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Introduction: The aim of this study was to validate the discriminatory ability and clinical utility of the N descriptor of the newly proposed ninth edition of the TNM staging system for lung cancer in a large independent cohort., Methods: We retrospectively analyzed patients who underwent curative surgery for NSCLC between January 2004 and December 2019. The N descriptor of patients included in this study was retrospectively reclassified based on the ninth edition of the TNM classification. Survival analysis was performed using the log-rank test and Cox proportional hazard model to compare adjacent N categories., Results: A total of 6649 patients were included in this study. The median follow-up period was 54 months. According to the newly proposed ninth edition N classification, 5573 patients (83.8%), 639 patients (9.6%), 268 patients (4.0%), and 169 patients (2.5%) were classified into the clinical N0, N1, N2a, and N2b categories and 4957 patients (74.6%), 744 patients (11.2%), 567 patients (8.5%), and 381 patients (5.7%) were classified into the pathologic N0, N1, N2a, and N2b categories, respectively. The prognostic differences between all adjacent clinical and pathologic N categories were highly significant in terms of both overall survival and recurrence-free survival., Conclusions: We validated the clinical utility of the newly proposed ninth edition N classification for both clinical and pathologic stages in NSCLC. The new N classification revealed clear prognostic separation between all categories (N0, N1, N2a, and N2b) in terms of both overall survival and recurrence-free survival., Competing Interests: Disclosure The authors declare no confict of interest., (Copyright © 2024 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.)

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., Competing Interests: Disclosure Dr. Donington reports receiving personal fees from Amgen, AstraZeneca, Bristol Myers Squibb, Merck & Co., Inc., and Genentech, Inc./F. Hoffmann-La Roche Ltd., outside the submitted work. Dr. Joubert reports grants from AstraZeneca, Roche, Merck and Biomark Signature Inc. and personal fees from AstraZeneca and Merck, outside the submitted work. Dr. Mino-Kenudson reports receiving personal fees from AstraZeneca, Pfizer, Repare, Sanofi, AbbVie, Daiichi Sankyo, Boehringer Ingelheim, and Elsevier, outside the submitted work. Dr. Nishimura reports receiving grants from IASLC, outside the submitted work. Dr. Nicholson reports receiving personal fees from Merck, Boehringer Ingelheim, Novartis, AstraZeneca, Bristol Myers Squibb, Roche, AbbVie, Oncologica, UpToDate, the European Society of Oncology, Liberum, Takeda UK, and Sanofi and grants and personal fees from Pfizer, outside the submitted work. Dr. Papotti reports receiving personal fees from Roche, Eli Lilly, AstraZeneca, and Pfizer, outside the submitted work. Dr. Ugalde Figueroa reports receiving personal fees from AstraZeneca, Bristol, Roche, Medtronic, and Johnson & Johnson, outside the submitted work. Dr. Van Schil reports receiving personal fees from AstraZeneca, Bristol Myers Squibb, Merck Sharp & Dohme, Roche, and Janssen, outside the submitted work; and serving as President of International Association for the Study of Lung Cancer and Treasurer of Belgian Association for Cardiothoracic Surgery. Dr. Yang reports being on advisory boards for AstraZeneca and Genentech. He has also received honorarium from AstraZeneca, outside the submitted work. Dr. Lievens reports financing from the EU ImmunoSABR project, personal fees from the UpLung project, Astra-Zeneca, outside the submitted work., (Copyright © 2024. Published by Elsevier Inc.)

Introduction: The TNM classification of lung cancer is periodically revised. The International Association for the Study of Lung Cancer collected and analyzed a new database to inform the forthcoming ninth edition of the TNM classification. The results are herewith presented., Methods: After exclusions, 76,518 patients from a total of 124,581 registered patients were available for analyses: 58,193 with clinical stage, 39,192 with pathologic stage, and 62,611 with best stage NSCLC. The proposed new N2 subcategories (N2a, involvement of single ipsilateral mediastinal or subcarinal nodal station, and N2b, involvement of multiple ipsilateral mediastinal nodal stations with or without involvement of the subcarinal nodal station) and the new M1c subcategories (M1c1, multiple extrathoracic metastases in one organ system, and M1c2, multiple extrathoracic metastases in multiple organ systems) were considered in the survival analyses. Several potential stage groupings were evaluated, using multiple analyses, including recursive partitioning, assessment of homogeneity within and discrimination between potential groups, clinical and statistical significance of survival differences, multivariable regression, and broad assessment of generalizability., Results: T1N1, T1N2a, and T3N2a subgroups are assigned to IIA, IIB, and IIIA stage groups, respectively. T2aN2b and T2bN2b subgroups are assigned to IIIB. M1c1 and M1c2 remain in stage group IVB. Analyses reveal consistent ordering, discrimination of prognosis, and broad generalizability of the proposed ninth edition stage classification of lung cancer., Conclusions: The proposed stages for the ninth edition TNM improve the granularity of nomenclature about anatomic extent that has benefits as treatment approaches become increasingly differentiated and complex., Competing Interests: Disclosure Dr. Nishimura and Ms. Giroux report that statistical consulting by Cancer Research And Biostatistics (CRAB) employees for data collection, analysis, and manuscript preparation in the Staging Project is funded by the International Association for the Study of Lung Cancer (IASLC). Dr. Fong reports payments made to his institution by Competitive Research Grant, MRFF EPCDR Improving Diagnosis in Cancers Low Survival Rates; Competitive Research Grant, NHMRC Ideas grant: Early lung cancer biomarkers; Competitive Research Grant, MRFF Next Generation Clinical Researchers Program - Practitioner Fellowship; Competitive Research Grant, NHMRC Project: Novel biomarkers for lung cancer; Competitive Research Grant, NHMRC Low dose computed tomography (LDCT) to diagnose lung cancer; payments to himself by UpToDate Reviewer and Cochrane Clinical Answers Reviewer; payment for travel support to WCLC, ATS in 2022 and PCCP in 2023; payments for travel support by the 2023 Asia Pacific Coalition Against Lung Cancer; and payments from the Asia Pacific Society of Respirology former President and EXCO member; support with loan bronchoscopes for the purpose of a research study—no financial funding from Olympus; and software licensing for Computer Aided Diagnosis research in the International Lung Screen Trial (ILST) from Mevis Veolity. Dr. Giuliani reports receiving payments for Advisory Board member from AstraZeneca and Bristol Myers Squibb; and being Section Editor, of the International Journal of Radiation Oncology, Biology and Physics. Dr. Marom reports receiving honorarium for lectures from Boehringer Ingelheim, Merck Sharp & Dohme, and AstraZeneca. Dr. Nicholson reports receiving personal fees from Merck, Boehringer Ingelheim, Novartis, AstraZeneca, Bristol-Myers Squibb, Roche, AbbVie, Oncologica, UpToDate, the European Society of Oncology, and Liberum; and grants and personal fees from Pfizer, outside of the submitted work. Dr. Van Schil reports receiving lecture and consulting institutional fees from AstraZeneca and Janssen and lecture and consulting personal fees from Bristol Myers Squibb, Merck Sharp & Dohme, and Roche; and having leadership positions in BACTS (Belgian Association for Cardiothoracic Surgery) as treasurer and IASLC (International Association for the Study of Lung Cancer) as president with no fees. Dr. Travis reports receiving a grant from the National Cancer Institute: MSKCC Core Grant: P30 CA008748. Dr. Rusch reports receiving support for the present manuscript from NIH Cancer Support Grant P30-CA008748; institutional funding for a clinical trail from Genentech; and miscellaneous honoraria for lectures of no commercial interest at various universities; funding for meeting preparation and attendance by NIH Thoracic Malignancy Steering Committee (Co-Chair); and reports being a committee member of MARS II and RAMON trials (Cancer Research UK). Dr. Asamura reports receiving funding for analyses of results and manuscript preparation and support from the IASLC for attending meetings. The remaining authors report no conflict of interest., (Copyright © 2024 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.)

Introduction: This study analyzed all metastatic categories of the current TNM classification of NSCLC to propose modifications of the M component in the next edition (ninth) of the classification., Methods: A database of 124,581 patients diagnosed between 2011 and 2019 was established; of these, 14,937 with NSCLC in stages IVA to IVB were available for this analysis. Overall survival was calculated using the Kaplan-Meier method, and prognosis was assessed using multivariable-adjusted Cox proportional hazards regression., Results: The eighth edition M categories revealed good discrimination in the ninth edition data set. Assessments revealed that an increasing number of metastatic lesions were associated with decreasing prognosis; because this seems to be a continuum and adjustment for confounders was not possible, no specific lesion number was deemed appropriate for stage classification. Among tumors involving multiple metastases, decreasing prognosis was found with an increasing number of organ systems involved. Multiple assessments, including after adjustment for potential confounders, revealed that M1c patients who had metastases to a single extrathoracic organ system were prognostically distinct from M1c patients who had involvement of multiple extrathoracic organ systems., Conclusions: These data validate the eighth edition M1a and M1b categories, which are recommended to be maintained. We propose the M1c category be divided into M1c1 (involvement of a single extrathoracic organ system) and M1c2 (involvement of multiple extrathoracic organ systems)., Competing Interests: Disclosure Dr. Fong reports receiving grants and contracts from MRFF EPCDR, NHMRC Ideas Grant, MRFF Next Generation Clinical Researchers Program, NHMRC; receiving funds from UpToDate as reviewer and Cochrane Clinical Answers as reviewer; and receiving equipment support from Olympus and Mevis Veolity. Mr. Rosenthal reports receiving fees for service from IASLC to complete work associated with publication. Ms. Giroux reports receiving fees for service from IASLC to complete work associated with publication. Dr. Nishimura reports receiving fees for service from IASLC to complete work associated with publication. Dr. Erasmus reports receiving honoraria from Canadian Association of Radiologists. Dr. Lievens reports receiving grants and contracts for the ImmunoSABR EU Project, HERO-VBHC chair; consulting fees from AstraZeneca; and honoraria from Merck Sharp & Dohme. Dr. Marom reports receiving honoraria from Boehringer Ingelheim, Merck, and AstraZeneca. Dr. Putora reports receiving grants or contracts from Bayer, Takeda, and AstraZeneca. Dr. Suárez reports receiving consulting fees from AstraZeneca and Roche; honoraria from AstraZeneca. The remaining authors declare no conflict of interest., (Copyright © 2024 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.)

