Biological aging occurs heterogeneously across individuals and organs. However, current measures of biological age incompletely capture organ-specific differences in health and disease risk. Because chest CT visualizes multiple thoracic organs, it offers an opportunity to quantify structural aging across organ systems. Here, we developed MOSAIC-Age, a framework characterizing eight organ-specific aging clocks on chest CT. The clocks were developed and validated using 9,971 CT scans from CT-RATE and MIDRC, and subsequently locked and applied to two independent prospective cohorts with 35,293 participants from the National Lung Screening Trial and Genetic Epidemiology of COPD study. CT-derived biological age gaps (BAGs) were examined in relation to lifestyle and socioeconomic factors, prevalent comorbidities, incident chronic diseases, and all-cause and cause-specific mortality. Higher BAGs, indicating organs that appeared older on CT than expected for their chronological age, were broadly associated with adverse health characteristics, chronic disease burden, and increased mortality risk. Multiple disease outcomes were associated with aging across several organs, whereas in multivariable analyses including all eight organ-specific BAGs, the remaining associations were more organ specific. A greater number of markedly older-appearing organs and a faster pace of aging were each associated with higher mortality. Together, these findings demonstrate that routine chest CT captures both shared and organ-specific patterns of biological aging and establish CT-derived organ aging as a quantitative imaging biomarker for assessing multi-organ health and long-term disease risk. ### Competing Interest Statement The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: N. I. V. receives consulting fees from Regeneron, Amgen, Xencor, AstraZeneca, Tempus, Pfizer, Summit, OncoHost, Guardant, ImmunityBio, and research funding from EMD Serono, IDEAYA, Amgen, Summit, Regeneron, Sanofi, BMS, and OncoHost, outside the submitted work. M.C.B.G. has received research funding from Siemens Healthcare. T. C. reports speaker fees/honoraria (including travel/meeting expenses) from ASCO Post, AstraZeneca, Bio Ascend, Bristol Myers Squibb, Clinical Care Options, IDEOlogy Health, Medical Educator Consortium, Medscape, OncLive, PeerView, Physicians' Education Resource, Targeted Oncology; advisory role/consulting fees (including travel/meeting expenses) from AstraZeneca, Bristol Myers Squibb, Daiichi Sankyo, Genentech, Johnson & Johnson, Merck, Nuvalent, oNKo-innate, Pfizer, and RAPT Therapeutics; and institutional research funding from AstraZeneca, Bristol Myers Squibb and Merck. X. L. reports receiving consultant and advisory fees from Eli Lilly, AstraZeneca, EMD Serono, Daiichi Sankyo, Spectrum Therapeutics, Boehringer Ingelheim, Hengrui Therapeutics, Novartis, and research funding from Eli Lilly, Boehringer Ingelheim, all outside of the submitted work. M. A. reports research funding from Genentech, Nektar Therapeutics, Merck, GlaxoSmithKline, Novartis, Jounce Therapeutics, Bristol Myers Squibb, Eli Lilly, Adaptimmune, Shattuck Labs, Gilead, Verismo Therapeutics, and Lyell; advisory board roles for GlaxoSmithKline, Shattuck Labs, Bristol Myers Squibb, AstraZeneca, Insightec, Regeneron, Genprex, and Lyell; speaker fees from AstraZeneca, Nektar Therapeutics, SITC, and Regeneron; and participation on a safety review committee for Nanobiotix-MDA Alliance, Henlius, all outside of the submitted work. A.A.S. reports serving on the advisory board of DELFI Diagnostics and as an advisor to Droplet Biosciences, all outside of the submitted work. D.E.G. reports research funding from AstraZeneca, Karyopharm, and Novocure; stock ownership in Gilead and Medtronic; stock options in Early Marker, Inc. and OncoSeer Diagnostics, Inc.; consulting and advisory roles for AbbVie, AstraZeneca, Bayer, Catalyst Pharmaceuticals, EMD Serono, and GSK; service on data and safety monitoring boards for Daiichi Sankyo, Summit Therapeutics, and Taiho Oncology; royalties from Oxford University Press; and roles as co-founder and Chief Medical Officer of OncoSeer Diagnostics, Inc., all outside of the submitted work. J. Y. C. has received travel sponsorship from Accuray and Varian Medical Systems, and grants from Varian Medical Systems, outside the submitted work. D. L. G. reports honoraria for scientific advisory boards from AstraZeneca, Sanofi, Alethia Biotherapeutics, Menarini, Eli Lilly, 4D Pharma and Onconova, and research support from Janssen, Takeda, Astellas, Ribon Therapeutics, NGM Biopharmaceuticals, Boehringer Ingelheim, Mirati Therapeutics and AstraZeneca, all outside of the submitted work. C. C. W. reports research support from Medical Imaging and Data Resource Center from NIBIB/University of Chicago and royalties from Elsevier, outside of the submitted work. J. V. H. reports receiving advisory/consulting fees from AstraZeneca, Boehringer Ingeheim, Catalyst, Genentech, GlaxoSmithKline, Guardant Health, Foundation Medicine, Hengrui Therapeutics, Eli Lilly, Novartis, Spectrum, Sanofi, Takeda Pharmaceuticals, Mirati Therapeutics, Bristiol Myers Squibb, BrightPath Biotherapeutics, Janssen Global Services, Nexus Health Systems, EMD Serono, Pneuma Respiratory, Kairos Venture Investments, Leads Biolabs, RefleXion, and research funding from GlaxoSmithKline, AstraZeneca, Spectrum, all outside of the submitted work. J. Z. reports grants from Merck, Novartis, Johnson and Johnson; and personal fees from BMS, AZ, Novartis, Johnson and Johnson, GenePlus, Hengrui, Innovent, outside the submitted work. J. W. reports research funding from Siemens Healthcare. The remaining authors declare that they have no competing interests. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Institutional Review Board of The University of Texas MD Anderson Cancer Center gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes CT-RATE imaging data are available through the Hugging Face repository under the repository's access conditions for academic, research, and educational use (https://huggingface.co/datasets/ibrahimhamamci/CT-RATE). MIDRC imaging data are available through the MIDRC Data Commons to registered users under the applicable MIDRC data use agreement (https://data.midrc.org/). The NLST imaging data are available through The Cancer Imaging Archive (https://www.cancerimagingarchive.net/collection/nlst/), and NLST clinical data are available through the NCI Cancer Data Access System (https://cdas.cancer.gov/nlst/). The COPDGene phenotype and imaging data used in this study are available through controlled access via dbGaP under study accession numbers phs000179.v7.p2 and phs004023.v1.p2, respectively. The paired CT/PET-CT data are not publicly available owing to patient privacy considerations but are available for research purposes from the corresponding author upon reasonable request. The pretrained organ-clock model weights are also available from the corresponding author upon reasonable request. National Institutes of Health, R01CA262425, R01CA276178 Cancer Prevention and Research Institute of Texas, RP240117 Victory Houston Permanent Health Funds QIAC Partnership in Research Grant Rexanna's Foundation for Fighting Lung Cancer

