was a reduction in mortality of 21% with LDCT screening compared to control groups of no screening or CXR screening (RR 0.79, 95% CI 0.72 to 0.87; 8 trials, 91,122 participants; moderate-certainty evidence). There were probably no differences in subgroups for analyses by control type, sex, geographical region, and nodule management algorithm. Females appeared to have a larger lung cancer-related mortality benefit compared to males with LDCT screening. There was also a reduction in all-cause mortality (including lung cancer-related) of 5% (RR 0.95, 95% CI 0.91 to 0.99; 8 trials, 91,107 participants; moderate-certainty evidence).  Invasive tests occurred more frequently in the LDCT group (RR 2.60, 95% CI 2.41 to 2.80; 3 trials, 60,003 participants; moderate-certainty evidence). However, analysis of 60-day postoperative mortality was not significant between groups (RR 0.68, 95% CI 0.24 to 1.94; 2 trials, 409 participants; moderate-certainty evidence).  False-positive results and recall rates were higher with LDCT screening compared to screening with CXR, however there was low-certainty evidence in the meta-analyses due to heterogeneity and risk of bias concerns. Estimated overdiagnosis with LDCT screening was 18%, however the 95% CI was 0 to 36% (risk difference (RD) 0.18, 95% CI -0.00 to 0.36; 5 trials, 28,656 participants; low-certainty evidence). Four trials compared different aspects of health-related quality of life (HRQoL) using various measures. Anxiety was pooled from three trials, with participants in LDCT screening reporting lower anxiety scores than in the control group (standardised mean difference (SMD) -0.43, 95% CI -0.59 to -0.27; 3 trials, 8153 participants; low-certainty evidence). There were insufficient data to comment on the impact of LDCT screening on smoking behaviour.  AUTHORS' CONCLUSIONS: The current evidence supports a reduction in lung cancer-related mortality with the use of LDCT for lung cancer screening in high-risk populations (those over the age of 40 with a significant smoking exposure). However, there are limited data on harms and further trials are required to determine participant selection and optimal frequency and duration of screening, with potential for significant overdiagnosis of lung cancer. Trials are ongoing for lung cancer screening in non-smokers.

Low-dose CT for lung cancer screening: position paper from the Italian college of thoracic radiology

Smoking is the main risk factor for lung cancer (LC), which is the leading cause of cancer-related death worldwide. Independent randomized controlled trials, governmental and inter-governmental task forces, and meta-analyses established that LC screening (LCS) with chest low dose computed tomography (LDCT) decreases the mortality of LC in smokers and former smokers, compared to no-screening, especially in women. Accordingly, several Italian initiatives are offering LCS by LDCT and smoking cessation to about 10,000 high-risk subjects, supported by Private or Public Health Institutions, envisaging a possible population-based screening program. Because LDCT is the backbone of LCS, Italian radiologists with LCS expertise are presenting this position paper that encompasses recommendations for LDCT scan protocol and its reading. Moreover, fundamentals for classification of lung nodules and other findings at LDCT test are detailed along with international guidelines, from the European Society of Thoracic Imaging, the British Thoracic Society, and the American College of Radiology, for their reporting and management in LCS. The Italian College of Thoracic Radiologists produced this document to provide the basics for radiologists who plan to set up or to be involved in LCS, thus fostering homogenous evidence-based approach to the LDCT test over the Italian territory and warrant comparison and analyses throughout National and International practices.

European lung cancer screening: valuable trial evidence for optimal practice implementation

Lung cancer screening (LCS) by low-dose computed tomography is a strategy for secondary prevention of lung cancer. In the last two decades, LCS trials showed several options to practice secondary prevention in association with primary prevention, however, the translation from trial to practice is everything but simple. In 2020, the European Society of Radiology and European Respiratory Society published their joint statement paper on LCS. This commentary aims to provide the readership with detailed description about hurdles and potential solutions that could be encountered in the practice of LCS.

