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Intraoperative frozen section (FS) examination of oncologic surgical specimens is frequently performed to ensure complete surgical resection. Data on the gross evaluation of surgical margins are limited. We recently published a study suggesting the use of a macroscopic 2.0 cm tumor-margin cutoff during intraoperative evaluation to decrease the number of unnecessary FS. This study aimed to validate the safety and the clinical impacts of implementing a 2.0 cm tumor-margin threshold for FS diagnosis in evaluating surgical margins during oncologic lung surgery. This retrospective analysis included patients who underwent lung resection for primary or metastatic neoplasms between 2018 and 2022 at the Institut Universitaire de Cardiologie et de Pneumologie de Québec, following the implementation of this practice. Clinicopathological data were retrieved from the medical files. Univariate and multivariate analyses were used to identify the variables associated with positive margins. This study included 1575 tumors in 1299 patients. FS evaluations were performed in 24.4% of patients. No positive margins were observed when the tumor-margin distance was >2.0 cm. The incidence rate of positive margins was 2.95%, with parenchymal margins being the most affected. Multivariate analysis identified the tumor-margin distance as a significant predictor of positive margin status. This practice led to a 79.9% reduction in FS evaluations without compromising the margin assessment accuracy or patient safety. A 2.0 cm tumor-margin distance threshold for intraoperative FS evaluation in oncologic lung surgery is safe and effective in reducing unnecessary FS evaluations while maintaining accurate margin assessments., Competing Interests: Conflicts of Interest and Source of Funding: The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Objectives: Radiomics can predict patient outcomes by automatically extracting a large number of features from medical images. This study is aimed to investigate the sensitivity of radiomics features extracted from 2 different pipelines, namely, Pyradiomics and RaCat, as well as the impact of gray-level discretization on the discovery of immune checkpoint inhibitors (ICIs) biomarkers., Methods: A retrospective cohort of 164 non-small cell lung cancer patients administered with ICIs was used in this study. Radiomic features were extracted from the pre-treatment CT scans. Univariate models were used to assess the association of common radiomics features between 2 libraries with progression-free survival (PFS), programmed death ligand 1 (PD-L1), and tumour infiltrating lymphocytes (CD8 counts). In addition, we also examined the impact of gray-level discretization incorporated in Pyradiomics on the robustness of features across various clinical endpoints., Results: We extracted 1224, 441 radiomic features using Pyradiomics and RaCat, respectively. Among these, 75 features were found to be common between the 2 libraries. Our analysis revealed that the directionality of association between radiomic features and clinical endpoints is highly dependent on the library. Notably, a larger number of Pyradiomics features were statistically associated with PFS, whereas RaCat features showed a stronger association with PD-L1 expression. Furthermore, intensity-based features were found to have a consistent association with clinical endpoints regardless of the gray-level discretization parameters in Pyradiomics-extracted features., Conclusions: This study highlights the heterogeneity of radiomics libraries and the gray-level discretization parameters that will impact the feature selection and predictive model development for biomarkers. Importantly, our work highlights the significance of standardizing radiomic features to facilitate translational studies that use imaging as an endpoint., Advances in Knowledge: Our study emphasizes the need to select stable CT-derived handcrafted features to build immunotherapy biomarkers, which is a necessary precursor for multi-institutional validation of imaging biomarkers., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Institute of Radiology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: Circulating tumor DNA (ctDNA) positivity at diagnosis, which is associated with worse outcomes in multiple solid tumors including stage I-III non-small cell lung cancer (NSCLC), may have utility to guide (neo)adjuvant therapy., Methods: In this retrospective study, 260 patients with clinical stage I NSCLC (180 adenocarcinoma, 80 squamous cell carcinoma) were allocated (2:1) to high- and low-risk groups based on relapse versus disease-free status ≤5 years post-surgery. We evaluated the association of preoperative ctDNA detection by a plasma-only targeted methylation-based multi-cancer early detection (MCED) test with NSCLC relapse ≤5 years post-surgery in the overall population, followed by histology-specific subgroup analyses., Results: Across clinical stage I patients, preoperative ctDNA detection did not associate with relapse within 5 years post-surgery. Sub-analyses confined to lung adenocarcinoma suggested a histology-specific association between ctDNA detection and outcome. In this group, ctDNA positivity tended to associate with relapse within 2 years, suggesting prognostic implications of MCED test positivity may be histology- and time-dependent in stage I NSCLC. Preoperative ctDNA detection was associated with upstaging of clinical stage I to pathological stage II-III NSCLC., Conclusions: Our findings suggest preoperative ctDNA detection in patients with resectable clinical stage I NSCLC using MCED, a pan-cancer screening test developed for use in an asymptomatic population, has no detectable prognostic value for relapse ≤5 years post-surgery. MCED detection may be associated with early adenocarcinoma relapse and increased pathological upstaging rates in stage I NSCLC. However, given the exploratory nature of these findings, independent validation is required., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Y. Bossé has received grants (paid to institution) from AstraZeneca and the IUCPQ foundation. A. Dasgupta is a full-time employee of and owns stock in AstraZeneca. M. Abadier is a full-time employee of and owns stock in Moderna Therapeutics. V. Guthrie is a full-time employee of and owns stock in Exact Sciences. F. Song is a full-time employee of and owns stock in AstraZeneca. F.C. Lamaze has a patent pending related to cancer diagnosis (U.S. Application No. 17/612,695; filed November 19, 2021). C. Melton is a full-time employee of GRAIL, LLC and owns stock in Illumina, Inc. T. Nance is a full-time employee of GRAIL, LLC. T. Hung is a full-time employee of GRAIL, LLC and owns stock in Illumina, Inc. D. Hodgson is a full-time employee of and owns stock in AstraZeneca. C. Abbosh is a full-time employee of and owns stock in AstraZeneca, and holds patents in MRD detection (ES2913468T3 and US20200248266A1). P. Joubert has received honoraria and research grants from AstraZeneca. The remaining authors declare no conflict of interest., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