Supplementary Table S7: Lung cancers characteristics diagnosed during the active phase of the LEAP study based on the selection method for inclusion criteria: NLST criteria vs broadened criteria (≥ 20 pack-years, ≥ 50 years old, one additional lung cancer risk factor)

Supplementary Figure S1: Map of participating study centers

Supplementary Table S4: LDCT screening results in the LEAP cohort by centers

Individuals with rheumatoid arthritis (RA), particularly those with a history of smoking, are at increased risk for lung cancer. However, real-world data on the use of low dose computed tomography (LDCT) screening for lung cancer in this population are lacking. Therefore, we assessed the rates, trends, and predictors of LDCT screening among patients with a smoking history and RA. A retrospective cohort study using the IQVIA PharMetrics® Plus for Academics Closed Health Plan claims database was conducted. Patients with RA diagnosed in 2014-2023, aged 50-77 years, and with a history of smoking and no prior cancer or advanced respiratory illness were identified. LDCT screening was identified using Healthcare Common Procedure Coding System codes. Among 15,341 eligible patients, 997 (6.5%) received LDCT screening; median time from RA diagnosis to first LDCT claim was 452 days. In a sensitivity analysis of 12,301 patients with continuous insurance coverage during the 12 months after RA diagnosis, only 367 (3%) underwent LDCT within the first year after RA diagnosis. Demographic factors such as male sex (aOR=1.24; p=0.003), age 55-69 (aOR=~1.90; p<0.0001), and residence outside the West region (p<0.0001) increased uptake. Clinical characteristics and healthcare utilization such as COPD (aOR=2.85; p<0.0001), more outpatient visits (aOR=2.15-2.41; p< 0.001) and DMARD use (aOR=1.27-1.52; p≤ 0.001) also increased odds, whereas greater comorbidity burden reduced screening (aOR=0.79-0.39; p≤0.006). In conclusion, LDCT rates in patients with RA remained low overall and varied according to demographic and clinical factors. Targeted efforts may be needed to improve screening uptake in this high-risk population.

