The International Association for the Study of Lung Cancer (IASLC) Staging Project for Lung Cancer: Recommendation to Introduce Spread Through Air Spaces as a Histologic Descriptor in the Ninth Edition of the TNM Classification of Lung Cancer. Analysis of 4061 Pathologic Stage I NSCLC.

INTRODUCTION: Spread through air spaces (STAS) consists of lung cancer tumor cells that are identified beyond the edge of the main tumor in the surrounding alveolar parenchyma. It has been reported by meta-analyses to be an independent prognostic factor in the major histologic types of lung cancer, but its role in lung cancer staging is not established. METHODS: To assess the clinical importance of STAS in lung cancer staging, we evaluated 4061 surgically resected pathologic stage I R0 NSCLC collected from around the world in the International Association for the Study of Lung Cancer database. We focused on whether STAS could be a useful additional histologic descriptor to supplement the existing ones of visceral pleural invasion (VPI) and lymphovascular invasion (LVI). RESULTS: STAS was found in 930 of 4061 of the pathologic stage I NSCLC (22.9%). Patients with tumors exhibiting STAS had a significantly worse recurrence-free and overall survival in both univariate and multivariable analyses involving cohorts consisting of all NSCLC, specific histologic types (adenocarcinoma and other NSCLC), and extent of resection (lobar and sublobar). Interestingly, STAS was independent of VPI in all of these analyses. CONCLUSIONS: These data support our recommendation to include STAS as a histologic descriptor for the Ninth Edition of the TNM Classification of Lung Cancer. Hopefully, gathering these data in the coming years will facilitate a thorough analysis to better understand the relative impact of STAS, LVI, and VPI on lung cancer staging for the Tenth Edition TNM Stage Classification.

CXCL13 Positive Cells Localization Predict Response to Anti-PD-1/PD-L1 in Pulmonary Non-Small Cell Carcinoma.

Background: Immune checkpoint inhibitors (ICIs) have revolutionized non-small cell lung cancers (NSCLCs) treatment, but only 20-30% of patients benefit from these treatments. Currently, PD-L1 expression in tumor cells is the only clinically approved predictor of ICI response in lung cancer, but concerns arise due to its low negative and positive predictive value. Recent studies suggest that CXCL13+ T cells in the tumor microenvironment (TME) may be a good predictor of response. We aimed to assess if CXCL13+ cell localization within the TME can predict ICI response in advanced NSCLC patients. Methods: This retrospective study included 65 advanced NSCLC patients treated with Nivolumab/Pembrolizumab at IUCPQ or CHUM and for whom a pretreatment surgical specimen was available. Good responders were defined as having a complete radiologic response at 1 year, and bad responders were defined as showing cancer progression at 1 year. IHC staining for CXCL13 was carried out on a representative slide from a resection specimen, and CXCL13+ cell density was evaluated in tumor (T), invasive margin (IM), non-tumor (NT), and tertiary lymphoid structure (TLS) compartments. Cox models were used to analyze progression-free survival (PFS) and overall survival (OS) probability, while the Mann-Whitney test was used to compare CXCL13+ cell density between responders and non-responders. Results: We showed that CXCL13+ cell density localization within the TME is associated with ICI efficacy. An increased density of CXCL13+ cells across all compartments was associated with a poorer prognostic (OS; HR = 1.22; 95%CI = 1.04-1.42; p = 0.01, PFS; HR = 1.16; p = 0.02), or a better prognostic when colocalized within TLSs (PFS; HR = 0.84, p = 0.03). Conclusion: Our results support the role of CXCL13+ cells in advanced NSCLC patients, with favorable prognosis when localized within TLSs and unfavorable prognosis when present elsewhere. The concomitant proximity of CXCL13+ and CD20+ cells within TLSs may favor antigen presentation to T cells, thus enhancing the effect of PD-1/PD-L1 axis inhibition. Further validation is warranted to confirm the potential relevance of this biomarker in a clinical setting.

The Cross-Scale Association between Pathomics and Radiomics Features in Immunotherapy-Treated NSCLC Patients: A Preliminary Study.