Introduction: The accurate assessment of nodal (N) status is crucial to the management and prognostication of nonmetastatic NSCLC. We sought to determine whether the current N descriptors should be maintained or revised for the upcoming ninth edition of the international TNM lung cancer staging system., Methods: Data were assembled by the International Association for the Study of Lung Cancer on patients with NSCLC, detailing both clinical and pathologic N status, with information about anatomical location and individual station-level identification. Survival was calculated by the Kaplan-Meier method and prognostic groups were assessed by a Cox regression analysis., Results: Data for clinical N and pathologic N status were available in 45,032 and 35,009 patients, respectively. The current N0 to N3 descriptors for both clinical N and pathologic N categories reflect prognostically distinct groups. Furthermore, single-station N2 involvement (N2a) exhibited a better prognosis than multistation N2 involvement (N2b) in both clinical and pathologic classifications, and the differences between all neighboring nodal subcategories were highly significant. The prognostic differences between N2a and N2b were robust and consistent across resection status, histologic type, T category, and geographic region., Conclusions: The current N descriptors should be maintained, with the addition of new subdescriptors to N2 for single-station involvement (N2a) and multiple-station involvement (N2b)., (Copyright © 2023 International Association for the Study of Lung Cancer. All rights reserved.)

Identifying the lung carcinoma subtype in small biopsy specimens is an important part of determining a suitable treatment plan but is often challenging without the help of special and/or immunohistochemical stains. Pathology image analysis that tackles this issue would be helpful for diagnoses and subtyping of lung carcinoma. In this study, we developed AI models to classify multinomial patterns of lung carcinoma; ADC, LCNEC, SCC, SCLC, and non-neoplastic lung tissue based on convolutional neural networks (CNN or ConvNet). Four CNNs that were pre-trained using transfer learning and one CNN built from scratch were used to classify patch images from pathology whole-slide images (WSIs). We first evaluated the diagnostic performance of each model in the test sets. The Xception model and the CNN built from scratch both achieved the highest performance with a macro average AUC of 0.90. The CNN built from scratch model obtained a macro average AUC of 0.97 on the dataset of four classes excluding LCNEC, and 0.95 on the dataset of three subtypes of lung carcinomas; NSCLC, SCLC, and non-tumor, respectively. Of particular note is that the relatively simple CNN built from scratch may be an approach for pathological image analysis., (© 2022. The Author(s).)

Purpose: To clarify the correlation between KIF11 (kinesin family member 11) and clinicopathologic characteristics of non-small cell lung cancer (NSCLC) and identify the prognostic value of KIF11 in patients with NSCLC., Methods: For investigating the expression of KIF11 in NSCLC, two tissue microarrays (TMAs: one contained 60 paired NSCLC tissues and paratumor tissues, the other contained 140 NSCLC tissues and 10 normal lung tissues) were constructed, stained, and scored. The Cancer Genome Atlas (TCGA) datasets were used to explore the differential expression level of KIF11 between NSCLC and paratumor. Kaplan-Meier survival curves were plotted and multivariate analysis were carried out., Results: The staining of KIF11 mainly distributed throughout the cytoplasm of tumor cells. Its expression was higher in NSCLC than paratumor cells, and similar results were obtained from TCGA datasets. We found that high expression of KIF11 had a significant correlation with lymph node metastases ( p = 0.024) and pathologic stage ( p = 0.018); that significant difference was not found in any other clinicopathologic characteristic. As univariate and multivariate analysis showed, KIF11 expression was significantly correlated with overall survival time of NSCLC ( p = 0.002, p = 0.025, respectively). High KIF11 expression was found to significantly associate with overall survival of stage II-III ( p = 0.001) and lung adenocarcinoma ( p = 0.036)., Conclusion: High KIF11 expression predicts poor outcome in NSCLC. KIF11 is expected to be a viable prognostic biomarker for NSCLC.

Purpose: Most cancer-related deaths worldwide are associated with lung cancer. Subtyping of non-small cell lung cancer (NSCLC) into adenocarcinoma (AC) and squamous cell carcinoma (SqCC) is of importance, as therapy regimes differ. However, conventional staining and immunohistochemistry have their limitations. Therefore, a spatial metabolomics approach was aimed to detect differences between subtypes and to discriminate tumor and stroma regions in tissues., Methods: Fresh-frozen NSCLC tissues (n = 35) were analyzed by matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI) of small molecules (< m/z 1000). Measured samples were subsequently stained and histopathologically examined. A differentiation of subtypes and a discrimination of tumor and stroma regions was performed by receiver operating characteristic analysis and machine learning algorithms., Results: Histology-guided spatial metabolomics revealed differences between AC and SqCC and between NSCLC tumor and tumor microenvironment. A diagnostic ability of 0.95 was achieved for the discrimination of AC and SqCC. Metabolomic contrast to the tumor microenvironment was revealed with an area under the curve of 0.96 due to differences in phospholipid profile. Furthermore, the detection of NSCLC with rarely arising mutations of the isocitrate dehydrogenase (IDH) gene was demonstrated through 45 times enhanced oncometabolite levels., Conclusion: MALDI-MSI of small molecules can contribute to NSCLC subtyping. Measurements can be performed intraoperatively on a single tissue section to support currently available approaches. Moreover, the technique can be beneficial in screening of IDH-mutants for the characterization of these seldom cases promoting the development of treatment strategies., (© 2021. The Author(s).)

Histological stratification in metastatic non-small cell lung cancer (NSCLC) is essential to properly guide therapy. Morphological evaluation remains the basis for subtyping and is completed by additional immunohistochemistry labelling to confirm the diagnosis, which delays molecular analysis and utilises precious sample. Therefore, we tested the capacity of convolutional neural networks (CNNs) to classify NSCLC based on pathologic HES diagnostic biopsies. The model was estimated with a learning cohort of 132 NSCLC patients and validated on an external validation cohort of 65 NSCLC patients. Based on image patches, a CNN using InceptionV3 architecture was trained and optimized to classify NSCLC between squamous and non-squamous subtypes. Accuracies of 0.99, 0.87, 0.85, 0.85 was reached in the training, validation and test sets and in the external validation cohort. At the patient level, the CNN model showed a capacity to predict the tumour histology with accuracy of 0.73 and 0.78 in the learning and external validation cohorts respectively. Selecting tumour area using virtual tissue micro-array improved prediction, with accuracy of 0.82 in the external validation cohort. This study underlines the capacity of CNN to predict NSCLC subtype with good accuracy and to be applied to small pathologic samples without annotation., (© 2021. The Author(s).)

The IASLC lymph node map grouped the lymph node stations into "zones" for prognostic analyses. In the N1 lymph nodes group, N1 nodes are divided into the Hilar/Interlobar zone (N1h) and Peripheral zone (N1p). There is no consensus on the different prognostic values of N1 lymph nodes in N1h and N1p. Therefore, we conducted a systematic review and meta-analysis to assess the survival difference between N1h and N1p in patients of pN1M0 NSCLC. Medline, the Cochrane Library, Embase, and the Web of science were systematically searched to identify relevant studies published up to April 4th, 2020. A retrospective and prospective cohort study comparing N1h versus N1p to the pN1M0 NSCLC was included. Hazard ratios (HRs) for OS were aggregated according to a fixed or random-effect model. Ten publications for 1946 patients of pN1M0 NSCLC were included for the meta-analysis.The 5-year OS was lower for patients with N1h (HR: 1.67, 95% CI 1.44-1.94; P < 0.001). The pooled 5-year OS in N1h and N1p were 40% and 56%, respectively. The patients in pN1M0 NSCLC have different survival according to different N1 lymph node zones involvement: patients with N1p metastasis have a better prognosis than those with N1h metastasis., (© 2021. The Author(s).)

Background: The current nodal classification is unsatisfactory in distinguishing the prognostically heterogeneous N1 or N2 non-small cell lung cancer (NSCLC)., Research Question: Is the combination of the current N category and the number of metastatic lymph nodes (N-#number) or the combination of the current N category and the ratio of the number of positive to resected lymph nodes (N-#ratio) better than the current N category alone?, Study Design and Methods: We identified 2,162 patients with N1 or N2 NSCLC from the Surveillance, Epidemiology, and End Results database (2004-2016). We classified these patients into three N-#number categories (N-#number-1, N-#number-2a, N-#number-2b) and three N-#ratio categories (N-#ratio-1, N-#ratio-2a, N-#ratio-2b). Lung cancer-specific survival (LCSS) were compared using the Kaplan-Meier method. The prognostic significance of the new nodal classifications was validated across each tumor size category (≤3 cm, 3-5 cm, 5-7cm, >7 cm). Cox proportional hazards regression was used to evaluate the association between each nodal classification and LCSS., Results: The survival curves showed clear differences between each pair of N-#number and N-#ratio categories. A significant tendency toward the deterioration of LCSS from N-#number-1 to N-#number-2b was observed in all tumor size categories. However, the differences between each pair of N-#ratio categories were significant only in tumors from 3 to 7 cm. Although all three nodal classifications were independent prognostic indicators, the N-#number classification provided more accurate prognostic stratifications compared with the N-#ratio classification and the current nodal classification., Interpretation: The N-#number classification followed by the N-#ratio classification might be better prognostic determinants than the current nodal classification in prognostically heterogeneous N1 or N2 NSCLC., (Copyright © 2021 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.)