Supplementary Table S5: Characteristics of screen-detected lung cancers and during follow-up in the LEAP cohort

Supplementary Table S2: Management procedures for positive screening tests within the LEAP cohort, all sites.

Supplementary Table S8: Comparison of lung cancer incidence between the LEAP cohort and the NLST, stratified by age at enrollment, sex, pack-year cigarette use and race.

BACKGROUND:Blood-based biomarkers could improve the effectiveness of lung cancer screening (LCS) with low-dose CT (LDCT) through more accurate lung cancer risk stratification and nodule malignancy risk assessment. The Lung Cancer, Early Detection, Assessment of Risk, and Prevention (LEAP) study aims to establish a reference set to validate promising cancer biomarkers in the context of LCS. METHODS:This prospective international cohort study (United States, France, and Spain) enrolled individuals with elevated risk of developing lung cancer based on National Comprehensive Cancer Network Guidelines into an LCS program. Participants underwent three annual rounds of LDCT-LCS, with blood specimens collected at each time point. Questionnaires and medical chart reviews were utilized to determine participants' cancers status. RESULTS:Between 2014 and 2019, 2,841 participants were enrolled who underwent LDCT: 2,841 at baseline, 2,097 at year 1 (74%), and 1,779 at year 2 (63%). Rates at baseline, year 1, and year 2 were positive scans (13%, 7.5%, and 7.5%, respectively), false positives (12%, 6.8%, and 6.9%, respectively), and screen-detected lung cancer (0.9%, 0.9%, and 0.7%, respectively). After 5 years of follow-up, 74 lung cancer cases were detected: 55% stage I, 11% stage II, 16% stage III, and 12% stage IV. The LEAP biobank collected 6,586 blood specimens, including 126 (lung) and 201 (other) prediagnostic samples within 5 years of diagnosis. CONCLUSIONS:The LEAP cohort provides a resource with a longitudinal database of participant data, LDCT imaging, and matched blood specimens for biomarker validation, aiming to address unmet clinical needs in LCS. IMPACT:LEAP provides longitudinal biospecimens linked with clinical follow-up and LDCT imaging for biomarker validation in the context of imaging findings and lung cancer diagnosis.

Rationale: A 4-protein biomarker panel (4MP) can improve estimation of lung cancer risk and identify individuals who may benefit most from lung cancer screening. In the current study, we evaluate the performance of the 4MP for risk determination of lung cancer in individuals undergoing lung cancer screening. Methods: We obtained two cohorts of samples from the National Lung Screening Trial (NLST). The first cohort consisted of 675 samples from individuals with CT scans without suspicious findings, including 135 eventually diagnosed with lung cancer and 540 matched control samples. The second cohort consisted of 715 samples from individuals with screen-detected pulmonary nodules, including 143 diagnosed with cancer and 572 matched controls. We additionally obtained plasma from 27 cases lung cancer cases and 1308 non-cases with screen-detected pulmonary nodules that were drawn from the University of British Columbia site of the International Lung Screening Trial (ILST). The 4MP was measured using a multiplex bead-based immunoassay using coefficients fixed from a previously developed logistic regression model. Performance was evaluated using receiver operating characteristic analysis, including computing the area under the curve (AUCs) as well as a net reclassification index (NRI) to estimate how well the 4MP improved existing risk prediction models such as the Brock nodule risk model. Results: In the NLST cohort with negative CTs, for those individuals eventually diagnosed with stage II or higher lung cancer, the 4MP showed an AUC of 0.67 (95% CI 0.59-0.74). For all those with advanced (stage III and higher), AUC was 0.71 (95% CI 0.61-0.81). For individuals diagnosed within 1 year of blood draw, the AUC was 0.71 (95% CI 0.51-0.91). In those with indeterminate nodules, the 4MP showed similar performance in both the NLST and ILST samples, with an AUC of 0.64 (95% CI 0.59-0.70) in those eventually diagnosed with stage II or higher lung cancer from the NLST. We further evaluated the performance of the 4MP to reclassify pulmonary nodules, roughly based on Lung-RADS risk category. This model was developed in the ILST samples and validated in the NLST samples. The 4MP, added to the Brock model, showed an NRI of 0.26 compared to the Brock model alone in the validation set. Conclusions: The 4MP may be a useful adjunct to screening, especially in identifying those who will develop more advanced stage disease in the following year. This could help to identify individuals who may benefit from closer clinical follow-up.