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., (© 2024. The Author(s).)

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., (© 2023. The Author(s).)

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., Competing Interests: Conflicts of Interest and Source of Funding: Supported by the IUCPQ Foundation owing to a generous donation from Normand Lord, President & CEO, St-Augustin Canada Electric. This work was also supported by the IUCPQ Biobank of the Quebec Respiratory Health Research Network. Y.B. holds a Canada Research Chair in Genomics of Heart and Lung Diseases. P.J. is the recipient of a Junior 2 Clinical Research Scholar Award from the Fonds de recherche Québec—Santé (FRQS), and a member of the “cancer-environment-genetics” axis of the Quebec Respiratory Health Research Network. For the remaining authors none were declared., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.)

Single-cell technologies have revealed the complexity of the tumour immune microenvironment with unparalleled resolution 1-9 . 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-mm 2 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., (© 2023. The Author(s).)

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., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

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., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

Objectives: Venous thrombotic events (VTEs) are a frequent complication of non-small cell lung cancer (NSCLC) and are associated with increased morbidity. Immune checkpoint inhibitors (ICIs) are revolutionizing the management of NSCLC, but little is known about their impact on thrombosis. This study aims to define the incidence and clinical relevance of VTEs in NSCLC patients receiving these treatments., Methods: A retrospective multicentric cohort study including 593 patients from three centers in Canada and France was performed. The cumulative incidence of VTEs after ICIs was estimated using competing risk analysis, and the association of these events with survival and response to treatment was determined. Finally, univariate and multivariate tests were performed to identify VTE risk factors., Results: The cumulative incidence of VTEs in the cohort was 14.8% (95% CI = 7.4-22.2%) for an incidence rate of 76.5 (95% CI = 59.9-97.8) thrombosis per 1000 person-years, with most thromboses occurring rapidly after treatment initiation. VTEs were not correlated with overall survival, progression-free survival, or objective response to ICIs. Age ˂ 65 years old (HR = 2.00; 95% CI = 1.11-3.59) and tumors with PD-L1 1-49% (HR = 3.36; 95% CI = 1.19-9.50) or PD-L1 ≥ 50% (HR = 3.22; 95% CI = 1.21-8.57) were associated with more VTEs after 12 months of ICI initiation. Also, a delay of less than 12 months from diagnosis to the first ICI treatment (HR = 2.06; 95% CI = 1.09-3.89) and active smoking (HR = 2.00; 95% CI = 1.12-3.58) are probable risk factors of VTEs., Conclusion: This study suggests that the incidence of VTEs in NSCLC patients treated with ICIs is comparable to what is reported in other cohorts of patients treated with chemotherapy. In our cohort, VTEs were not associated with a decreased survival or response to therapy. Patient age < 65 and tumors with PD-L1 ≥ 1% were associated with a higher risk of VTEs under ICIs., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Lung carcinoid tumors are classified as either typical or atypical based on the presence of necrosis and the maximum mitotic count per 2 mm 2 area. Determining the mitotic count, which is manually conducted on slides stained with hematoxylin and eosin (HE), is time-consuming and subject to high interobserver variability. The objective of this study was to test the sensitivity and specificity of a surrogate mitosis marker, phospho-histone-H3 (PHH3) immunostaining, in the processing of pulmonary carcinoids as compared with the standard HE evaluation. Carcinoid tissue blocks that were available from lung resection specimens were analyzed using HE and PHH3 stains. Two thoracic pathologists and two residents determined the mitotic count on HE and PHH3 stains in accordance with the 2015 WHO guidelines and recorded the time required to complete this task. For both methods, the interobserver agreement among raters for the mitotic count/2 mm 2 was assessed by conducting intraclass correlation analyses. We found that for both pathologists and residents, the time required to determine the mitotic count using the PHH3 method was reduced compared with the traditional HE method. Furthermore, residents detected more mitoses/2 mm 2 using the PHH3 stain compared with the HE method. More importantly, the PHH3 method yielded better interobserver agreement than the HE method in terms of mitoses/mm 2 detection. Overall, our data confirmed that histologic assessments of carcinoid tumors using PHH3 staining provides practical benefits in terms of scoring times, mitosis detection, and reproducibility of mitotic counts. In addition, we found that the benefit was even greater for less experienced pathologists., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Pulmonary neuroendocrine neoplasms are classified by WHO as either typical or atypical carcinoids, large cell (LCNEC) or small cell (SCLC) neuroendocrine carcinoma based on mitotic count, morphology, and necrosis assessment. LCNEC with low mitotic count and sharing morphologic features with carcinoids are in a gray zone for classification and their rare prevalence and the paucity of studies precludes proper validation of the current grading system. In this study, we aim to investigate their clinicopathological and transcriptomic profiles. Lung resection specimens obtained from 18 patients diagnosed with carcinoids or LCNEC were selected. Four of them were characterized as borderline tumors based on a mitotic rate ranging between 10 and 30 mitoses per 2 mm 2 . Comprehensive morphological and immunohistochemical (IHC) evaluation was performed and tumor-based transcriptomic profiles were analyzed through unsupervised clustering. Clustering analysis revealed two distinct molecular groups characterized by low (C1) and high (C2) proliferation. C1 was comprised of seven carcinoids and three borderline tumors, while C2 was comprised of seven LCNEC and one borderline tumor. Furthermore, patients in cluster C1 had a better recurrence-free survival compared with patients in cluster C2 (20% vs 75%). Histological features, IHC profile, and molecular analysis showed that three out of four borderline tumors showed features consistent with carcinoids. Therefore, our findings convey that the current diagnostic guidelines are suboptimal for classification of pulmonary neuroendocrine tumors with increased proliferative index and carcinoid-like morphology. These results support the emerging concept that neuroendocrine tumors with carcinoid-like features and mitotic count of <20 mitoses per 2 mm 2 should be regarded as pulmonary carcinoids instead of LCNEC.