Survival from lung cancer remains low, yet is the most common cancer diagnosed worldwide. With survival contrasting between the main histological groupings, small-cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), it is important to assess the extent that geographical differences could be from varying proportions of cancers with unspecified histology across countries. Lung cancer cases diagnosed 2010-2014, followed until 31 December 2015 were provided by cancer registries from seven countries for the ICBP SURVMARK-2 project. Multiple imputation was used to reassign cases with unspecified histology into SCLC, NSCLC and other. One-year and three-year age-standardised net survival were estimated by histology, sex, age group and country. In all, 404 617 lung cancer cases were included, of which 47 533 (11.7%) and 262 040 (64.8%) were SCLC and NSCLC. The proportion of unspecified cases varied, from 11.2% (Denmark) to 29.0% (The United Kingdom). After imputation with unspecified histology, survival variations remained: 1-year SCLC survival ranged from 28.0% (New Zealand) to 35.6% (Australia) NSCLC survival from 39.4% (The United Kingdom) to 49.5% (Australia). The largest survival change after imputation was for 1-year NSCLC (4.9 percentage point decrease). Similar variations were observed for 3-year survival. The oldest age group had lowest survival and largest decline after imputation. International variations in SCLC and NSCLC survival are only partially attributable to differences in the distribution of unspecified histology. While it is important that registries and clinicians aim to improve completeness in classifying cancers, it is likely that other factors play a larger role, including underlying risk factors, stage, comorbidity and care management which warrants investigation., (© 2021 UICC.)

Objective: To explore the data characteristics of tongue and pulse of non-small-cell lung cancer with Qi deficiency syndrome and Yin deficiency syndrome, establish syndrome classification model based on data of tongue and pulse by using machine learning methods, and evaluate the feasibility of syndrome classification based on data of tongue and pulse., Methods: We collected tongue and pulse of non-small-cell lung cancer patients with Qi deficiency syndrome ( n = 163), patients with Yin deficiency syndrome ( n = 174), and healthy controls ( n = 185) using intelligent tongue diagnosis analysis instrument and pulse diagnosis analysis instrument, respectively. We described the characteristics and examined the correlation of data of tongue and pulse. Four machine learning methods, namely, random forest, logistic regression, support vector machine, and neural network, were used to establish the classification models based on symptom, tongue and pulse, and symptom and tongue and pulse, respectively., Results: Significant difference indices of tongue diagnosis between Qi deficiency syndrome and Yin deficiency syndrome were TB-a, TB-S, TB-Cr, TC-a, TC-S, TC-Cr, perAll, and the tongue coating texture indices including TC-CON, TC-ASM, TC-MEAN, and TC-ENT. Significant difference indices of pulse diagnosis were t 4 and t 5 . The classification performance of each model based on different datasets was as follows: tongue and pulse < symptom < symptom and tongue and pulse. The neural network model had a better classification performance for symptom and tongue and pulse datasets, with an area under the ROC curves and accuracy rate which were 0.9401 and 0.8806., Conclusions: It was feasible to use tongue data and pulse data as one of the objective diagnostic basis in Qi deficiency syndrome and Yin deficiency syndrome of non-small-cell lung cancer., Competing Interests: The authors declare that they have no competing interests., (Copyright © 2021 Yu-lin Shi et al.)

Introduction: The International Association for the Study of Lung Cancer has proposed a new classification of N descriptor based on the number of metastatic lymph nodes (LNs) stations, including skip metastasis. The aim of the study was to determine the effect of removed LNs on the adequacy of this new classification., Materials and Methods: The material was collected retrospectively based on the database of the Polish Lung Cancer Group, including information on 8016 patients with non-small cell lung cancer operated in 23 thoracic surgery centers in Poland. The material covered the period from January 2005 to September 2015. We divided patients into two groups: ≤6LNs and >6LNs removed., Results: In the whole group, an average of 13.4 nodes and 4.54 nodal stations were removed. 5-year survivals in the >6LNs group vs ≤ 6LNs group were: 62.3% and 55.1% (N0), 44.5% and 35.9% (N1a), 34.1% and 31,7% (N1b), 37.3% and 26.3% (N2a1), 32.4% and 26.7% (N2a2), 29.4% and 29.2% (N2b1), and 22.0% and 23.0% (N2b2), respectively. Comparing these groups, we detected significant differences at N0 (p < 0.001) and N2a1 (p = 0.022). In the ≤6LNs group, the survival curves for N2a1, N2a2, N2b1, and N2b2 overlapped (p > 0.05). In the >6LNs group, the survival curves were significantly different between grades, with survival for N2a1 better than N1b (p = 0.232)., Conclusion: The proposed classification N descriptor is potentially better at differentiating patients into different stages. The accuracy of the classification depends on the number of lymph nodes removed. Therefore, the extent of lymphadenectomy has a significant impact on the staging of surgically-treated lung cancer., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Background: The eighth edition of nodal classification for non-small cell lung cancer (NSCLC) is defined only by the anatomical location of metastatic lymph nodes., Research Question: We sought to evaluate the prognostic significance and discriminatory capability of the number of involved nodal stations (nS) in a large Chinese cohort., Study Design and Methods: A total of 4,011 patients with NSCLC undergoing surgical resection between 2009 and 2013 were identified. The optimal cutoff values for nS classification were determined with X-tile software. Kaplan-Meier and multivariate Cox analysis were used to examine the prognostic performance of nS classification in comparison with location-based N classification. A decision curve analysis was performed to evaluate the standardized net benefit of nS classification in predicting prognosis., Results: All the patients were classified into four prognostically different subgroups according to the number of involved nodal stations: (1) nS0 (none positive), (2) nS1 (one involved station), (3) nS2 (two involved stations), and (4) nS ≥ 3 (three or more involved stations). The prognoses among all the neighboring categories of nS classification were statistically significantly different in terms of disease-free survival and overall survival. The multivariate Cox analysis demonstrated that nS was an independent prognostic factor of disease-free survival and overall survival. Patients with N1 or N2 stage disease could be divided into three prognostically different subgroups according to nS classification. However, the prognosis was similar between the N1 and N2 subgroups when patients were staged in the same nS category. The decision curve analysis showed that nS classification tended to have a higher predictive capability than location-based N classification., Interpretation: The nS classification could be used to provide a more accurate prognosis for patients with resected NSCLC. The nS is worth taking into consideration when defining nodal category in the forthcoming ninth edition of the staging system., (Copyright © 2020 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.)

Background: Programmed cell death-ligand 1 (PD-L1) protein expression is one of the most extensively studied biomarkers in patients with non-small cell lung cancer (NSCLC). However, there is scarce information regarding its association with distinct adenocarcinoma subtypes. This study evaluated the frequency of PD-L1 expression according to the IASLC/ATS/ERS classification and other relevant histological and clinical features. Patients and Methods: PD-L1 expression was assessed by immunohistochemistry (IHC). According to its positivity in tumor cells membrane, we stratified patients in three different tumor proportions score (TPS) cut-off points: a) <1% (negative), b) between 1 and 49%, and c) ≥50%; afterward, we analyzed the association among PD-L1 expression and lung adenocarcinoma (LADC) predominant subtypes, as well as other clinical features. As an exploratory outcome we evaluated if a PD-L1 TPS score ≥15% was useful as a biomarker for determining survival. Results: A total of 240 patients were included to our final analysis. Median age at diagnosis was 65 years (range 23-94 years). A PD-L1 TPS ≥1% was observed in 52.5% of the entire cohort; regarding specific predominant histological patterns, a PD-L1 TPS ≥1 was documented in 31.2% of patients with predominant-lepidic pattern, 46.2% of patients with predominant-acinar pattern, 42.8% of patients with a predominant-papillary pattern, and 68.7% of patients with predominant-solid pattern ( p = 0.002). On the other hand, proportion of tumors with PD-L1 TPS ≥50% was not significantly different among adenocarcinoma subtypes. At the univariate survival analysis, a PD-L1 TPS cut-off value of ≥15% was associated with a worse PFS and OS. Conclusion: According to IASLC/ATS/ERS lung adenocarcinoma classification, the predominant-solid pattern is associated with a higher proportion of PD-L1 positive samples, no subtype was identified to be associated with a high (≥50%) TPS PD-L1., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Cruz-Rico, Avilés-Salas, Popa-Navarro, Lara-Mejía, Catalán, Sánchez-Reyes, López-Sánchez, Cabrera-Miranda, Aquiles Maldonado-Martínez, Samtani-Bassarmal and Arrieta.)

The aim of this study was to assess the relationship of cluster of differentiation 117 (CD117) expression with the clinicopathological characteristics and the prognosis in patients with non-small cell lung cancer (NSCLC). No meta-analysis concerning the correlation of CD117 expression with clinical and prognostic values of the patients with NSCLC is reported. A systematic literature search was conducted to achieve eligible studies. The combined odds ratios (ORs) or hazard ratios (HRs: multivariate Cox analysis) with their 95% confidence intervals (CIs) were calculated in this analysis. Final 17 eligible studies with 4,893 NSCLC patients using immunohistochemical detection were included in this meta-analysis. CD117 expression was not correlated with gender (male vs. female), clinical stage (stages 3-4 vs. stages 1-2), tumor grade (grade 3 vs. grades 1-2), T-stage (T-stages 3-4 vs. T-stages 0-2), distal metastasis, and disease-free survival (DFS) of NSCLC (all p values >0.05). CD117 expression was associated with lymph node metastasis (positive vs. negative: OR = 0.74, 95% CI = 0.56-0.97, p = 0.03), histological type (adenocarcinoma (AC) versus squamous cell carcinoma (SCC): OR = 1.74, 95% CI = 1.26-2.39, p = 0.001), and a worse overall survival (OS) (HR = 1.89, 95% CI = 1.22-2.92, p = 0.004). The expression of CD117 was significantly higher in AC than in SCC. CD117 may be an independent prognostic indicator for worse OS in NSCLC., (© 2021 S. Karger AG, Basel.)

