Evaluation of risk prediction models to select lung cancer screening participants in Europe: a prospective cohort consortium analysis.

BACKGROUND: Lung cancer risk prediction models might efficiently identify individuals who should be offered lung cancer screening. However, their performance has not been comprehensively evaluated in Europe. We aimed to externally validate and evaluate the performance of several risk prediction models that predict lung cancer incidence or mortality in prospective European cohorts. METHODS: We analysed 240 137 participants aged 45-80 years with a current or former smoking history from nine European countries in four prospective cohorts from the pooled database of the Lung Cancer Cohort Consortium: the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study (Finland), the Nord-Trøndelag Health Study (Norway), CONSTANCES (France), and the European Prospective Investigation into Cancer and Nutrition (Denmark, Germany, Italy, Spain, Sweden, the Netherlands, and Norway). We evaluated ten lung cancer risk models, which comprised the Bach, the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial 2012 model (PLCOm2012), the Lung Cancer Risk Assessment Tool (LCRAT), the Lung Cancer Death Risk Assessment Tool (LCDRAT), the Nord-Trøndelag Health Study (HUNT), the Optimized Early Warning Model for Lung Cancer Risk (OWL), the University College London-Death (UCLD), the University College London-Incidence (UCLI), the Liverpool Lung Project version 2 (LLP version 2), and the Liverpool Lung Project version 3 (LLP version 3) models. We quantified model calibration as the ratio of expected to observed cases or deaths and discrimination using the area under the receiver operating characteristic curve (AUC). For each model, we also identified risk thresholds that would screen the same number of individuals as each of the US Preventive Services Task Force 2021 (USPSTF-2021), the US Preventive Services Task Force 2013 (USPSTF-2013), and the Nederlands-Leuvens Longkanker Screenings Onderzoek (NELSON) criteria. FINDINGS: Among the participants, 1734 lung cancer cases and 1072 lung cancer deaths occurred within five years of enrolment. Most models had reasonable calibration in most countries, although the LLP version 2 overpredicted risk by more than 50% in eight countries (expected to observed ≥1·50). The PLCOm2012, LCDRAT, LCRAT, Bach, HUNT, OWL, UCLD, and UCLI models showed similar discrimination in most countries, with AUCs ranging from 0·68 (95% CI 0·59-0·77) to 0·83 (0·78-0·89), whereas the LLP version 2 and LLP version 3 showed lower discrimination, with AUCs ranging from 0·64 (95% CI 0·57-0·72) to 0·78 (0·74-0·83). When pooling data from all countries (but excluding the HUNT cohort), 33·9% (73 313 of 216 387) of individuals were eligible by USPSTF-2021 criteria, which included 74·8% (1185) of lung cancers and 76·3% (730) of lung cancer deaths occurring over 5 years. Fewer individuals were selected by USPSTF-2013 and NELSON criteria. After applying thresholds to select a population of equal size to USPSTF-2021, the PLCOm2012, LCDRAT, LCRAT, Bach, HUNT, OWL, UCLD, and UCLI, models identified 77·6%-79·1% of future cases, although they selected slightly older individuals compared with USPSTF-2021 criteria. Results were similar for USPSTF-2013 and NELSON. INTERPRETATION: Several lung cancer risk prediction models showed good performance in European countries and might improve the efficiency of lung cancer screening if used in place of categorical eligibility criteria. FUNDING: US National Cancer Institute, l'Institut National du Cancer, Cancer Research UK.

What Is the Accuracy of Clinical Staging for Stage III-Single-station N2 NSCLC? A Multi-Centre UK Study.

Introduction: Single-station N2 (ssN2) versus multi-station N2 has been used as a selection criterion for treatment recommendations between surgical versus non-surgical multimodality treatment in stage III-N2 NSCLC. We hypothesized that clinical staging would be susceptible to upstaging on pathologic staging and, therefore, challenge this practice. Methods: A retrospective study of prospectively collected routine clinical data for patients with stage III-N2 NSCLC that had completed computed tomography (CT), positron emission tomography (PET), and staging endobronchial ultrasound (EBUS) and had been confirmed clinical stage III-ssN2 at multidisciplinary team discussion and went on to complete surgical resection as the first treatment to provide pathologic staging. The study was completed in two cohorts (A) across a single cancer alliance in England (Greater Manchester) January 1, 2015 to December 31, 2018 and (B) across five United Kingdom centers to validate the findings in part A January 1, 2016 to December 31, 2020. Results: A total of 115 patients met the inclusion criteria across cohort A (56 patients) and cohort B (59 patients) across 15 United Kingdom hospitals. The proportion of cases in which clinical stage III-ssN2 was upstaged to pathologic stage III-multi-station N2 was 34% (19 of 56) in cohort A, 32% in cohort B (19 of 59), and 33% across the combined study cohort (38 of 115). Most patients had a single radiologically abnormal lymph node on CT and PET (88%, 105 of 115). In the majority, the reasons for missed N2 disease on staging EBUS were due to inaccessible (stations 5, 6, 8, 9) N2 nodes at EBUS (34%, 13 of 38) and accessible lymph nodes not sampled during staging EBUS as not meeting sampling threshold (40%, 15 of 38) rather than false-negative sampling during EBUS (26%, 10 of 38). Conclusions: During multidisciplinary team discussions, clinicians must be aware that one-third of patients with stage III-ssN2 on the basis of CT, PET, and staging EBUS do not truly have ssN2 and this questions the use of this criterion to define treatment recommendations.