Background/Objectives: We aimed to discover genes with bimodal expression linked to patient outcomes, to reveal underlying oncogenotypes and identify new therapeutic insights in lung adenocarcinoma (LUAD). Methods: We performed meta-analysis to screen LUAD datasets for prognostic genes with bimodal expression patterns. Kynureninase (KYNU), a key enzyme in tryptophan catabolism, emerged as a top candidate. We then examined its relationship with LUAD mutations, metabolic alterations, immune microenvironment states, and expression patterns in human and mouse models using bulk and single-cell transcriptomics, metabolomics, and preclinical model datasets. Pan-cancer prognostic associations were also assessed. Results: Model-based clustering of KYNU expression outperformed median-based dichotomization in prognostic accuracy. KYNU was elevated in tumors with KEAP1 and STK11 co-mutations but remained a strong independent prognostic marker. Metabolomic analysis showed that KYNU-high tumors had increased anthranilic acid, a catalytic product, while maintaining stable kynurenine levels, suggesting a compensatory mechanism sustaining immunosuppressive signaling. Single-cell and bulk data showed KYNU expression was cancer cell-intrinsic in immune-cold tumors and myeloid-derived in immune-infiltrated tumors. In murine LUAD models, Kynu expression was predominantly immune-derived and uncoupled from Nrf2/Lkb1 signaling, indicating poor model fidelity. KYNU’s prognostic associations extended across cancer types, with poor outcomes in pancreatic and kidney cancers but favorable outcomes in melanoma, underscoring the need for lineage-specific considerations in therapy development. Conclusions:KYNU is a robust prognostic biomarker and potential immunometabolic target in LUAD, especially in STK11 and KEAP1 co-mutated tumors. Its cancer cell-intrinsic expression and immunosuppressive metabolic phenotype offer translational potential, though species-specific expression patterns pose challenges for preclinical modeling.

Treatment of lung adenocarcinomas (LUADs) that exhibit activated epidermal growth factor receptor (EGFR) with EGFR tyrosine kinase inhibitors (TKIs) has limited efficacy. Assessment of the impact of EGFR TKI on the LUAD surfaceome remodeling reveals potential therapeutic targets. We identify placental type alkaline phosphatase (ALPP), which has restricted expression in normal tissues, among upregulated surface proteins following EGFR TKI treatment of both TKI sensitive as well as resistant cells. EGF treatment represses ALPP expression, whereas EGFR TKIs upregulate its expression through dephosphorylation and activation of FoxO3a, a transcriptional regulator that binds to the promoter region of ALPP. The combination of EGFR TKI plus ALPP antibody conjugated with monomethyl auristatin F enhances tumor killing in osimertinib-sensitive and -resistant LUAD models compared to either treatment alone. Our findings support a combination therapy involving an EGFR inhibitor together with an ALPP antibody drug conjugate for EGFR-mutated LUADs.