Objective: The carcinogenesis role of PARP1 in lung cancer is still not clear. Analysis at allelic levels cannot fully explain the function of PARP1 on lung cancer. Our study aims to further explore the relation between PARP1 haplotypes and lung cancer., Materials and Methods: DNA and RNA were extracted from non-small cell lung cancer (NSCLC) tumor and adjacent normal fresh frozen tissue. Five PARP1-SNPs were genotyped and PARP1-specific SNPs were imputed using IMPUTE and SHAPEIT software. The SNPs were subjected to allelic, haplotype and SNP-SNP interaction analyses. Correlation between SNPs and mRNA/protein expressions were performed., Results: SNP imputation inferred the ungenotyped SNPs and increased the power for association analysis. Tumor tissue samples are more likely to carry rs1805414 (OR = 1.85; 95% CI: 1.12-3.06; P-value: 0.017) and rs1805404 (OR = 2.74; 95%CI 1.19-6.32; P-value: 0.015) compared to normal tissues. Our study is the first study to show that haplotypes comprising of 5 SNPs on PARP1 (rs1136410, rs3219073, rs1805414, rs1805404, rs1805415) is able to differentiate the NSCLC tumor from normal tissues. Interaction between rs3219073, rs1805415, and rs1805414 were significantly associated with the NSCLC tumor with OR ranging from 3.61-6.75; 95%CI from 1.82 to 19.9; P-value<0.001., Conclusion: PARP1 haplotypes may serve as a better predictor in lung cancer development and prognosis compared to single alleles., Competing Interests: The authors have declared that no competing interests exist.

Accurately classifying patients with non-small cell lung cancer (NSCLC) from the perspective of tumor evolution has not been systematically studied to date. Here, we reconstructed phylogenetic relationships of somatic mutations in 100 early NSCLC patients (327 lesions) through reanalyzing the TRACERx data. Based on the genomic evolutionary patterns presented on the phylogenetic trees, we grouped NSCLC patients into three evolutionary subtypes. The phylogenetic trees among three subtypes exhibited distinct branching structures, with one subtype representing branched evolution and another reflecting the early accumulation of genomic variation. However, in the evolutionary pattern of the third subtype, some mutations experienced selective sweeps and were gradually replaced by multiple newly formed subclonal populations. The subtype patients with poor prognosis had higher intra-tumor heterogeneity and subclonal diversity. We combined genomic heterogeneity with clinical phenotypes analysis and found that subclonal expansion results in the progression and deterioration of the tumor. The molecular mechanisms of subtype-specific Early Driver Feature (EDF) genes differed across the evolutionary subtypes, reflecting the characteristics of the subtype itself. In summary, our study provided new insights on the stratification of NSCLC patients based on genomic evolution that can be valuable for us to understand the development of pulmonary tumor profoundly., (© 2020 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.)

Durvalumab is a selective, high-affinity human immunoglobulin monoclonal antibody in a class called check point inhibitors, that blocks PD-L1 on tumour cells. Despite clinical success in increasing progression-free survival rates in patients with stage III non-small-cell lung cancer, durvalumab has been associated with immune-related side effects such as pneumonitis and colitis. We present a case of an 84-year-old woman with acral vasculitis presenting as blue toe syndrome, associated with prolonged use of durvalumab. After 1 year of fortnightly durvalumab therapy postchemoradiation therapy, the patient came in with a left blue big toe, and later developed bilateral livedo racemosa. The diagnosis of durvalumab-associated vasculitis was made and treatment with prednisolone was started with clinical improvement., Competing Interests: Competing interests: None declared., (© BMJ Publishing Group Limited 2020. No commercial re-use. See rights and permissions. Published by BMJ.)

Purpose: SMARCA4 mutations are among the most common recurrent alterations in non-small cell lung cancer (NSCLC), but the relationship to other genomic abnormalities and clinical impact has not been established., Experimental Design: To characterize SMARCA4 alterations in NSCLC, we analyzed the genomic, protein expression, and clinical outcome data of patients with SMARCA4 alterations treated at Memorial Sloan Kettering., Results: In 4,813 tumors from patients with NSCLC, we identified 8% ( n = 407) of patients with SMARCA4 -mutant lung cancer. We describe two categories of SMARCA4 mutations: class 1 mutations (truncating mutations, fusions, and homozygous deletion) and class 2 mutations (missense mutations). Protein expression loss was associated with class 1 mutation (81% vs. 0%, P < 0.001). Both classes of mutation co-occurred more frequently with KRAS, STK11 , and KEAP1 mutations compared with SMARCA4 wild-type tumors ( P < 0.001). In patients with metastatic NSCLC, SMARCA4 alterations were associated with shorter overall survival, with class 1 alterations associated with shortest survival times ( P < 0.001). Conversely, we found that treatment with immune checkpoint inhibitors (ICI) was associated with improved outcomes in patients with SMARCA4 -mutant tumors ( P = 0.01), with class 1 mutations having the best response to ICIs ( P = 0.027)., Conclusions: SMARCA4 alterations can be divided into two clinically relevant genomic classes associated with differential protein expression as well as distinct prognostic and treatment implications. Both classes co-occur with KEAP1, STK11 , and KRAS mutations, but individually represent independent predictors of poor prognosis. Despite association with poor outcomes, SMARCA4 -mutant lung cancers may be more sensitive to immunotherapy., (©2020 American Association for Cancer Research.)

Background: Distinguishing adenocarcinoma and squamous cell carcinoma subtypes of non-small cell lung cancers is critical to patient care. Preoperative minimally-invasive biopsy techniques, such as fine needle aspiration (FNA), are increasingly used for lung cancer diagnosis and subtyping. Yet, histologic distinction of lung cancer subtypes in FNA material can be challenging. Here, we evaluated the usefulness of desorption electrospray ionization mass spectrometry imaging (DESI-MSI) to diagnose and differentiate lung cancer subtypes in tissues and FNA samples., Methods: DESI-MSI was used to analyze 22 normal, 26 adenocarcinoma, and 25 squamous cell carcinoma lung tissues. Mass spectra obtained from the tissue sections were used to generate and validate statistical classifiers for lung cancer diagnosis and subtyping. Classifiers were then tested on DESI-MSI data collected from 16 clinical FNA samples prospectively collected from 8 patients undergoing interventional radiology guided FNA., Results: Various metabolites and lipid species were detected in the mass spectra obtained from lung tissues. The classifiers generated from tissue sections yielded 100% accuracy, 100% sensitivity, and 100% specificity for lung cancer diagnosis, and 73.5% accuracy for lung cancer subtyping for the training set of tissues, per-patient. On the validation set of tissues, 100% accuracy for lung cancer diagnosis and 94.1% accuracy for lung cancer subtyping were achieved. When tested on the FNA samples, 100% diagnostic accuracy and 87.5% accuracy on subtyping were achieved per-slide., Conclusions: DESI-MSI can be useful as an ancillary technique to conventional cytopathology for diagnosis and subtyping of non-small cell lung cancers., (© American Association for Clinical Chemistry 2020. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

In the 21st century, there has been a dramatic shift in the management of advanced-stage lung carcinoma, and this has coincided with an increasing use of minimally invasive tissue acquisition methods. Both have had significant downstream effects on cytology and small biopsy specimens. Current treatments require morphologic, immunohistochemical, and/or genotypical subtyping of non-small cell lung carcinoma. To meet these objectives, standardized classification of cytology and small specimen diagnoses, immunohistochemical algorithms, and predictive biomarker testing guidelines have been developed. This review provides an overview of current classification, biomarker testing, methods of small specimen acquisition and triage, clinical management strategies, and emerging technologies., (Copyright © 2020 American Society of Cytopathology. Published by Elsevier Inc. All rights reserved.)

Lung cancer is generally treated with conventional therapies, including chemotherapy and radiation. These methods, however, are not specific to cancer cells and instead attack every cell present, including normal cells. Personalized therapies provide more efficient treatment options as they target the individual's genetic makeup. The goal of this study was to identify the frequency of causal genetic mutations across a variety of lung cancer subtypes in the earlier stages. 833 samples of non-small cell lung cancer from 799 patients who received resection of their lung cancer, were selected for molecular analysis of six known mutations, including EGFR, KRAS, BRAF, PIK3CA, HER2 and ALK. A SNaPshot assay was used for point mutations and fragment analysis searched for insertions and deletions. ALK was evaluated by IHC +/- FISH. Statistical analysis was performed to determine correlations between molecular and clinical/pathological patient data. None of the tested variants were identified in most (66.15%) of cases. The observed frequencies among the total samples vs. only the adenocarcinoma cases were notable different, with the highest frequency being the KRAS mutation (24.49% vs. 35.55%), followed by EGFR (6.96% vs. 10.23%), PIK3CA (1.20% vs. 0.9%), BRAF (1.08% vs. 1.62%), ALK (0.12% vs. 0.18%), while the lowest was the HER2 mutation (0% for both). The statistical analysis yielded correlations between presence of a mutation with gender, cancer type, vascular invasion and smoking history. The outcome of this study will provide data that helps stratify patient prognosis and supports development of more precise treatments, resulting in improved outcomes for future lung cancer patients., Competing Interests: The study was partially supported by Roche Canada, Pfizer Canada and Boehringer Ingelheim Canada. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The funding provided by the pharmaceutical companies were helping us for validation of the tests and not related to employment, consultancy, patents, products in development, marketed products, and any commercial purposes. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Background/aim: Proliferation biomarkers such as MIB-1 are strong predictors of clinical outcome and response to therapy in patients with non-small-cell lung cancer, but they require histological examination. In this work, we present a classification model to predict MIB-1 expression based on clinical parameters from positron emission tomography., Patients and Methods: We retrospectively evaluated 78 patients with histology-proven non-small-cell lung cancer (NSCLC) who underwent 18 F-FDG-PET/CT for clinical examination. We stratified the population into a low and high proliferation group using MIB-1=25% as cut-off value. We built a predictive model based on binary classification trees to estimate the group label from the maximum standardized uptake value (SUV max ) and lesion diameter., Results: The proposed model showed ability to predict the correct proliferation group with overall accuracy >82% (78% and 86% for the low- and high-proliferation group, respectively)., Conclusion: Our results indicate that radiotracer activity evaluated via SUV max and lesion diameter are correlated with tumour proliferation index MIB-1., (Copyright© 2020, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.)