BACKGROUND: Recent advances in cancer biomarker development have led to a surge of distinct data modalities, such as medical imaging and histopathology. To develop predictive immunotherapy biomarkers, these modalities are leveraged independently, despite their orthogonality. This study aims to explore the cross-scale association between radiological scans and digitalized pathology images for immunotherapy-treated non-small cell lung cancer (NSCLC) patients. METHODS: This study involves 36 NSCLC patients who were treated with immunotherapy and for whom both radiology and pathology images were available. A total of 851 and 260 features were extracted from CT scans and cell density maps of histology images at different resolutions. We investigated the radiopathomics relationship and their association with clinical and biological endpoints. We used the Kolmogorov-Smirnov (KS) method to test the differences between the distributions of correlation coefficients with the two imaging modality features. Unsupervised clustering was done to identify which imaging modality captures poor and good survival patients. RESULTS: Our results demonstrated a significant correlation between cell density pathomics and radiomics features. Furthermore, we also found a varying distribution of correlation values between imaging-derived features and clinical endpoints. The KS test revealed that the two imaging feature distributions were different for PFS and CD8 counts, while similar for OS. In addition, clustering analysis resulted in significant differences in the two clusters generated from the radiomics and pathomics features with respect to patient survival and CD8 counts. CONCLUSION: The results of this study suggest a cross-scale association between CT scans and pathology H&E slides among ICI-treated patients. These relationships can be further explored to develop multimodal immunotherapy biomarkers to advance personalized lung cancer care.

Multi-institutional prognostic modeling of survival outcomes in NSCLC patients treated with first-line immunotherapy using radiomics.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have emerged as one of the most promising first-line therapeutics in the management of non-small cell lung cancer (NSCLC). However, only a subset of these patients responds to ICIs, highlighting the clinical need to develop better predictive and prognostic biomarkers. This study will leverage pre-treatment imaging profiles to develop survival risk models for NSCLC patients treated with first-line immunotherapy. METHODS: Advanced NSCLC patients (n = 149) were retrospectively identified from two institutions who were treated with first-line ICIs. Radiomics features extracted from pretreatment imaging scans were used to build the predictive models for progression-free survival (PFS) and overall survival (OS). A compendium of five feature selection methods and seven machine learning approaches were utilized to build the survival risk models. The concordance index (C-index) was used to evaluate model performance. RESULTS: From our results, we found several combinations of machine learning algorithms and feature selection methods to achieve similar performance. K-nearest neighbourhood (KNN) with ReliefF (RL) feature selection was the best-performing model to predict PFS (C-index = 0.61 and 0.604 in discovery and validation cohorts), while XGBoost with Mutual Information (MI) feature selection was the best-performing model for OS (C-index = 0.7 and 0.655 in discovery and validation cohorts). CONCLUSION: The results of this study highlight the importance of implementing an appropriate feature selection method coupled with a machine learning strategy to develop robust survival models. With further validation of these models on external cohorts when available, this can have the potential to improve clinical decisions by systematically analyzing routine medical images.

Optimizing tissue stewardship in non-small cell lung cancer to support molecular characterization and treatment selection: statement from a working group of thoracic pathologists.

Many patients with non-small cell lung cancer do not receive guideline-recommended, biomarker-directed therapy, despite the potential for improved clinical outcomes. Access to timely, accurate, and comprehensive molecular profiling, including targetable protein overexpression, is essential to allow fully informed treatment decisions to be taken. In turn, this requires optimal tissue management to protect and maximize the use of this precious finite resource. Here, a group of leading thoracic pathologists recommend factors to consider for optimal tissue management. Starting from when lung cancer is first suspected, keeping predictive biomarker testing in the front of the mind should drive the development of practices and procedures that conserve tissue appropriately to support molecular characterization and treatment selection.

Imaging-Based Biomarkers Predict Programmed Death-Ligand 1 and Survival Outcomes in Advanced NSCLC Treated With Nivolumab and Pembrolizumab: A Multi-Institutional Study.

Background: Although the immune checkpoint inhibitors, nivolumab and pembrolizumab, were found to be promising in patients with advanced NSCLC, some of them either do not respond or have recurrence after an initial response. It is still unclear who will benefit from these therapies, and, hence, there is an unmet clinical need to build robust biomarkers. Methods: Patients with advanced NSCLC (N = 323) who were treated with pembrolizumab or nivolumab were retrospectively identified from two institutions. Radiomics features extracted from baseline pretreatment computed tomography scans along with the clinical variables were used to build the predictive models for overall survival (OS), progression-free survival (PFS), and programmed death-ligand 1 (PD-L1). To develop the imaging and integrative clinical-imaging predictive models, we used the XGBoost learning algorithm with ReliefF feature selection method and validated them in an independent cohort. The concordance index for OS, PFS, and area under the curve for PD-L1 was used to evaluate model performance. Results: We developed radiomics and the ensemble radiomics-clinical predictive models for OS, PFS, and PD-L1 expression. The concordance indices of the radiomics model were 0.60 and 0.61 for predicting OS and PFS and area under the curve was 0.61 for predicting PD-L1 in the validation cohort, respectively. The combined radiomics-clinical model resulted in higher performance with 0.65, 0.63, and 0.68 to predict OS, PFS, and PD-L1 in the validation cohort, respectively. Conclusions: We found that pretreatment computed tomography imaging along with clinical data can aid as predictive biomarkers for PD-L1 and survival end points. These imaging-driven approaches may prove useful to expand the therapeutic options for nonresponders and improve the selection of patients who would benefit from immune checkpoint inhibitors.