Background: Non-smokers and individuals with minimal smoking history represent a significant proportion of lung cancer cases but are often overlooked in current risk assessment models. Pulmonary nodules are commonly detected incidentally—appearing in approximately 24–31% of all chest CT scans regardless of smoking status. However, most established risk models, such as the Brock model, were developed using cohorts heavily enriched with individuals who have substantial smoking histories. This limits their generalizability to non-smoking and light-smoking populations, highlighting the need for more inclusive and tailored risk prediction strategies. Purpose: We aimed to develop a longitudinal radiomics-based approach for lung cancer risk prediction, integrating time-varying radiomic modeling to enhance early detection in USPSTF-ineligible patients. Methods: Unlike conventional models that rely on a single scan, we conducted a longitudinal analysis of 122 patients who were later diagnosed with lung cancer, with a total of 622 CT scans analyzed. Of these patients, 69% were former smokers, while 30% had never smoked. Quantitative radiomic features were extracted from serial chest CT scans to capture temporal changes in nodule evolution. A time-varying survival model was implemented to dynamically assess lung cancer risk. Additionally, we evaluated the integration of handcrafted radiomic features and the deep learning-based Sybil model to determine the added value of combining local nodule characteristics with global lung assessments. Results: Our radiomic analysis identified specific CT patterns associated with malignant transformation, including increased nodule size, voxel intensity, textural entropy, as indicators of tumor heterogeneity and progression. Integrating radiomics, delta-radiomics, and longitudinal imaging features resulted in the optimal predictive performance during cross-validation (concordance index [C-index]: 0.69), surpassing that of models using demographics alone (C-index: 0.50) and Sybil alone (C-index: 0.54). Compared to the Brock model (67% accuracy, 100% sensitivity, 33% specificity), our composite risk model achieved 78% accuracy, 89% sensitivity, and 67% specificity, demonstrating improved early cancer risk stratification. Kaplan–Meier curves and individualized cancer development probability functions further validated the model’s ability to track dynamic risk progression for individual patients. Visual analysis of longitudinal CT scans confirmed alignment between predicted risk and evolving nodule characteristics. Conclusions: Our study demonstrates that integrating radiomics, sybil, and clinical factors enhances future lung cancer risk prediction in USPSTF-ineligible patients, outperforming existing models and supporting personalized screening and early intervention strategies.

RATIONALE Lung cancer remains the number one cause of cancer mortality in the United States, often diagnosed at advanced stage with poor 5-year survival. However, lung cancer screening has been shown to increase the proportion of patients diagnosed at early stage and reduce cancer specific mortality by 20-24%. Recently, the development and clinical implementation of AI in patient management has been shown to equal or surpass pulmonary nodule detection and characterization performance of radiologists. Here we present a pivotal validation study of an AI/ML-based software that detects, segments, and characterizes pulmonary nodules between 4-30mm present on low-dose CT screening exams. METHODS We conducted a retrospective validation study of 1147 patients (29.8% cancer prevalence) meeting the USPSTF criteria for LCS with LDCT screening exams having solid and part-solid nodules ≤30mm identified from academic and community sites in the EU (29%) and USA (71%). To demonstrate software stability on technical and clinical subclasses (e.g. size, shape), the dataset was enriched such that each subclass was sufficiently represented (table). Reference standard was established via histopathology or ≥12month stability. Sensitivity, specificity, and FP/scan values are presented at the maximum Youden Index (MYI). RESULTS Average nodule size for benign (2275 nodules) and cancerous (371 nodules) nodules was 6.4±2.9mm and 14.9±5.7mm respectively. Patients with lung cancer had predominately stage I (79.7%) disease and 20.5% had nodules that were < 10mm in size. Patient level AUC was 0.904 [95% CI, 0.881-0.926], with sensitivity and specificity of 80.1% and 86.6%. To assess correct localization and characterization via highest risk assignment of cancer nodules the AU-LROC was 0.869 with a sensitivity of 78.4% and specificity 86.6% at the MYI. For nodule subclass analysis, AUCs for patients with nodules <10mm and without nodules was 0.834, with corresponding sensitivity of 91.4%. The patient level AUC was 0.890 for Stage I disease and 0.909 for stage II-IV. Focusing on stage I cancer, the sensitivity and specificity were 80.4% and 83.6% respectively. Corresponding FROC showed a sensitivity of 92.6% with a FP/scan of 0.801. CONCLUSIONS The AI/ML algorithm demonstrates a high level of performance in a multicenter validation cohort enriched for cancer prevalence, cancer stage, and small non-spiculated cancer nodules, with high sensitivity across nodule size and cancer stage. This software may help optimize the detection and management of screen-detected nodules leading to earlier diagnosis and more effective therapy.