Background: Exosomal microRNAs (miRNAs) are proposed to be excellent candidate biomarkers for clinical applications. However, little is known about their potential value as diagnostic biomarkers for metastatic non-small cell lung cancer (NSCLC)., Methods: In this study, microarrays were used to determine distinct miRNA profiles of plasma exosomes in a discovery cohort of healthy donors, metastatic NSCLC and nonmetastatic NSCLC patients. Three potential candidate miRNAs were selected based on the differential expression profiles. The discovery set data were validated by quantitative real-time polymerase chain reaction using a validation cohort., Results: NSCLC patients (n = 80) and healthy controls (n = 30) had different exosome-related miRNA profiles in plasma. Results demonstrated that the level of let-7f-5p was decreased in plasma exosomes of NSCLC patients (p < 0.0001). Further analysis of three differentially expressed miRNAs revealed that miR-320a, miR-622 and let-7f-5p levels could significantly segregate patients with metastatic NSCLC from patients with nonmetastatic NSCLC (p < 0.0001, p < 0.0001 and p = 0.023, respectively). In addition, the combination of let-7f-5p, CEA and Cyfra21-1 generated an area under the curve (AUC) of 0.981 for the diagnosis of NSCLC patients, and the combination of miR-320a, miR-622, CEA and Cyfra21-1 had an AUC of 0.900 for the diagnosis of patients with metastatic NSCLC., Conclusions: This novel study demonstrated that plasma exosomal miRNAs are promising noninvasive diagnostic biomarkers for metastatic NSCLC.

The development of multigene classifiers for cancer prognosis, treatment prediction, molecular subtypes or clinicopathological groups has been a cornerstone in transcriptomic analyses of human malignancies for nearly two decades. However, many reported classifiers are critically limited by different preprocessing needs like normalization and data centering. In response, a new breed of classifiers, single sample predictors (SSPs), has emerged. SSPs classify samples in an N-of-1 fashion, relying on, e.g. gene rules comparing expression values within a sample. To date, several methods have been reported, but there is a lack of head-to-head performance comparison for typical cancer classification problems, representing an unmet methodological need in cancer bioinformatics. To resolve this need, we performed an evaluation of two SSPs [k-top-scoring pair classifier (kTSP) and absolute intrinsic molecular subtyping (AIMS)] for two case examples of different magnitude of difficulty in non-small cell lung cancer: gene expression-based classification of (i) tumor histology and (ii) molecular subtype. Through the analysis of ~2000 lung cancer samples for each case example (n = 1918 and n = 2106, respectively), we compared the performance of the methods for different sample compositions, training data set sizes, gene expression platforms and gene rule selections. Three main conclusions are drawn from the comparisons: both methods are platform independent, they select largely overlapping gene rules associated with actual underlying tumor biology and, for large training data sets, they behave interchangeably performance-wise. While SSPs like AIMS and kTSP offer new possibilities to move gene expression signatures/predictors closer to a clinical context, they are still importantly limited by the difficultness of the classification problem at hand., (© The Author(s) 2019. Published by Oxford University Press.)

Long noncoding RNAs (lncRNAs) are emerging as critical regulators of gene expression and they play fundamental roles in immune regulation. Here we introduce an integrated algorithm, ImmLnc, for identifying lncRNA regulators of immune-related pathways. We comprehensively chart the landscape of lncRNA regulation in the immunome across 33 cancer types and show that cancers with similar tissue origin are likely to share lncRNA immune regulators. Moreover, the immune-related lncRNAs are likely to show expression perturbation in cancer and are significantly correlated with immune cell infiltration. ImmLnc can help prioritize cancer-related lncRNAs and further identify three molecular subtypes (proliferative, intermediate, and immunological) of non-small cell lung cancer. These subtypes are characterized by differences in mutation burden, immune cell infiltration, expression of immunomodulatory genes, response to chemotherapy, and prognosis. In summary, the ImmLnc pipeline and the resulting data serve as a valuable resource for understanding lncRNA function and to advance identification of immunotherapy targets.

Aims: Accurate assessment of programmed death-ligand 1 (PD-L1) levels in non-small cell lung carcinoma (NSCLC) samples is complicated by intratumoral heterogeneity. We aimed to: (i) establish whether intratumoral PD-L1 variation is associated with differences in local histotype; (ii) identify histotypes associated with a tendency for there to be higher or lower PD-L1 scores; and (iii) estimate the frequency of clinically significant discordance in PD-L1 levels between intratumoral histotype areas., Methods and Results: We reviewed 166 NSCLC resection specimens clinically tested for PD-L1 with the 22C3 pharmDx assay. Multiple histotypes were present in 55% (68/123) of non-mucinous adenocarcinoma samples. Solid histotypes had significantly higher PD-L1 levels than other histotypes, both when samples were grouped by predominant histotype, and when histotype areas within a tumour were compared (P < 0.02). Lepidic areas had significantly lower PD-L1 levels than other histotype areas within the same tumour (P < 0.02). Discordance between intratumoral histotype areas at a clinically relevant threshold (PD-L1 tumour proportion score of 1% or 50%) was present in 32% (22/68) of non-mucinous adenocarcinoma specimens with multiple histotype areas. The lepidic histotype was most frequently involved in discordance., Conclusions: Intratumoral heterogeneity in PD-L1 is associated with variation in histotype. Over-representation of solid areas may increase the PD-L1 score assigned to a tumour, whereas over-representation of lepidic areas may decrease the PD-L1 score. Evaluation of how histotype representation impacts on the predictive value of PD-L1 testing is warranted., (© 2019 John Wiley & Sons Ltd.)

In the past decade, research efforts were made to identify molecular biomarkers useful as therapeutic targets in Non-Small Cell Lung Cancer (NSCLC), the most frequent type of lung carcinoma. NSCLC presents different histological subtypes being the most prevalent LUSC (Lung Squamous Cell Cancer) and LUAD (Lung Adenocarcinoma), and only a subset of LUAD patients' present tumors expressing known targetable genetic alterations. Telomeres and its components, including telomerase, the enzyme that replenishes telomeres, have been considered potential cancer biomarkers due to their crucial role in cell proliferation and genome stability. Our study aims to quantify expression changes affecting telomere-associated genes and ncRNAs associated with telomere regulation and maintenance in NSCLC. We first assessed the transcriptome (RNA-Seq) data of NSCLC patients from The Cancer Genome Atlas (TCGA) and then we tested the expression of telomere-associated genes and telomeric ncRNAs (TERC, telomerase RNA component, and TERRA, telomere repeat-containing RNA) in Brazilian NCSLC patient samples by quantitative RT-PCR, using matched normal adjacent tissue samples as the control. We also estimated the mean size of terminal restriction fragments (TRF) of some Brazilian NSCLC patients using telomeric Southern blot. The TCGA analysis identified alterations in the expression profile of TERT and telomere damage repair genes, mainly in the LUSC subtype. The study of Brazilian NSCLC samples by RT-qPCR showed that LUSC and LUAD express high amounts of TERT and that although the mean TRF size of tumor samples was shorter compared to normal cells, telomeres in NSCLC are probably maintained by telomerase. Also, the expression analysis of Brazilian NSCLC samples identified statistically significant alterations in the expression of genes involved with telomere damage repair, as well as in TERC and TERRA, mainly in the LUSC subtype. We, therefore, concluded that telomere maintenance genes are significantly deregulated in NSCLC, representing potential biomarkers in the LUSC subtype., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Background: We evaluated the prognostic impact of the presence of a ground-glass opacity (GGO) component on thin-section computed tomography for the refined clinical T classification of multiple primary lung cancers., Methods: We reviewed 272 surgically resected, clinically node-negative multiple lung cancers. Dominant tumors were classified into 2 groups based on the presence of a GGO component; that is, a GGO tumor (consolidation tumor ratio, 0 to <1.0) or pure-solid (PS) tumor (consolidation tumor ratio, 1.0). Furthermore, multifocal GGOs (MFGGOs) were defined as lesions showing a GGO component for all tumors. Their prognoses were evaluated using Cox proportional hazard model., Results: There were 153 MFGGOs (56%) with a significantly better 5-year overall survival than non-MFGGOs (97.2% vs 68.5%, P < .001). A multivariable analysis revealed that MFGGO and absence of nodal involvement were independently significant prognosticators of better survival (P = .007 and P = .012, respectively). Furthermore, among the patients of non-MFGGO groups, multivariate analysis showed that a PS + PS pattern and presence of nodal involvement were independently significant prognosticators of poorer survival (P = .008 and P = .001, respectively). We divided the tumors into 3 groups based on the results and focusing on the presence of a GGO; that is, MFGGO (n = 153), PS + additional GGO (n = 81), and PS + PS (n = 38). The 5-year overall survival was clearly split among them: MFGGO, 97.2%; PS + additional GGO, 82.1%; and PS + PS, 41.3% (P < .001)., Conclusions: Our results suggest that presence of a GGO component has the ability to distinguish the survival even for multiple lung cancers. Further investigations including multicenter trials are certainly warranted to address the revision of T variable of multiple lung cancers considering a presence of GGO component., (Copyright © 2020 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.)