Implementing lung cancer screening in Europe: taking a systems approach

Lung cancer is the leading cause of cancer death in Europe. Screening by means of low-dose computed tomography (LDCT) can shift detection to an earlier stage and reduce lung cancer mortality in high-risk individuals. However, to date, Poland, Croatia, Italy, and Romania are the only European countries to commit to large-scale implementation of targeted LDCT screening. Using a health systems approach, this article evaluates key factors needed to enable the successful implementation of screening programs across Europe. Recent literature on LDCT screening was reviewed for 10 countries (Belgium, Croatia, France, Germany, Italy, the Netherlands, Poland, Spain, Sweden, and United Kingdom) and complemented by 17 semistructured interviews with local experts. Research findings were mapped against a health systems framework adapted for lung cancer screening. The European policy landscape is highly variable, but potential barriers to implementation are similar across countries and consistent with those reported for other cancer screening programs. While consistent quality and safety of screening must be ensured across all screening centers, system factors are also important. These include appropriate data infrastructure, targeted recruitment methods that ensure equity in participation, sufficient capacity and workforce training, full integration of screening with multidisciplinary care pathways, and smoking cessation programs. Stigma and underlying perceptions of lung cancer as a self-inflicted condition are also important considerations. Building on decades of implementation research, governments now have a unique opportunity to establish effective, efficient, and equitable lung cancer screening programs adapted to their health systems, curbing the impact of lung cancer on their populations.

Early detection of cancer

Survival improves when cancer is detected early. However, ~50% of cancers are at an advanced stage when diagnosed. Early detection of cancer or precancerous change allows early intervention to try to slow or prevent cancer development and lethality. To achieve early detection of all cancers, numerous challenges must be overcome. It is vital to better understand who is at greatest risk of developing cancer. We also need to elucidate the biology and trajectory of precancer and early cancer to identify consequential disease that requires intervention. Insights must be translated into sensitive and specific early detection technologies and be appropriately evaluated to support practical clinical implementation. Interdisciplinary collaboration is key; advances in technology and biological understanding highlight that it is time to accelerate early detection research and transform cancer survival.

Outstanding negative prediction performance of solid pulmonary nodule volume AI for ultra-LDCT baseline lung cancer screening risk stratification

OBJECTIVE

To evaluate performance of AI as a standalone reader in ultra-low-dose CT lung cancer baseline screening, and compare it to that of experienced radiologists.

METHODS

283 participants who underwent a baseline ultra-LDCT scan in Moscow Lung Cancer Screening, between February 2017-2018, and had at least one solid lung nodule, were included. Volumetric nodule measurements were performed by five experienced blinded radiologists, and independently assessed using an AI lung cancer screening prototype (AVIEW LCS, v1.0.34, Coreline Soft, Co. ltd, Seoul, Korea) to automatically detect, measure, and classify solid nodules. Discrepancies were stratified into two groups: positive-misclassification (PM); nodule classified by the reader as a NELSON-plus /EUPS-indeterminate/positive nodule, which at the reference consensus read was < 100 mm3, and negative-misclassification (NM); nodule classified as a NELSON-plus /EUPS-negative nodule, which at consensus read was ≥ 100 mm3.

RESULTS

1149 nodules with a solid-component were detected, of which 878 were classified as solid nodules. For the largest solid nodule per participant (n = 283); 61 [21.6 %; 53 PM, 8 NM] discrepancies were reported for AI as a standalone reader, compared to 43 [15.1 %; 22 PM, 21 NM], 36 [12.7 %; 25 PM, 11 NM], 29 [10.2 %; 25 PM, 4 NM], 28 [9.9 %; 6 PM, 22 NM], and 50 [17.7 %; 15 PM, 35 NM] discrepancies for readers 1, 2, 3, 4, and 5 respectively.

CONCLUSION

Our results suggest that through the use of AI as an impartial reader in baseline lung cancer screening, negative-misclassification results could exceed that of four out of five experienced radiologists, and radiologists' workload could be drastically diminished by up to 86.7%.