Canadian Consensus Recommendations for the Management of Operable Stage II/III Non-Small-Cell Lung Cancer: Results of a Modified Delphi Process.

The treatment paradigm for patients with stage II/III non-small-cell lung cancer (NSCLC) is rapidly evolving. We performed a modified Delphi process culminating at the Early-stage Lung cancer International eXpert Retreat (ELIXR23) meeting held in Montreal, Canada, in June 2023. Participants included medical and radiation oncologists, thoracic surgeons and pathologists from across Quebec. Statements relating to diagnosis and treatment paradigms in the preoperative, operative and postoperative time periods were generated and modified until all held a high level of consensus. These statements are aimed to help guide clinicians involved in the treatment of patients with stage II/III NSCLC.

Generalization optimizing machine learning to improve CT scan radiomics and assess immune checkpoint inhibitors' response in non-small cell lung cancer: a multicenter cohort study.

Background: Recent developments in artificial intelligence suggest that radiomics may represent a promising non-invasive biomarker to predict response to immune checkpoint inhibitors (ICIs). Nevertheless, validation of radiomics algorithms in independent cohorts remains a challenge due to variations in image acquisition and reconstruction. Using radiomics, we investigated the importance of scan normalization as part of a broader machine learning framework to enable model external generalizability to predict ICI response in non-small cell lung cancer (NSCLC) patients across different centers. Methods: Radiomics features were extracted and compared from 642 advanced NSCLC patients on pre-ICI scans using established open-source PyRadiomics and a proprietary DeepRadiomics deep learning technology. The population was separated into two groups: a discovery cohort of 512 NSCLC patients from three academic centers and a validation cohort that included 130 NSCLC patients from a fourth center. We harmonized images to account for variations in reconstruction kernel, slice thicknesses, and device manufacturers. Multivariable models, evaluated using cross-validation, were used to estimate the predictive value of clinical variables, PD-L1 expression, and PyRadiomics or DeepRadiomics for progression-free survival at 6 months (PFS-6). Results: The best prognostic factor for PFS-6, excluding radiomics features, was obtained with the combination of Clinical + PD-L1 expression (AUC = 0.66 in the discovery and 0.62 in the validation cohort). Without image harmonization, combining Clinical + PyRadiomics or DeepRadiomics delivered an AUC = 0.69 and 0.69, respectively, in the discovery cohort, but dropped to 0.57 and 0.52, in the validation cohort. This lack of generalizability was consistent with observations in principal component analysis clustered by CT scan parameters. Subsequently, image harmonization eliminated these clusters. The combination of Clinical + DeepRadiomics reached an AUC = 0.67 and 0.63 in the discovery and validation cohort, respectively. Conversely, the combination of Clinical + PyRadiomics failed generalizability validations, with AUC = 0.66 and 0.59. Conclusion: We demonstrated that a risk prediction model combining Clinical + DeepRadiomics was generalizable following CT scan harmonization and machine learning generalization methods. These results had similar performances to routine oncology practice using Clinical + PD-L1. This study supports the strong potential of radiomics as a future non-invasive strategy to predict ICI response in advanced NSCLC.

A Radiomics-Clinical Model Predicts Overall Survival of Non-Small Cell Lung Cancer Patients Treated with Immunotherapy: A Multicenter Study.