Objectives: PD-L1 staining assessed by immunohistochemistry (IHC) is a predictive biomarker used to select advanced stage non-small cell lung carcinoma (NSCLC) patients who are likely to respond to PD-1/PD-L1 inhibitors. Cytology specimens represent a significant percentage of the diagnostic samples and additional data are required to show that they provide reliable PD-L1 results when compared to tissue specimens. We aimed to compare PD-L1 staining obtained from patient-matched tissue and cytology specimens. We also want to assess the feasibility of PD-L1 testing on cell blocks with two assays by evaluating the intra- and inter-observer agreement and the level of difficulty for determining the percentage of stained tumor cells (TPS)., Materials and Methods: Forty-six patients with NSCLC were selected. Each patient provided a surgical specimen and a cytology sample (cell block) and/or a biopsy at diagnosis. PD-L1 staining using Agilent PD-L1 IHC 28-8 pharmDx and VENTANA PD-L1 (SP263) assays was evaluated by four pathologists using the TPS. Sixty slides were rescored to document intra-observer agreement. Pathologists were asked to score the level of difficulty for evaluating PD-L1 TPS for each slide. Fleiss's and Cohen's kappas (k) were used to assess the agreement between paired specimens as well as intra- and inter-observer agreement., Results: The concordance in PD-L1 TPS between cell blocks and surgical specimens (k varying from 0.56 to 0.82) or biopsies (k from 0.43 to 0.81) was moderate to substantial, depending on the cut-off. On cell blocks, inter-observer agreement was substantial (k from 0.74 to 0.82) and intra-observer agreement was almost perfect (k from 0.85 to 0.93). The perceived difficulty of PD-L1 evaluation of cell blocks was not different from surgical specimens but more difficult than biopsy samples., Conclusion: PD-L1 TPS was concordant between cell blocks and tissue specimens, mainly at 10, 25 and 50 % cut-offs. PD-L1 evaluation on cell blocks was feasible and reproducible between different observers and assays., Competing Interests: Declaration of Competing Interest Dr Tsao reports personal fees from Merck during the conduct of the study. He also reports personal fees from BMS, Bayer, Takeda and Amgen as well as grants from Bayer outside the submitted work.The other authors (A.G., M.O, D.I., D.J. and P.J.) declare no conflict of interest., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Background: Age-related immune remodelling is thought to be associated with resistance to immune checkpoint inhibitors (ICIs) in cancer. Patients older than 70 years, representing >50% of the population with non-small cell lung cancer (NSCLC) according to SEER database, are underrepresented in clinical trials exploring ICIs. The objective of this study was to determine if patients with NSCLC older than ≥70 years had inferior clinical outcomes with ICIs., Methods: We conducted a retrospective analysis of 381 patients treated with anti-PD-(L)1 ICI for advanced NSCLC at the Dijon Cancer Center (n = 177), University of Montreal Hospital (n = 106) and Quebec Heart and Lung Institute (n = 98). Age was considered as a categorical variable. Patients' baseline characteristics were compared using the Chi-squared test. Survival curves were estimated by the Kaplan-Meier method and compared with the Log-rank test in a univariate analysis. Multivariate cox regression model was used to determine hazard ratios and 95% confidence intervals for progression-free survival (PFS) and overall survival (OS) between the groups, adjusting for other clinicopathologic features., Results: Among 381 patients included, 335 (88%) received ICI after platinum chemotherapy. The median age was 66 (range 37-89) and 33% were older than 70 years of age. Considering age as a categorical variable, differences in age were not associated with PFS or OS. Subgroup analysis and multivariate cox regression did not reveal significant