Objectives: To evaluate the diagnostic accuracy of radiomics method and frozen sections (FS) for the pathological classification of peripheral lung adenocarcinoma manifesting as ground-glass nodules (GGNs) in computer tomography (CT)., Materials and Methods: A dataset of 831 peripheral lung adenocarcinoma manifesting as GGNs in CT were divided into two cohorts: training cohort (n = 581) and validation cohort (n = 250). Combined with clinical features, the radiomics classifier was trained and validated to distinguish the pathological classification of these nodules. FS diagnoses in the validation cohort were collected. Diagnostic performance of the FS and radiomics methods was compared in the validation cohort. The predictive factors for the misdiagnosis of FS were determined via univariate and multivariate analyses., Results: The accuracy of radiomics method in the training and validation cohorts was 72.5 % and 68.8 % respectively. The overall accuracy of FS in the validation cohort was 70.0 %. The concordance rate between FS and final pathology when FS had a different diagnosis from radiomics classifier was significantly lower than when FS had the same diagnosis as radiomics classifier (46 vs. 87 %, P < 0.001). Univariate and Multivariate analyses showed that different diagnosis between FS and radiomics classifier was the independent predictive factor for the misdiagnosis of FS (OR: 7.46; 95%CI: 4.00-13.91; P < 0.001)., Conclusions: Radiomics classifier predictions may be a reliable reference for the classification of peripheral lung adenocarcinoma manifesting as GGNs when FS cannot provide a timely diagnosis. Intraoperative diagnoses of the cases where FS had a different diagnosis from radiomics method should be considered cautiously due to the higher misdiagnosis rate., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Introduction: Lung carcinoma is the most commonly cancer causing deaths throughout the world that mainly occurs due to smoking. Small cell lung cancer and Non-small cell lung cancer (NSCLC) are the two different types of Lung cancer. For the detection and classification of lung cancer, there are different techniques in the literature., Methods: This paper emphasis on the three class classification of the Adenocarcinomas, Squamous cell carcinomas, and large cell carcinomas of NSCLC. For precise and superior results, Computer Aided Detection (CADe) system has been designed so that the radiologist can diagnose carcinoma in the ultrasonic images conveniently. CADe analyses the quality of the images, selects the region of interest, preprocesses the data, extracts the features and classifies the cancer., Results: After exhaustive literature survey, Laws' mask features and SVM classifier with Gaussian RBF kernels have been used in this paper. The experimentation was performed on 92 images using 50% - 50% training and testing criteria., Conclusion: Comparative study reveals that our system for separating three class lung cancer provides 95.65% average accuracy for Laws' mask 3 dimensions using the SVM classifier that is maximum among the existing methods reported in the literature using the same dataset., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Introduction: 85% of lung cancers are non-small cell carcinomas (NSCLC), the majority of which are diagnosed in an advanced stage. Immunotherapy has changed the treatment pattern for these tumors and created the need to find a marker for patient selection. Although not ideal, PD-L1 is the biomarker currently used in clinical practice., Material and Methods: Retrospective review by two pathologists of 53 cases of NSCLC from 2005 to 2007 in Hospital Universitario La Paz, using the WHO 2015 classification studying PD-L1 with clones 22C3 and 28-8. The consistency between observers and clones was assessed and all data studied were correlated with survival rates., Results: We found a prevalence of PD-L1 expression in tumor cells (TC) similar to that previously reported in the literature and a very good consistency between clones in the evaluation of TC and immune cells (ICC 0.99-0.93, p<.001). Interobserver concordance was very good in the evaluation of TC (ICC 0.902, 95% CI: 0.836-0.942, p<.001 for clone 22C3 and ICC 0.927, 95% CI: 0.877-0.957, p<.001 for clone 28-8) and poor for immune cells (ICC of 0.413, 95% CI: 0.163-0.613, p=.001 with clone 22C3 and ICC of 0.313, 95% CI: 0.053-0.534, p=.010 with clone 28-8). Subtype and histological grade were the only variables related to prognosis., Conclusions: The clones of PD-L1 22C3 and 28-8 are equivalent and there is good interobserver consistency in the evaluation of TC but not in immune cells., (Copyright © 2019 Sociedad Española de Anatomía Patológica. Publicado por Elsevier España, S.L.U. All rights reserved.)

Background: Distinction in the mutational profile between the common histological types, lung adenocarcinoma (LUAD) and squamous cell lung carcinoma (LUSC) has been well-established. However, comprehensive mutation profiles of the predominant histological subtypes within LUAD and LUSC remains elusive., Methods: We analyzed the mutational profile of 318 Chinese NSCLC patients of adenocarcinoma and squamous cell carcinoma predominant subtypes from seven hospitals using capture-based ultra-deep sequencing of 68 lung cancer-related genes., Results: Of the 318 NSCLC patients, 215 were diagnosed with LUAD and 103 with LUSC. Adenocarcinoma in situ and acinar adenocarcinoma were the most predominant subtypes of LUAD. On the other hand, keratinizing squamous cell carcinoma was the most predominant subtype of LUSC. Among the LUAD subtypes, EGFR sensitizing mutations were most prevalent in the invasive lepidic subtype. More than half of the patients with preinvasive adenocarcinoma in situ, minimally invasive, acinar, micropapillary and papillary subtypes were also EGFR-mutants. Patients with colloidal, invasive mucinous, and fetal subtypes had the least number of EGFR mutations. Moreover, KRAS mutations were prevalent in patients with invasive mucinous, colloid, enteric and solid subtypes. A total of 90% of the LUSC patients harbor mutations in TP53, wherein all patients except five with nonkeratinizing were TP53 mutants. PIK3CA amplifications were most prevalent in keratinizing, followed by basaloid and nonkeratinizing subtypes., Conclusion: These data suggest that the mutational profiles among the predominant histological subtypes were very distinct, which provided a reliable tool to improve treatment decisions., (© 2019 The Authors. Thoracic Cancer published by China Lung Oncology Group and John Wiley & Sons Australia, Ltd.)

Background: Clinical-pathologic predictors of acquired T790M epidermal growth factor receptor (EGFR) mutation in Caucasian patients with non-small-cell lung cancer (NSCLC) progressing after first-/second-generation tyrosine kinase inhibitors (TKIs) is an open field for research. Similarly, the best time point for T790M detection by liquid or tissue biopsy after disease progression is currently matter of debate., Patients and Methods: This is an observational study at 7 Italian centers enrolling patients with EGFR-mutant NSCLC progressing after first-/second-generation EGFR TKIs, between 2014 and 2018, aiming at comparing baseline clinical-pathologic features and progression patterns in acquired T790M-positive compared with T790M-negative cases., Results: A total of 235 patients received first-line treatment with gefitinib (N = 126; 53%), erlotinib (N = 51; 22%), or afatinib (N = 58; 25%). In 120 (51%) cases, T790M was detected in liquid biopsy, tissue biopsy, or both. Age younger than 65 years (P = .037), the presence of common mutations (P = .004), and better response to first-line TKI (P = .023) were correlated with T790M positivity. T790M detection was associated with higher number of new progressing sites (P = .04), liver progression (P = .002), and a lower frequency of lung metastases (P = .027). When serial liquid biopsies were performed (N = 15), an oligoprogressive disease was correlated with a negative test outcome, whereas systemic progression was observed at the time of T790M positivity., Conclusion: This study on a Caucasian population showed that age, type of EGFR mutation at diagnosis, response to first-line treatment, and peculiar progression pattern are associated with T790M status. Serial liquid biopsy might be useful for treatment selection, especially when tissue rebiopsy is not feasible., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Introduction: Synchronous oligometastatic non-small cell lung cancer (NSCLC) definition varies from 1 metastasis in 1 organ (tumour-node-metastasis 8 [TNM8]), 1-3 metastases (European Society for Medical Oncology [ESMO]), ≤3 metastases (including mediastinal nodes [MLN]) after systemic treatment to 3-5 metastases in ongoing trials. A single definition is however needed to design/compare trials. To assess oligometastatic NSCLC definitions used by clinical experts in daily practice and its evolution, we redistributed a 2012 case-based survey (Dooms, the World Congress of Lung Cancer 2013)., Methods: In December 2017, 10 real-life multidisciplinary team (MDT) discussed patients (good condition, no significant comorbidities, 18 F-fluorodeoxyglucose positron emission tomography/brain magnetic resonance imaging staged, all < 5 metastases, 9/10 ≤ 3 metastases, oncogene-addicted or wild-type) were distributed to 33 international NSCLC experts involved in the European Organisation for Research and Treatment of Cancer oligometastatic NSCLC consensus group, questioning is this oligometastatic disease and if oligometastatic, which treatment proposal. The answers provided in 2017 were compared with the 2012 answers; real-life treatment and survival of the patients was added., Results: Twenty-six of 33 experts (24 centres) replied: 8 medical oncologists, 7 pulmonologists, 7 radiation oncologists, 4 thoracic surgeons. Sixty-two percent of respondents discussed the cases with their MDT. One case had 100% oligometastatic consensus, and 3 cases had >90% consensus; the number of treatment proposals varied between 3 and 8. Radical treatment was more often offered in case of single metastasis or N0. Compared with 2012, there was a trend towards a more conservative oligometastatic definition and chemotherapy was more frequently included in the treatment proposal., Conclusions: Oligometastatic NSCLC definition was conservative. The number of organs, MLN status and radical treatment possibility seem to be components of daily practice oligometastatic definition., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Background The 8th edition of tumor node metastasis (TNM) staging system for lung cancer introduced a revision of M descriptor. The limitation of new classification to predict prognosis is its focus on anatomical extent of the disease only. Information on molecular status of the tumor significantly influences treatment response and survival; however, data addressing this issue is scarce. This report points to the impact of epidermal growth factor receptor (EGFR) mutation in non-small cell lung cancer (NSCLC) patients on survival in view of new M descriptors of TNM classification system. Patients and methods Medical records of 479 consecutive metastatic NSCLC patients treated between 2009 and 2011, all tested for EGFR mutations, were retrospectively reviewed. For 355 patients medical records included sufficient information to be appropriately categorized into one of the new subgroups according to the M descriptor in 8th TNM classification, of those 89 (25.1%) patients harboured EGFR mutations (EGFR-m). Results Median overall survival (mOS) of EGFR-m patients was significantly longer than mOS of patients without EGFR mutations (20.6 months vs. 8.3 months, p < 0.001). Patients with limited disease burden (M1b sub-group) had the longest mOS among EGFR wild type patients (EGFR-wt) and also among EGFR-m patients, 14.4 months and 39.2 month, respectively. In spite of widespread metastatic disease of M1c EGFR-m patients, their mOS (18.8 months) was longer than mOS of oligometastatic EGFR-wt patients (M1b), who had the lowest disease burden (14.4 months). Median follow up was 53.9 months. Conclusions Incorporation of EGFR mutation status in advanced NSCLC further differentiates survival curves of M categories in 8th TNM classification and more precisely predicts survival compared to number of metastasis or number of metastatic sites alone.