BACKGROUND: Immune checkpoint inhibitors (ICIs) are a great breakthrough in cancer treatments and provide improved long-term survival in a subset of non-small cell lung cancer (NSCLC) patients. However, prognostic and predictive biomarkers of immunotherapy still remain an unmet clinical need. In this work, we aim to leverage imaging data and clinical variables to develop survival risk models among advanced NSCLC patients treated with immunotherapy. METHODS: This retrospective study includes a total of 385 patients from two institutions who were treated with ICIs. Radiomics features extracted from pretreatment CT scans were used to build predictive models. The objectives were to predict overall survival (OS) along with building a classifier for short- and long-term survival groups. We employed the XGBoost learning method to build radiomics and integrated clinical-radiomics predictive models. Feature selection and model building were developed and validated on a multicenter cohort. RESULTS: We developed parsimonious models that were associated with OS and a classifier for short- and long-term survivor groups. The concordance indices (C-index) of the radiomics model were 0.61 and 0.57 to predict OS in the discovery and validation cohorts, respectively. While the area under the curve (AUC) values of the radiomic models for short- and long-term groups were found to be 0.65 and 0.58 in the discovery and validation cohorts. The accuracy of the combined radiomics-clinical model resulted in 0.63 and 0.62 to predict OS and in 0.77 and 0.62 to classify the survival groups in the discovery and validation cohorts, respectively. CONCLUSIONS: We developed and validated novel radiomics and integrated radiomics-clinical survival models among NSCLC patients treated with ICIs. This model has important translational implications, which can be used to identify a subset of patients who are not likely to benefit from immunotherapy. The developed imaging biomarkers may allow early prediction of low-group survivors, though additional validation of these radiomics models is warranted.

Radiomics approaches to predict PD-L1 and PFS in advanced non-small cell lung patients treated with immunotherapy: a multi-institutional study.

With the increasing use of immune checkpoint inhibitors (ICIs), there is an urgent need to identify biomarkers to stratify responders and non-responders using programmed death-ligand (PD-L1) expression, and to predict patient-specific outcomes such as progression free survival (PFS). The current study is aimed to determine the feasibility of building imaging-based predictive biomarkers for PD-L1 and PFS through systematically evaluating a combination of several machine learning algorithms with different feature selection methods. A retrospective, multicenter study of 385 advanced NSCLC patients amenable to ICIs was undertaken in two academic centers. Radiomic features extracted from pretreatment CT scans were used to build predictive models for PD-L1 and PFS (short-term vs. long-term survivors). We first employed the LASSO methodology followed by five feature selection methods and seven machine learning approaches to build the predictors. From our analyses, we found several combinations of feature selection methods and machine learning algorithms to achieve a similar performance. Logistic regression with ReliefF feature selection (AUC = 0.64, 0.59 in discovery and validation cohorts) and SVM with Anova F-test feature selection (AUC = 0.64, 0.63 in discovery and validation datasets) were the best-performing models to predict PD-L1 and PFS. This study elucidates the application of suitable feature selection approaches and machine learning algorithms to predict clinical endpoints using radiomics features. Through this study, we identified a subset of algorithms that should be considered in future investigations for building robust and clinically relevant predictive models.

A Simplified Version of the IASLC Grading System for Invasive Pulmonary Adenocarcinomas With Improved Prognosis Discrimination.

Tumor grading enables better management of patients and treatment options. The International Association for the Study of Lung Cancer (IASLC) Pathology Committee has recently released a 3-tier grading system for invasive pulmonary adenocarcinoma consisting of predominant histologic patterns plus a cutoff of 20% of high-grade components including solid, micropapillary, and complex glandular patterns. The goal of this study was to validate the prognostic value of the new IASLC grading system and to compare its discriminatory performance to the predominant pattern-based grading system and a simplified version of the IASLC grading system without complex glandular patterns. This was a single-site retrospective study based on a 20-year data collection of patients that underwent lung cancer surgery. All invasive pulmonary adenocarcinomas confirmed by the histologic review were evaluated in a discovery cohort (n=676) and a validation cohort (n=717). The median duration of follow-up in the combined dataset (n=1393) was 7.5 years. The primary outcome was overall survival after surgery. The 3 grading systems had strong and relatively similar predictive performance, but the best parsimonious model was the simplified IASLC grading system (log-rank P =1.39E-13). The latter was strongly associated with survival in the validation set ( P =1.1E-18) and the combined set ( P =5.01E-35). We observed a large proportion of patients upgraded to the poor prognosis group using the IASLC grading system, which was attenuated when using the simplified IASLC grading system. In conclusion, we identified a histologic simpler classification for invasive pulmonary adenocarcinomas that outperformed the recently proposed IASLC grading system. A simplified grading system is clinically convenient and will facilitate widespread implementation.

Single-cell spatial landscape of immunotherapy response reveals mechanisms of CXCL13 enhanced antitumor immunity.

