Pathologic Subtypes of Lung Adenocarcinoma Brain Metastasis Is a Strong Predictor of Survival After Resection

Casteillo, François; Guy, Jean‐Baptiste; Dal-Col, Pierre; Karpathiou, Georgia; Pommier, Benjamin; Bayle-Bleuez, Sophie; Fournel, P.; Vassal, François; Forest, Fabien

doi:10.1097/pas.0000000000001161

Cited by 19 publications

(25 citation statements)

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“…In a previous study, we showed that even grading of lung adenocarcinoma Bmets has an important prognostic impact. 12 Furthermore, we found important correlations between the primary tumor's infiltration by CD8-positive lymphocytes and brain metastasis characteristics. Specifically, primary tumors less infiltrated by CD8 cells gave multiple BM most often, as well as metastases that reveal the primary tumor and occipital metastases.…”

Section: Discussionmentioning

confidence: 71%

“…Molecular analysis was performed for 93 cases (lung adenocarcinomas, melanomas, and gastrointestinal carcinomas), as previously described. [10][11][12] Briefly, formalinfixed paraffin-embedded tumor sections were used for DNA extraction (automated extraction: Qiacube kit used; Qiagen, Courtaboeuf, France). DNA concentration and quality were assessed by the Qubit method and a Nanodrop spectrophotometer (Thermo Fisher Scientific, Waltham, Massachusetts, USA).…”

Section: Molecular Analysismentioning

confidence: 99%

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Brain metastasis PD-L1 and CD8 expression is dependent on primary tumor type and its PD-L1 and CD8 status

Camy

Karpathiou

Dumollard

et al. 2020

J Immunother Cancer

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BackgroundBrain metastases (Bmets) are frequent; however, limited data exist on the efficacy of immunotherapy in these lesions. The aims of the study were to analyze the immunohistochemical expressions of programmed death ligand 1 (PD-L1) and CD8 in Bmets and to compare them with their expressions in paired primary tumors, as well as correlate the results with clinicopathological features.MethodsThis is a retrospective study of 233 patients with Bmets and 111 paired primaries. Clinical, histological, and molecular data were recorded and compared with the immunohistochemical results of PD-L1 and CD8 expressions. The statistical analysis included χ2 test, Cramer’s V test, factorial analyses of variance, simple regression analysis, and Kaplan-Meier analysis with log-rank product limit estimation.ResultsPD-L1 expression was found in 23.6% of Bmets and in 29.0% of primary tumors with concordant expression between them in 75.5% of cases. Bmets PD-L1 expression was associated with primary tumor PD-L1 expression and the primary tumor type. Significant CD8 peritumoral expression was found in 68.6% of Bmets and in 87.7% of primary tumors. CD8 expression was concordant between primary and metastatic tumors in 73.3% of cases. Bmets CD8 expression was associated with primary tumor CD8 expression and primary tumor type. PD-L1 expression was associated with CD8 expression in both primary and metastatic tumors. The concordance between primary and metastatic tumor PD-L1 expression was independent of all factors studied. The concordance between primary and metastatic CD8 expressions was marginally associated to the time of Bmets development. No prognostic role for PD-L1 and CD8 expression in Bmets was found.ConclusionPD-L1 and CD8 Bmets expressions are associated with the primary tumor type and its PD-L1 and CD8 expressions. No factor predicts the discordance for PD-L1 expression, while time to Bmets development is associated with CD8 expression discordance.

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Section: Discussionmentioning

confidence: 71%

Section: Molecular Analysismentioning

confidence: 99%

Brain metastasis PD-L1 and CD8 expression is dependent on primary tumor type and its PD-L1 and CD8 status

Camy

Karpathiou

Dumollard

et al. 2020

J Immunother Cancer

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“…Adenocarcinoma may consist of some cubic tumor cells and some columnar [32]. The pathology of primary lung adenocarcinoma is mostly papillary type, others are solid, lepidic, acinar, and micropapillary subtypes., which is often diagnosed as a mixture of multiple subtypes [33]. The pathological subtypes of lung squamous cell carcinoma include small cell, clear cell, basaloid and papillary subtypes [6,34], in which papillary squamous cell carcinoma is usually characterized by exogenous endobronchial growth [35].…”

Section: Discussionmentioning

confidence: 99%

Using CT texture analysis to differentiate between peripheral lung cancer and pulmonary inflammatory pseudotumor