INTRODUCTION:Natural history models (NHMs) of lung cancer (LC) simulate the disease's natural progression providing a baseline for assessing the impact of interventions. NHMs have been increasingly used to inform public health policies, highlighting their utility. The objective of this scoping review was to summarize existing LC NHMs, identify their limitations, and propose a framework for future NHM development. METHODS:We searched MEDLINE, Embase, Web of Science, and IEEE Xplore from their inception to October 5, 2023, for peer-reviewed, full-length articles with an LC NHM. Model characteristics, their applications, data sources used, and limitations were extracted and narratively synthesized. RESULTS:From 238 publications, 69 publications were included in our review, corresponding to 22 original LC NHMs and 47 model applications. The majority of the models (n = 15, 68 %) used a microsimulation approach. NHM parameters were predominately informed by cancer registries, trial and institutional data, and literature. Model quality and performance were evaluated in 8 (36 %) models. Twenty (91 %) models included at least one carcinogenesis risk factor-primarily age, sex, and smoking history. Three (14 %) LC NHMs modeled progression in never-smokers; one (5 %) addressed recurrence. Non-tobacco smoking, nodule type, and biomarker expression were not considered in existing NHMs. Based on our findings, we proposed a framework for future LC NHM development which incorporates recurrence, nodule type differentiation, biomarker expression levels, biological factors, and non-smoking-related risk factors. CONCLUSION:Regular updating and future research are warranted to address limitations in existing NHMs thereby ensuring relevance and accuracy of modeling approaches in the evolving LC landscape.

Introduction Un projet pilote de dépistage du cancer broncho-pulmonaire (CBP) par scanner thoracique faible dose (STFD) va prochainement être initié en France suite aux résultats favorables des essais cliniques randomisés de phase 3 NLST (États-Unis) et NELSON (Europe). Ce dépistage concerne des sujets âgés de 50 à 74ans, ayant un tabagisme≥20 paquets-années, actifs ou sevrés depuis moins de 15ans. Toutefois, la population ciblée par ce dépistage présente fréquemment des comorbidités pouvant affecter la balance bénéfice-risque du dépistage. L’objectif de cette étude est d’évaluer l’impact des comorbidités sur les bénéfices de mortalité du dépistage du CBP. Méthodes Il s’agit d’une analyse post-hoc de deux essais cliniques US : l’étude PLCO et NLST. Les données de l’étude PLCO ont été utilisées pour élaborer un indice de comorbidité (PLCO-ci) en pondérant chaque comorbidité en fonction de sa valeur prédictive sur la mortalité (hors décès par CBP) à 5ans. Le PLCO-ci a ensuite été calculé pour chaque participant de l’étude NLST ; puis les résultats de l’étude NLST ont été analysés en stratifiant la population en quintile en fonction de la valeur du PLCO-ci (Q1 : indice de comorbidité bas ; Q5 : indice de comorbidité élevé). La mortalité par CBP a été comparée entre les deux bras de randomisation (radiographie thoracique (CXR) vs STFD) à l’aide de mesures relatives (cause-specific hazard ratio (csHR) [95 % confident interval] (CI)) et absolues (nombre de décès par CBP évités) dans chaque quintile. Résultats L’indice PLCO-Ci a pris en compte l’âge, le sexe, la durée du tabagisme et les antécédents d’accident vasculaire cérébral, d’hypertension artérielle, de maladies cardiovasculaires, de diabète, de broncho-pneumopathie chronique obstructive et de cancer hors CBP. Dans l’étude NLST, après 6,5ans de suivi, 448 et 359 décès par CBP étaient respectivement rapportés dans le bras radiographie thoracique (bras contrôle) et le bras SFTD (dépistage). Les sujets avec un niveau intermédiaire de comorbidité (quintiles 2 à 4) présentaient une réduction significative de la mortalité par CBP (csHR [CI 95 %] Q2=0,63 [0,41–0,95], Q3=0,68 [0,48–0,96], Q4=0,72 [0,55–0,96]). Dans le Q1, la réduction de la mortalité par CBP n’atteignait pas le seuil de significativité (csHR=0,72 [0,45–1,17]) ; aucun bénéfice n’était retrouvé dans le Q5 (csHR=0,99 [0,79–1,23]). Les 60 % des participants avec un niveau de comorbidité intermédiaire (quintiles 2 à 4) représentaient 89 % (79/89) des décès par CBP évités grâce au dépistage. Les sujets avec le moins de comorbidités (Q1) étaient caractérisés par une incidence plus faible des CBP et une fréquence plus importance d’adénocarcinome in situ ou à invasion minime ; et les sujets avec le plus de comorbidités (Q5) par une létalité plus importante des CBP localisés et un taux plus important de CBP n’ayant pas reçu de traitement. Conclusion La prise en compte du niveau de comorbidités permet de sélectionner une population qui tire le plus grand bénéfice du dépistage organisé du cancer du poumon.