Background: The potentially curative and/or palliative therapy for non-resectable lung cancer has evolved significantly over the past 2 decades. With the availability of targeted therapies, the need for precise sub-typing of non-small cell lung carcinoma (NCSLC) has become paramount., Objectives: As there are few data from South Africa, we aimed to determine utility of TTF-1, napsin A, p63 and CK5 immunostaining on fine needle aspiration (FNA) cell block and formalin-fixed paraffin-embedded tissue biopsy specimens in subtyping NSCLC as adenocarcinoma and squamous cell carcinomas., Methods: All cases of NSCLC diagnosed during a 3-year period were retrospectively identified. All FNA biopsy and formalin-fixed paraffin-embedded cases that were stained with TTF-1, napsin A, CK5 and p63 were collected. A lung cancer registry was used to access and correlate clinical and radiological data., Results: We included 271 cases with diagnoses of adenocarcinoma of the lung (n = 201), squamous cell carcinoma of the lung (n = 53), unspecified NSCLC (n = 8) and other carcinomas (n = 9). TTF-1 and napsin A had sensitivities of 99.0% and 91.9%, respectively, positive predictive values (PPVs) of 90.8% and 90.3%, respectively, and accuracies of 91.0% for adenocarcinoma of the lung. Napsin A had a higher specificity than TTF-1 (90.2% vs 62.8%). Both CK5 and P63 had high sensitivities (95.4% and 97.9%, respectively) and negative predictive values of 96.4% and 96.8%, respectively, for squamous cell carcinoma of the lung. CK5 had a higher specificity than p63 (84.4% and 61.2%, respectively), PPV (80.4% and 70.8%, respectively) and accuracy (88.8% and 79.2%, respectively) for squamous cell carcinoma., Conclusion: All four immunostaining methods had high sensitivities. TTF-1 and napsin A both had high PPV and diagnostic accuracy for adenocarcinoma of the lung, whereas CK5 had an equally high PPV and accuracy for squamous cell carcinoma of the lung. The specificity of napsin A for adenocarcinoma was higher than that of TTF-1. The specificity of CK5 for squamous cell carcinoma was higher than p63., (© 2019 John Wiley & Sons Ltd.)

Purpose: In the most recent (eighth) edition of the TNM classification, the clinical T descriptor has been adapted to measure the consolidation size of sub-solid lung cancer. Sub-centimeter non-small cell lung cancer (NSCLC) has thereby been subclassified into three groups: Tis, T1mi, and T1a; however, the revision has not been validated well. Thus, we investigated the clinicopathological characteristics and long-term oncological outcomes of sub-centimeter NSCLCs based on the solid size., Methods: The subjects of this retrospective review were 99 patients who underwent complete resection for NSCLC with ≤ 1 cm in consolidation size on computed tomography (CT). Survival was reanalyzed after reclassification according to the new TNM classification., Results: This cohort consisted of 14 patients with cTis tumors, 18 with cT1mi tumors, and 67 with cT1a tumors. Among the patients with tumors classified as cT1a, two had lymph node metastasis and two had vascular invasion. The cumulative incidences of recurrence at 5 and 10 years were 0% for cTis/cT1mi tumors, and 4.5% and 6.1% for cT1a tumors, respectively., Conclusions: There may be pathological and survival differences between cTis/cT1mi tumors and cT1a tumors, but not between cTis tumors and cT1mi tumors.

Objective: Wearable devices such as a wrist actigraph may have a potential to objectively estimate patients' functioning and may supplement performance status (PS). This proof-of-concept study aimed to evaluate whether actigraphy data are significantly associated with patients' functioning and are predictive of their survival in patients with metastatic non-small cell lung cancer., Method: We collected actigraphy data for a three-day period in ambulatory patients with stage IV non-small cell lung cancer. We computed correlations between actigraphy data (specifically, proportion of time spent immobile while awake) and clinician-rated PS, subjective report of physical activities, quality of life (the Functional Assessment of Cancer Therapy - Trial Outcome Index), and survival., Result: Actigraphy data (the proportion of time awake spent immobile) were significantly correlated with Functional Assessment of Cancer Therapy - Trial Outcome Index (r = -0.53, p < 0.001) and with the Eastern Cooperative Oncology Group PS (ECOG PS) (r = 0.37, p < 0.001). The proportion of time awake spent immobile was significantly associated with worse survival. For each 10% increase in this measure, the hazard ratio (HR) was 1.48 (95% confidence interval [CI95%] = 1.06, 2.06) for overall mortality, and odds ratio was 2.99 (CI95% = 1.27, 7.05) for six-month mortality. ECOG PS was also associated with worse survival (HR = 2.80, CI95% = 1.34, 5.86). Among patients with ECOG PS 0-1, the percentage of time awake spent immobile was significantly associated with worse survival, HR = 1.93 (CI95% = 1.10, 3.42), whereas ECOG PS did not predict survival., Significance of Results: Actigraphy may have potential to predict important clinical outcomes, such as quality of life and survival, and may serve to supplement PS. Further validation study is warranted.

Objectives: To evaluate the performance of using radiomics method to classify lung cancer histological subtypes based on nonenhanced computed tomography images., Materials and Methods: 278 patients with pathologically confirmed lung cancer were collected, including 181 nonsmall cell lung cancer (NSCLC) and 97 small cell lung cancers (SCLC) patients. Among the NSCLC patients, 88 patients were adenocarcinomas (AD) and 93 patients were squamous cell carcinomas (SCC). In total, 1695 quantitative radiomic features (QRF) were calculated from the primary lung cancer tumor in each patient. To build radiomic classification model based on the extracted QRFs, several machine-learning algorithms were applied sequentially. First, unsupervised hierarchical clustering was used to exclude highly correlated QRFs; second, the minimum Redundancy Maximum Relevance feature selection algorithm was employed to select informative and nonredundant QRFs; finally, the Incremental Forward Search and Support Vector Machine classification algorithms were used to combine the selected QRFs and build the model. In our work, to study the phenotypic differences among lung cancer histological subtypes, four classification models were built. They were models of SCLC vs NSCLC, SCLC vs AD, SCLC vs SCC, and AD vs SCC. The performance of the classification models was evaluated by the area under the receiver operating characteristic curve (AUC) estimated by three-fold cross-validation., Results: The AUC (95% confidence interval) for the model of SCLC vs NSCLC was 0.741(0.678, 0.795). For the models of SCLC vs AD and SCLC vs SCC, the AUCs were 0.822(0.755, 0.875) and 0.665(0.583, 0.738), respectively. The AUC for the model of AD vs SCC was 0.655(0.570, 0.731). Several QRFs ("Law&#95;15," "LoG&#95;Uniformity," "GLCM&#95;Contrast," and "Compactness Factor") that characterize tumor heterogeneity and shape were selected as the significant features to build the models., Conclusion: Our results show that phenotypic differences exist among different lung cancer histological subtypes on nonenhanced computed tomography image., (Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Introduction: Patients with pulmonary large-cell carcinoma (LCC) have poor prognosis and limited treatment options. The identification of clinically actionable molecular alterations helps to guide personalized cancer treatment decisions., Patients and Methods: A consecutive cohort of 789 resected NSCLC cases were reviewed. Fifty-nine NSCLC cases lacking morphologic differentiation, accounting for 7.5% of all resected NSCLCs, were identified and further characterized by immunohistochemistry according to the 2015 WHO lung tumor classification. Molecular alterations were investigated by multiple technologies including target capture sequencing, immunohistochemistry, and fluorescence in situ hybridizations., Results: Of 59 NSCLC cases lacking morphologic differentiation, 20 (33.9%) were reclassified as adenocarcinoma (LCC-AD), 14 (23.7%) as squamous cell carcinoma (LCC-SqCC), and 25 (42.4%) as LCC-Null. Approximately 92% of LCC-Null, 95% of LCC-AD, and 86% of LCC-SqCC harbored clinically relevant alterations. Alterations characteristic of adenocarcinoma (EGFR, KRAS, ALK receptor tyrosine kinase [ALK], ROS1, and serine/threonine kinase 11 [STK11]) were detected in the LCC-AD subgroup but not in LCC-SqCC, whereas squamous-lineage alterations (phosphatidylinositol-4,5-biphosphate 3-kinase catalytic subunit alpha [PIK3CA], SRY-box 2 [SOX2], fibroblast growth factor receptor 1 [FGFR1], and AKT1) were detected in the LCC-SqCC subgroup but not in the LCC-AD group. Although some LCC-Null tumors displayed a genetic profile similar to either adenocarcinoma or squamous-cell carcinoma, more than half of the LCC-Null group were completely devoid of recognizable lineage-specific genetic profiles. High programmed death ligand 1 expression and high frequency of cell cycle regulatory gene alterations were found in the LCC-Null group offering alternative options of targeted therapy., Conclusions: This comprehensive molecular study provided further insight into the genetic architecture of LCC. The presence of clinically actionable alterations in a majority of the tumors allowed personalized treatment to emerge., (Copyright © 2019 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.)