BACKGROUND: Immunotherapy has revolutionized clinical outcomes for patients suffering from lung cancer, yet relatively few patients sustain long-term durable responses. Recent studies have demonstrated that the tumor immune microenvironment fosters tumorous heterogeneity and mediates both disease progression and response to immune checkpoint inhibitors (ICI). As such, there is an unmet need to elucidate the spatially defined single-cell landscape of the lung cancer microenvironment to understand the mechanisms of disease progression and identify biomarkers of response to ICI. METHODS: Here, in this study, we applied imaging mass cytometry to characterize the tumor and immunological landscape of immunotherapy response in non-small cell lung cancer by describing activated cell states, cellular interactions and neighborhoods associated with improved efficacy. We functionally validated our findings using preclinical mouse models of cancer treated with anti-programmed cell death protein-1 (PD-1) immune checkpoint blockade. RESULTS: We resolved 114,524 single cells in 27 patients treated with ICI, enabling spatial resolution of immune lineages and activation states with distinct clinical outcomes. We demonstrated that CXCL13 expression is associated with ICI efficacy in patients, and that recombinant CXCL13 potentiates anti-PD-1 response in vivo in association with increased antigen experienced T cell subsets and reduced CCR2+ monocytes. DISCUSSION: Our results provide a high-resolution molecular resource and illustrate the importance of major immune lineages as well as their functional substates in understanding the role of the tumor immune microenvironment in response to ICIs.

Single-cell spatial landscapes of the lung tumour immune microenvironment.

Single-cell technologies have revealed the complexity of the tumour immune microenvironment with unparalleled resolution1-9. Most clinical strategies rely on histopathological stratification of tumour subtypes, yet the spatial context of single-cell phenotypes within these stratified subgroups is poorly understood. Here we apply imaging mass cytometry to characterize the tumour and immunological landscape of samples from 416 patients with lung adenocarcinoma across five histological patterns. We resolve more than 1.6 million cells, enabling spatial analysis of immune lineages and activation states with distinct clinical correlates, including survival. Using deep learning, we can predict with high accuracy those patients who will progress after surgery using a single 1-mm2 tumour core, which could be informative for clinical management following surgical resection. Our dataset represents a valuable resource for the non-small cell lung cancer research community and exemplifies the utility of spatial resolution within single-cell analyses. This study also highlights how artificial intelligence can improve our understanding of microenvironmental features that underlie cancer progression and may influence future clinical practice.

Retrospective Assessment of Complementary Liquid Biopsy on Tissue Single-Gene Testing for Tumor Genotyping in Advanced NSCLC.

Biomarker testing is key for non-small cell lung cancer (NSCLC) management and plasma based next-generation sequencing (NGS) is increasingly characterized as a non-invasive alternative. This study aimed to evaluate the value of complementary circulating tumor DNA (ctDNA) NGS on tissue single-gene testing (SGT). Ninety-one advanced stage NSCLC patients with tumor genotyping by tissue SGT (3 genes) followed by ctDNA (38 genes amplicon panel) were included. ctDNA was positive in 47% (n = 43) and identified a targetable biomarker in 19 patients (21%). The likelihood of positivity on ctDNA was higher if patients had extra-thoracic disease (59%) or were not under active treatment (59%). When compared to SGT, ctDNA provided additional information in 41% but missed a known alteration in 8%. Therapeutic change for targeted therapy based on ctDNA occurred in five patients (5%), while seven patients with missed alterations on ctDNA had EGFR mutations or ALK fusions. The median turnaround time of ctDNA was 10 days (range 6-25), shorter (p = 0.002) than the cumulative delays for the tissue testing trajectory until biomarker availability (13 d; range 7-1737). Overall, the results from this study recapitulate the potential and limitations of ctDNA when used complementarily to tissue testing with limited biomarker coverage.

Maximizing lymph node dissection from fresh lung cancer specimens.

Evaluation of lymph nodes during lung cancer resection is essential for pathologic staging and adjuvant treatment decisions. We developed a standardized approach for grossing resected lobes and segments to better assign the N1 category to hilar and peripheral lymph nodes. Lung specimens were dissected centrifugally from the bronchial stump, and all lymph nodes at the segmental and subsegmental bifurcations were removed. When combined with mediastinal lymph node dissection, this approach will likely maximize the number of lymph nodes analysed and improve the accuracy of pathologic N descriptor classification.

Tumor Mutational Burden by Whole-Genome Sequencing in Resected NSCLC of Never Smokers.