interaction of age with outcomes. ECOG performance status was the only significant factor in the three cohorts., Conclusions: Unlike previously described in the era of chemotherapy, age was not associated with outcomes in NSCLC patients treated with ICI., Competing Interests: Declaration of Competing Interest All authors and co-authors declare no conflict of interest., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Introduction: Molecules targeting programmed cell death 1 or its ligand programmed death ligand 1 (PD-L1) revolutionized the treatment of patients with NSCLC. The only approved biomarker for predicting treatment response is the PD-L1 tumor proportion score (TPS) determined by immunohistochemistry. According to International Association for the Study of Lung Cancer recommendations, specimens that include fewer than 100 tumor cells or are older than 3 years should not be used for PD-L1 testing and the reliability of cell blocks has yet to be validated., Methods: This retrospective study included 1249 consecutive patients with NSCLC who were tested for PD-L1 (using the clone 22C3) between September 2016 and April 2017. The associations between the presence of suboptimal characteristics (specimens with <100 tumor cells, specimens older than 3 years, or cell blocks) and PD-L1 TPS were examined by using a multinomial logistic regression., Results: Specimens from 35.5% of the patients had at least one suboptimal characteristic. For patients with a PD-L1 TPS of higher than 50%, there was a significantly higher probability that they had a specimen with more than 100 tumor cells (OR = 1.97, p = 0.008) and a more recent block (within 30 days versus after >3 years) (OR = 2.46, p = 0.023). There was no statistical difference in PD-L1 TPS between cell blocks and tissue specimens (biopsy OR = 0.99 [p = 0.996] and surgery OR = 0.73 [p = 0.302])., Conclusions: Our results suggest that specimens containing fewer than 100 tumor cells or older than 3 years may lead to an underestimation of PD-L1 status. Our findings also provide support for the use of cell blocks for PD-L1 testing, although further research is needed., (Copyright © 2019 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.)

Because of a lack of official guidelines, systematic use of intraoperative frozen section for the evaluation of surgical margins in lung oncology constitutes standard practice in many pathology departments. This costly and time-consuming procedure seems unjustified as reported rates of positive margins remain low. We aimed to evaluate clinicopathologic criteria associated with positive margins and establish evidence-based recommendations regarding the use of frozen sections. This retrospective cohort included 1903 consecutive patients with a lung resection for malignant neoplasm between 2006 and 2015. Clinicopathologic data were retrieved from medical files. Univariate and multivariate analyses were used to identify variables associated with a positive margin. Receiver operating characteristic curves and a probability table of positive margins based on tumor-margin distance were created. Our results were confirmed in a validation cohort of 27 patients with positive margins. The rate of positive margins was 3.8%. A positive margin status changed the surgical management in 48.6% of patients. A short macroscopic tumor-margin distance was associated with a higher risk of positive bronchovascular and parenchymal margins in univariate and multivariate analyses. Selecting a 2.0 cm tumor-margin distance cut-off for performing a frozen section would result in a 55.3% reduction of intraoperative evaluations, with a risk of missing a positive margin of 0.61%. Overall, we showed that systematic use of frozen section for intraoperative evaluation of surgical margins is unnecessary. A better selection of patients with a higher risk of a positive margin can be achieved with tumor-margin distance as a simple gross evaluation parameter.