Introduction: Despite adoption of molecular biomarkers in the management of NSCLC, the recently adopted eighth edition of the TNM staging system utilized only clinicopathologic characteristics and validated improvement in risk stratification of early-stage disease has remained elusive. We therefore evaluated the integration of a clinically validated molecular prognostic classifier into conventional staging., Methods: A novel staging system, the TNMB (with the B denoting biology) system, which integrates a 14-gene molecular prognostic classifier into the eighth edition of the TNM staging system, was developed by using data from 321 patients with NSCLC at the University of California, San Francisco. The TNMB staging system was subsequently validated in an independent, multicenter cohort of 1373 patients, and its implementation was compared with adoption of the seventh and eighth edition staging systems utilizing metrics of reclassification., Results: Compared with staging according to the eighth edition of the TNM system, the TNMB staging system enhanced the identification of high-risk patients, with a net reclassification improvement of 0.33 (95% confidence interval [CI]: 0.24-0.41). It better predicted differences in survival, with a relative integrated discrimination improvement of 22.1% (95% CI: 8.8%-35.3%), and it improved agreement between observed and predicted survival, with a decrease in the reclassification calibration statistic of from 39 to 21. The seventh and eighth editions failed to change the net reclassification improvement (0.01 [95% CI: -0.04 to 0.03] and 0.03 [95% CI: 0.00 to 0.06], respectively) or relative integrated discrimination improvement (2.1% [95% CI: -5.8 to 9.9] and -2.5% [95% CI: -17.6 to 12.4], respectively); in addition, the eighth edition worsened calibration, with an increase in the reclassification calibration statistic from 23 to 25., Conclusions: Incorporation of a molecular prognostic classifier significantly improved identification of high-risk patients and survival predictions compared with when conventional staging is used. The TNMB staging system may lead to improved survival of early-stage disease through more effective application of adjuvant therapy., (Copyright © 2019 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.)

Purpose: To explore imaging biomarkers that can be used for diagnosis and prediction of pathologic stage in non-small cell lung cancer (NSCLC) using multiple machine learning algorithms based on CT image feature analysis., Methods: Patients with stage IA to IV NSCLC were included, and the whole dataset was divided into training and testing sets and an external validation set. To tackle imbalanced datasets in NSCLC, we generated a new dataset and achieved equilibrium of class distribution by using SMOTE algorithm. The datasets were randomly split up into a training/testing set. We calculated the importance value of CT image features by means of mean decrease gini impurity generated by random forest algorithm and selected optimal features according to feature importance (mean decrease gini impurity > 0.005). The performance of prediction model in training and testing sets were evaluated from the perspectives of classification accuracy, average precision (AP) score and precision-recall curve. The predictive accuracy of the model was externally validated using lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) samples from TCGA database., Results: The prediction model that incorporated nine image features exhibited a high classification accuracy, precision and recall scores in the training and testing sets. In the external validation, the predictive accuracy of the model in LUAD outperformed that in LUSC., Conclusions: The pathologic stage of patients with NSCLC can be accurately predicted based on CT image features, especially for LUAD. Our findings extend the application of machine learning algorithms in CT image feature prediction for pathologic staging and identify potential imaging biomarkers that can be used for diagnosis of pathologic stage in NSCLC patients.

Objective: The diagnostic criteria of lung biopsy specimens by 2015 WHO lung tumor classification were used to evaluate lung biopsy specimens along with detection of genetic alterations of major tumor driving genes including epidermal growth factor receptor (EGFR). Methods: The clinical data, histological slides, immunohistochemical stains and special stains of 806 lung biopsy specimens at Beijing Hospital from July 2015 to July 2018 were retrospectively analyzed. Diagnosis of lung cancer was reclassified according to the 2015 WHO lung tumor classification and related gene mutation data were analyzed. Results: During a three-year period, the total number of lung cancer diagnosis was 483 cases, including 221 female and 262 male patients with age ranging from 37 to 85 years (median age of 65 years). There were 40 cases(8.28%) of small cell carcinoma,11 cases (2.28%) of large cell neuroendocrine carcinoma, 3 cases (0.62%) of combined neuroendocrine carcinoma, 2 cases(0.41%) of atypical carcinoid, 208 cases (43.06%) of adenocarcinoma, 92 cases(19.05%) of non-small cell carcinoma, favor adenocarcinoma, 66 cases (13.66%) of squamous cell carcinoma, 42 cases(8.70%) of non-small cell carcinoma, favor squamous cell carcinoma, 16 cases(3.31%) of non-small cell carcinoma, not otherwise specified, and 3 cases (0.62%) of non-small cell carcinoma, possible adenosquamous carcinoma. Among 202 cases tested, 107 cases (52.97%) showed EGFR mutations, including 86 of 133 cases (64.66%) of adenocarcinoma and 18 of 52 cases (34.62%) of non-small cell carcinoma, favor adenocarcinoma. Twenty two cases were found to have T790M mutation among 27 patients after EGFR TKI targeted drug therapy. Immunohistochemical staining of ALK (D5F3) was positive in 3 of 354 cases of non-small cell lung cancer, confirmed by EML4-ALK fusion gene fluorescence PCR. ROS1 gene fusion was found in 1 of 38 cases. Splicing mutations in exon 14 of MET gene were seen in one case of non-small cell carcinoma with spindle cell differentiation. Conclusion: The new diagnostic criteria by the 2015 WHO lung tumor classification is better suited for diagnosing lung biopsy specimens and providing accurate treatment guidance and improving the patient outcome.

Objective: Convex probe endobronchial ultrasound (EBUS)-guided transbronchial needle aspiration (TBNA) is a minimally invasive technique with high sensitivity in the mediastinal staging of non-small cell carcinoma (NSCLC). In recent years, molecular testing has been developed to study genetic mutations in NSCLC. There are studies revealing improved survival in advanced NSCLC using targeted therapy as the first-line treatment in these patients. The aim of this study was to evaluate the adequacy of EBUS-TBNA in providing adequate size specimens for EGFR, ALK and ROS1 genetic mutation analysis in patients with adenocarcinoma or not otherwise specified (NOS) lung cancer., Materials and Methods: Charts of patients diagnosed with lung adenocarcinoma or NOS via EBUS-TBNA were retrospectively reviewed. Information on patient demographics, number of lymph nodes sampled, their size and location, targeted gene mutations and the adequacy of the material sampled for the molecular testing was recorded., Results: A total of 114 patients were included in the study, adenocarcinoma 86 (75%) and NOS 28 (25%). EGFR gene mutation was studied in all of the patients included in the study while ALK in 113 and ROS1 in 98. The material adequacy ratios for EGFR gene mutation, ALK and ROS1 rearrangements were found to be 88.6%, 93.8% and 91.8%, respectively. EGFR gene mutation, ALK and ROS1 rearrangements were found positive in 13 (11.4%), 9 (8%) and 1 (1%) patients, respectively., Conclusion: The study demonstrated that EBUS-TBNA provides adequate material for mutation analysis in patients with newly diagnosed adenocarcinoma or NOS lung cancer., (© 2018 John Wiley & Sons Ltd.)

In the eighth edition of the TNM classification of lung cancer, the prognostic impact of tumor size is emphasized as a descriptor of all T categories. Especially in lung cancer where tumor size is 5 cm or less, the 1-cm cutoff point significantly differentiated the survival outcome. In addition, the new staging categories were assigned, namely, Tis (adenocarcinoma in situ) and T1mi (minimally invasive adenocarcinoma). Furthermore, the measurement of a radiological solid component size excluding the ground glass opacity component or pathological invasive size without a lepidic component was proposed for deciding the cT/pT categories for lung adenocarcinoma. The N descriptors were kept the same as in the eventh edition on the whole, however, quantification of nodal disease had a prognostic impact based on the number of nodal stations involved in the eighth edition, i.e. N1a as a single N1 station, N1b as a multiple N1 station, N2a1 as a single N2 station without N1 (skip metastasis), N2a2 as a single N2 station with N1 disease, and N2b as a multiple N2 station. In the M descriptors, subclassification was performed based on the location or numbers of distantly metastatic lesions, i.e. M1a as any intrathoracic metastases, M1b as a single distant metastatic lesion in one organ, and M1c as multiple distant metastases in either a single organ or multiple organs. Survival analysis of the eighth edition of the TNM classification clearly separated the distinct groups, however, unsolved issues still remain that should be discussed and further revised for the forthcoming TNM staging system.

With the identification of therapeutic targets for lung adenocarcinoma, it has become mandatory to distinguish it from other entities. Some cases remain classified as non-small cell lung carcinoma, not otherwise specified (NSCLC-NOS) with immunohistochemistry. Electron microscopy (EM) can be useful, allowing the identification of glandular differentiation. The aim of this study was to determine the complementary value of immunohistochemistry and EM.Forty-eight NSCLC-NOS cases were selected (PSMAR-Biobank, Barcelona, Spain). Immunohistochemistry (TTF-1, p40) was performed. Tissue was retrieved from paraffin blocks. Results were compared to the final diagnosis, derived from combination of light microscopy, immunohistochemistry, EM, molecular studies and resection specimen.Immunohistochemistry concurred with final diagnosis in 36 cases (75%, Kappa = 0.517). EM agreed with final diagnosis in 35 (72.9%, Kappa = 0.471). Immunohistochemistry had a sensitivity = 73%, specificity = 100%, positive predictive value (PPV) = 100% and negative predictive value (NPV) = 52.4% for adenocarcinoma. All adenocarcinoma cases not solved by immunohistochemistry (n = 10) were classified by EM, and vice versa. Data from EM were identical to those of immunohistochemistry: sensitivity = 73%, specificity = 100%, PPV = 100% and NPV = 52.4%. Combining both techniques, 47 cases were coincident with final diagnosis (97.9%, Kappa = 0.943).EM can provide valuable information in subtyping NSCLC-NOS, being particularly useful when immunohistochemistry is inconclusive. EM could be considered as a complementary tool for decision-making in NSCLC-NOS.