BACKGROUND: Data are scarce about tumor mutational burden (TMB) as a biomarker in never smokers with non-small cell lung cancer (NSCLC). METHODS: TMB was assessed by whole-genome sequencing (WGS) and compared with in silico reduced whole-exome sequencing (WES) and targeted commercial next-generation sequencing (NGS) gene panels in 92 paired tumor-normal samples from never smokers who underwent NSCLC resection with curative intent. Analyses were performed to test for association with survival after surgery and to identify the optimal prognostic TMB cutoff. RESULTS: Tumors of never smokers with NSCLC had low TMB scores (median 1.57 mutations/Mb; range, 0.13-17.94). A TMB cutoff of 1.70 mutations/Mb was associated with a 5-year overall survival of 58% in the high-TMB (42% of cases) compared with 86% in low-TMB patients (Wald P = 0.0029). TMB scores from WGS and WES were highly correlated (Spearman ρ = 0.93, P < 2.2e-16). TMB scores from NGS panels demonstrated high intraindividual fluctuations and identified high-TMB patients with 65% concordance in average compared with WGS. CONCLUSIONS: In resected NSCLC of never smokers, high TMB was associated with worse prognosis. WES provided a good estimate of TMB while targeted NGS panels seem to lack adequate depth and resolution in the setting of low mutation burden. IMPACT: TMB is a prognostic indicator of survival in resected NSCLC from individuals who never smoked. In this setting of low mutation counts, TMB can be accurately measured by WGS or WES, but not NGS panels.

Prognostic value of complex glandular patterns in invasive pulmonary adenocarcinomas.

Prognostic stratification of patients surgically resected with invasive pulmonary adenocarcinoma must be improved. Previous studies reported that complex glandular patterns (CGPs), cribriform and fused gland growth patterns, are associated with unfavorable prognosis. The goal of this study is to evaluate the prognostic value of CGPs in patients with resected stage I-IV lung adenocarcinoma. The presence of CGPs as a minor to predominant component was tested for association with overall survival (OS, n = 676) and relapse-free survival (RFS, n = 463) after surgery. CGPs were observed in 284 tumors (42.0%). Cribriform and fused gland were the predominant patterns in 35 and 37 cases, respectively. The presence of cribriform pattern was associated with worse RFS, but not OS. The fused gland pattern alone or grouped into CGPs with the cribriform pattern was not associated with OS and RFS. As a predominant pattern, cribriform was associated with the worse survival compared to the 5 recognized histologic patterns. Patients with fused gland-predominant tumors had 5-year survival that ranged between papillary- and micropapillary-predominant tumors. We conclude that cribriform-predominant, but not fused gland-predominant, is a subtype with poor prognosis similar to the solid and micropapillary subtypes. In contrast, the presence of a minor component of fused gland or CGPs (cribriform + fused gland) is not associated with survival. The cribriform pattern alone offers prognosis stratification improvement, but this effect is attenuated when combined into CGPs to define a subset of acinar-predominant tumors with poor prognosis. This argues against combining cribriform and fused gland into CGPs to summarize high-grade patterns.

Wait Times and Survival in Lung Cancer Patients across the Province of Quebec, Canada.

Lung cancer is the leading cause of cancer death worldwide, with a five-year survival of 22% in Canada. Guidelines recommend rapid evaluation of patients with suspected lung cancer, but the impact on survival remains unclear. We reviewed medical records of all patients with newly diagnosed lung cancer in four hospital networks across the province of Quebec, Canada, between 1 February and 30 April 2017. Patients were followed for 3 years. Wait times for diagnosis and treatment were collected, and survival analysis using a Cox regression model was conducted. We included 1309 patients, of whom 39% had stage IV non-small cell lung cancer (NSCLC). Median wait times were, in general, significantly shorter in patients with stage III-IV NSCLC or SCLC. Surgery was associated with delays compared to other types of treatments. Median survival was 12.9 (11.1-15.7) months. The multivariate survival model included age, female sex, performance status, histology and stage, treatment, and the time interval between diagnosis and treatment. Longer wait times had a slightly protective to neutral effect on survival, but this was not significant in the stage I-II NSCLC subgroup. Wait times for the diagnosis and treatment of lung cancer were generally within targets. The shorter wait times observed for advanced NSCLC and SCLC might indicate a tendency for clinicians to act quicker on sicker patients. This study did not demonstrate the detrimental effect of longer wait times on survival.