Liu

Duan

et al. 2020

BMC Med Imaging

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Background: This study is to distinguish peripheral lung cancer and pulmonary inflammatory pseudotumor using CT-radiomics features extracted from PET/CT images. Methods: In this study, the standard 18F-fluorodeoxyglucose positron emission tomography/ computed tomography (18 F-FDG PET/CT) images of 21 patients with pulmonary inflammatory pseudotumor (PIPT) and 21 patients with peripheral lung cancer were retrospectively collected. The dataset was used to extract CT-radiomics features from regions of interest (ROI), The intra-class correlation coefficient (ICC) was used to screen the robust feature from all the radiomic features. Using, then, statistical methods to screen CT-radiomics features, which could distinguish peripheral lung cancer and PIPT. And the ability of radiomics features distinguished peripheral lung cancer and PIPT was estimated by receiver operating characteristic (ROC) curve and compared by the Delong test. Results: A total of 435 radiomics features were extracted, of which 361 features showed relatively good repeatability (ICC ≥ 0.6). 20 features showed the ability to distinguish peripheral lung cancer from PIPT. these features were seen in 14 of 330 Gray-Level Co-occurrence Matrix features, 1 of 49 Intensity Histogram features, 5 of 18 Shape features. The area under the curves (AUC) of these features were 0.731 ± 0.075, 0.717, 0.748 ± 0.038, respectively. The P values of statistical differences among ROC were 0.0499 (F9, F20), 0.0472 (F10, F11) and 0.0145 (F11, Mean4). The discrimination ability of forming new features (Parent Features) after averaging the features extracted at different angles and distances was moderate compared to the previous features (Child features). Conclusion: Radiomics features extracted from non-contrast CT based on PET/CT images can help distinguish peripheral lung cancer and PIPT.

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“…Adenocarcinoma may consist partly cubic tumor cells and partly of columnar [29]. The pathology of primary lung adenocarcinoma is mostly papillary type, others are solid, lepidic, acinar, and micropapillary subtypes., which is often diagnosed as a mixture of multiple subtypes [30]. The pathological subtypes of lung squamous cell carcinoma include small cell, clear cell, basaloid and papillary subtypes [6,31], in which papillary squamous cell carcinoma is usually characterized by exogenous endobronchial growth [32].…”

Section: Discussionmentioning

confidence: 99%

Using CT texture analysis to differentiate between peripheral lung cancer and pulmonary inflammatory pseudotumor

Liu

Duan

et al. 2020

Preprint

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Background: Pulmonary inflammatory pseudotumor (PIPT) usually presents as solitary peripheral well-defined nodules or masses, and CT features are complex and changeable, which are often confused with peripheral lung cancer. This study is to distinguish peripheral lung cancer and PIPT using CT-radiomics features extracted from PET/CT images.Methods: In this study, the standard 18F-fluorodeoxyglucose positron emission tomography/ computed tomography (18 F-FDG PET/CT) images of 21 patients with pulmonary inflammatory pseudotumor (PIPT) and 21 patients with peripheral lung cancer were retrospectively collected. The dataset was used to extract CT-radiomics features from regions of interest (ROI), Using, then, statistical methods to screen CT-radiomics features, which could distinguish peripheral lung cancer and PIPT. And the ability of radiomics features distinguished peripheral lung cancer and PIPT was estimated by receiver operating characteristic (ROC) curves, and compared by Delong test.Results: A total of 435 radiomics features were extracted, of which 23 could difference between peripheral lung cancer and PIPT. these features were seen in 16 of 330 Gray-Level Co-occurrence Matrix features, 1 of 49 Intensity Histogram features, 1 of 5 Neighbor Intensity Difference features, 5 of 18 Shape features. area under the curve (AUC) of these features were 0.731 0.075, 0.717, 0.737, 0.748 0.038, respectively. The P values of statistical differences among ROC were 0.0499 (F11, F23), 0.0472 (F12, F13) and 0.0250 (F13, Mean4). The discrimination ability of forming new features(Parent Feature) after averaging the features extracted at different angles and distances was moderate compared to the previous features(Child features).Conclusions: Radiomics features extracted from non-contrast CT based on PET/CT images can help distinguish peripheral lung cancer and PIPT.

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Pathologic Subtypes of Lung Adenocarcinoma Brain Metastasis Is a Strong Predictor of Survival After Resection

Cited by 19 publications

References 19 publications

Brain metastasis PD-L1 and CD8 expression is dependent on primary tumor type and its PD-L1 and CD8 status

Brain metastasis PD-L1 and CD8 expression is dependent on primary tumor type and its PD-L1 and CD8 status

Using CT texture analysis to differentiate between peripheral lung cancer and pulmonary inflammatory pseudotumor

Using CT texture analysis to differentiate between peripheral lung cancer and pulmonary inflammatory pseudotumor

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