When to reinvite initially ineligible populations for targeted lung cancer screening?

INTRODUCTION: Targeted low-dose CT lung cancer screening reduces lung cancer mortality. England's Targeted Lung Health Check programme uses risk prediction tools to determine eligibility for biennial screening among people with a smoking history aged 55-74. Some participants initially ineligible for lung cancer screening will later become eligible with increasing age and ongoing tobacco exposure. It is, therefore, important to understand how many people could qualify for reinvitation, and after how long, to inform implementation of services. METHODS: We prospectively predicted future risk (using Prostate, Lung, Colorectal and Ovarian trial's risk model (PLCOm2012) and Liverpool Lung Project version 2 (LLPv2) risk models) and time-to-eligibility of 5345 participants to estimate how many would become eligible through the course of a Lung Health Check screening programme for 55-74 years. RESULTS: Approximately a quarter eventually become eligible, with those with the lowest baseline risks unlikely to ever become eligible. Time-to-eligibility is shorter for participants with higher baseline risk, increasing age and ongoing smoking status. At a PLCOm2012 threshold ≥1.51%, 68% of those who continue to smoke become eligible compared with 18% of those who have quit. DISCUSSION: Predicting which participants may become eligible, and when, during a screening programme can help inform reinvitation strategies and service planning. Those with risk scores closer to the eligibility threshold, particularly people who continue to smoke, will reach eligibility in subsequent rounds while those at the lowest risk may be discharged from the programme from the outset.

Validated machine learning tools to distinguish immune checkpoint inhibitor, radiotherapy, COVID-19 and other infective pneumonitis.

BACKGROUND: Pneumonitis is a well-described, potentially disabling, or fatal adverse effect associated with both immune checkpoint inhibitors (ICI) and thoracic radiotherapy. Accurate differentiation between checkpoint inhibitor pneumonitis (CIP) radiation pneumonitis (RP), and infective pneumonitis (IP) is crucial for swift, appropriate, and tailored management to achieve optimal patient outcomes. However, correct diagnosis is often challenging, owing to overlapping clinical presentations and radiological patterns. METHODS: In this multi-centre study of 455 patients, we used machine learning with radiomic features extracted from chest CT imaging to develop and validate five models to distinguish CIP and RP from COVID-19, non-COVID-19 infective pneumonitis, and each other. Model performance was compared to that of two radiologists. RESULTS: Models to distinguish RP from COVID-19, CIP from COVID-19 and CIP from non-COVID-19 IP out-performed radiologists (test set AUCs of 0.92 vs 0.8 and 0.8; 0.68 vs 0.43 and 0.4; 0.71 vs 0.55 and 0.63 respectively). Models to distinguish RP from non-COVID-19 IP and CIP from RP were not superior to radiologists but demonstrated modest performance, with test set AUCs of 0.81 and 0.8 respectively. The CIP vs RP model performed less well on patients with prior exposure to both ICI and radiotherapy (AUC 0.54), though the radiologists also had difficulty distinguishing this test cohort (AUC values 0.6 and 0.6). CONCLUSION: Our results demonstrate the potential utility of such tools as a second or concurrent reader to support oncologists, radiologists, and chest physicians in cases of diagnostic uncertainty. Further research is required for patients with exposure to both ICI and thoracic radiotherapy.

Invitation strategies and participation in a community-based lung cancer screening programme located in areas of high socioeconomic deprivation.

INTRODUCTION: Although lung cancer screening is being implemented in the UK, there is uncertainty about the optimal invitation strategy. Here, we report participation in a community screening programme following a population-based invitation approach, examine factors associated with participation, and compare outcomes with hypothetical targeted invitations. METHODS: Letters were sent to all individuals (age 55-80) registered with a general practice (n=35 practices) in North and East Manchester, inviting ever-smokers to attend a Lung Health Check (LHC). Attendees at higher risk (PLCOm2012NoRace score≥1.5%) were offered two rounds of annual low-dose CT screening. Primary care recorded smoking codes (live and historical) were used to model hypothetical targeted invitation approaches for comparison. RESULTS: Letters were sent to 35 899 individuals, 71% from the most socioeconomically deprived quintile. Estimated response rate in ever-smokers was 49%; a lower response rate was associated with younger age, male sex, and primary care recorded current smoking status (adjOR 0.55 (95% CI 0.52 to 0.58), p<0.001). 83% of eligible respondents attended an LHC (n=8887/10 708). 51% were eligible for screening (n=4540/8887) of whom 98% had a baseline scan (n=4468/4540). Screening adherence was 83% (n=3488/4199) and lung cancer detection 3.2% (n=144) over 2 rounds. Modelled targeted approaches required 32%-48% fewer invitations, identified 94.6%-99.3% individuals eligible for screening, and included 97.1%-98.6% of screen-detected lung cancers. DISCUSSION: Using a population-based invitation strategy, in an area of high socioeconomic deprivation, is effective and may increase screening accessibility. Due to limitations in primary care records, targeted approaches should incorporate historical smoking codes and individuals with absent smoking records.