Despite the emerging public health concern related to the use of electronic cigarette vapors (ECV), its impact on lung cancer is poorly understood. We assessed the effect of ECV on lung tumorigenesis in a mouse model of lung adenocarcinoma. Mice were exposed to either room air, combustible cigarette smoke (CCS), or ECV 2 hours daily for 8 weeks at which lung samples were harvested and studied for different outcomes. We found that CCS, but not ECV, led to a significant increase in tumor burden. Immunophenotyping of both CCS- and ECV-exposed lungs displayed pronounced pro-tumor immunosuppressive phenotypes, characterized by significantly decreased CD4+ IFNγ+ and CD8+ GZMB+ T cells along with an elevated CD4+ FOXP3+ regulatory T cells. However, differential changes in myeloid cells were observed between CCS and ECV-exposed lungs. A microbiome profiling of matched stool and lung samples showed differences in the relative abundance of lung Pseudomonadotas, while gut Bacillota, particularly Turicibacter, and Ileibacterium were increased by CCS and ECV. We conclude that both CCS and ECV exposure under the applied regimen lead to a protumor immune suppressive lung microenvironment although with different magnitudes and slightly different phenotypes that might explain their differential effects on tumor burden warranting further studies.

Background/Objectives: Persistent pulmonary nodules are at higher risk of developing into lung cancers. Assessing their future cancer risk is essential for successful interception. We evaluated the performance of two risk prediction models for persistent nodules in hospital-based cohorts: the Brock model, based on clinical and radiological characteristics, and the Sybil model, a novel deep learning model for lung cancer risk prediction. Methods: Patients with persistent pulmonary nodules—defined as nodules detected on at least two computed tomography (CT) scans, three months apart, without evidence of shrinkage—were included in the retrospective (n = 130) and prospective (n = 301) cohorts. We analyzed the correlations between demographic factors, nodule characteristics, and Brock scores and assessed the performance of both models. We also built machine learning models to refine the risk assessment for our cohort. Results: In the retrospective cohort, Brock scores ranged from 0% to 85.82%. In the prospective cohort, 62 of 301 patients were diagnosed with lung cancer, displaying higher median Brock scores than those without lung cancer diagnosis (18.65% vs. 4.95%, p < 0.001). Family history, nodule size ≥10 mm, part-solid nodule types, and spiculation were associated with the risks of lung cancer. The Brock model had an AUC of 0.679, and Sybil’s AUC was 0.678. We tested five machine learning models, and the logistic regression model achieved the highest AUC at 0.729. Conclusions: For patients with persistent pulmonary nodules in real-world cancer hospital-based cohorts, both the Brock and Sybil models had values and limitations for lung cancer risk prediction. Optimizing predictive models in this population is crucial for improving early lung cancer detection and interception.

Personal history of cancer is an independent risk factor for developing lung cancer. However, it is not considered in the current US lung cancer screening (LCS) guidelines. In this study, we assessed the risk of developing lung cancer among cancer survivors across 24 different sites of first primary cancer stratified by their LCS eligibility status. Using data from the Patient History Database at the University of Texas MD Anderson Cancer Center, we calculated and compared the cumulative incidence of second primary lung cancer, the overall and the LCS eligibility status‐specific, stratified by the site of first primary cancer among cancer survivors. We found that among lung, head and neck (H&N), bladder, cervical, breast, and prostate cancer survivors, the risks of second primary lung cancer were statistically significantly higher compared to the overall risk among all cancer survivors (i.e., all cancer sites combined). Risk ratios (RR) ranged between 1.14 (95%CI:1.00–1.28, p = 0.0431) among prostate cancer survivors to 2.9 (95%CI:2.58–3.26, p < 0.001) among H&N cancer survivors. Other than first primary lung cancer (RR: 1.33; 95%CI:1.14–1.57; p < 0.001), H&N (RR: 1.73; 95%CI:1.45–2.05; p < 0.001) and bladder (RR: 1.32; 95%CI:1–1.74; p = 0.0483) cancer survivors, who were non‐eligible for LCS, had significantly higher lung cancer risk than all cancer survivors. In conclusion, H&N, bladder, cervical, breast, and prostate cancer survivors have a high risk of developing second primary lung cancer. Specifically, personal history of H&N and bladder cancer, even among non‐eligible for LCS individuals, remain at a sufficiently high risk, which warrants further consideration as an independent eligibility factor for LCS guidelines.