Miliary brain metastases in 2 cases with advanced non-small cell lung cancer harboring EGFR mutation during gefitinib treatment

2012 ◽  
Vol 50 (3) ◽  
pp. 117-121 ◽  
Author(s):  
Sayaka Mochizuki ◽  
Naoki Nishimura ◽  
Akira Inoue ◽  
Koji Murakami ◽  
Toshihiro Nukiwa ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Brain Metastases ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Egfr Mutation ◽  
Small Cell ◽  
Small Cell Lung ◽  
Gefitinib Treatment

scholarly journals Real-world disease burden and outcomes of brain metastases in EGFR mutation-positive non-small-cell lung cancer

2020 ◽  
Author(s):  
Eric Nadler ◽  
Janet L Espirito ◽  
Melissa Pavilack ◽  
Bismark Baidoo ◽  
Ancilla Fernandes
Keyword(s):  
Lung Cancer ◽  
Brain Metastases ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Real World ◽  
Service Use ◽  
Egfr Mutation ◽  
Symptom Burden ◽  
Small Cell ◽  
Small Cell Lung

Aim: To evaluate the real-world impact of brain metastases (BM) among patients with EGFR mutation-positive ( EGFRm) metastatic non-small-cell lung cancer (NSCLC). Materials & methods: This retrospective, observational matched cohort electronic health record study assessed adults with EGFRm metastatic NSCLC with/without BM. Results: Among 402 patients split equally between both cohorts (±BM), the majority were Caucasian (69%), female (65%) and with adenocarcinoma (92%). Overall symptom burden and ancillary support service use were higher and median overall survival from metastatic diagnosis was significantly shorter in BM patients (11.9 vs 16 months; p = 0.017). Conclusion: BM in EGFRm NSCLC patients can negatively impact clinical outcomes. New targeted therapies that can penetrate the blood–brain barrier should be considered for treating these patients.

scholarly journals The impact of EGFR mutation status and single brain metastasis on the survival of non-small-cell lung cancer patients with brain metastases

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Yuya Fujita ◽  
Manabu Kinoshita ◽  
Tomohiko Ozaki ◽  
Koji Takano ◽  
Kei Kunimasa ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Brain Metastases ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Egfr Mutation ◽  
Genetic Alterations ◽  
Small Cell ◽  
Small Cell Lung ◽  
Mutation Status ◽  
The Impact

Abstract Background Molecular and genetic alterations of non-small-cell lung cancer (NSCLC) now play a vital role in patient care of this neoplasm. The authors focused on the impact of epidermal growth factor receptor mutation (EGFR-mt) status on the survival of patients after brain metastases (BMs) from NSCLC. The purpose of the study was to understand the most desirable management of BMs from NSCLC. Methods This was a retrospective observational study analyzing 647 patients with NSCLC, including 266 patients with BMs, diagnosed at our institute between January 2008 and December 2015. EGFR mutation status, overall survival (OS) following diagnosis, OS following BMs, duration from diagnosis to BMs, and other factors related to OS and survival after BMs were measured. Results Among 647 patients, 252 (38.8%) had EGFR mutations. The rate and frequency of developing BMs were higher in EGFR-mt patients compared with EGFR wildtype (EGFR-wt) patients. EGFR-mt patients showed longer median OS (22 vs 11 months, P < .001) and a higher frequency of BMs. Univariate and multivariate analyses revealed that good performance status, presence of EGFR-mt, single BM, and receiving local therapies were significantly associated with favorable prognosis following BM diagnosis. Single metastasis, compared with multiple metastases, exhibited a positive impact on patient survival after BMs in EGFR-mt patients, but not in EGFR-wt NSCLC patients. Conclusions Single BM with EGFR-mt performed better than other groups. Furthermore, effective local therapies were recommended to achieve better outcomes.

scholarly journals Correlation between EGFR mutation status and the incidence of brain metastases in patients with non-small cell lung cancer

2017 ◽  
Vol 9 (8) ◽  
pp. 2510-2520 ◽  
Author(s):  
Lina Li ◽  
Shuimei Luo ◽  
Heng Lin ◽  
Haitao Yang ◽  
Huijuan Chen ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Brain Metastases ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Egfr Mutation ◽  
Small Cell ◽  
Small Cell Lung ◽  
Mutation Status

Impact of EGFR mutation on outcomes following SRS for brain metastases in non-small cell lung cancer

Lung Cancer ◽  
2021 ◽  
Author(s):  
Fabio Y. Moraes ◽  
Alireza Mansouri ◽  
Archya Dasgupta ◽  
Matthew Ramotar ◽  
Natalya Kosyak ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Brain Metastases ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Egfr Mutation ◽  
Small Cell ◽  
Small Cell Lung

scholarly journals CMET-04. RADIOMICS FEATURES CAN DIFFERENTIATE THE EGFR MUTATION STATUS OF BRAIN METASTASES FROM NON-SMALL CELL LUNG CANCER

2019 ◽  
Vol 21 (Supplement_6) ◽  
pp. vi51-vi51
Author(s):  
Yae Won Park ◽  
Sung Soo Ahn ◽  
Dongmin Choi ◽  
Hwiyoung Kim
Keyword(s):  
Lung Cancer ◽  
Brain Metastases ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Egfr Mutation ◽  
Multiparametric Mri ◽  
Small Cell ◽  
Machine Learning Algorithms ◽  
Small Cell Lung ◽  
Mutation Status

Abstract BACKGROUND AND PURPOSE To assess whether radiomics features on DTI and conventional postcontrast T1-weighted (T1C) images can differentiate the epidermal growth factor receptor (EGFR) molecular status in brain metastases from non-small cell lung cancer (NSCLC). MATERIALS AND METHODS Radiomics features (n = 5046) were extracted from preoperative MRI including T1C and DTI from pathologically confirmed brain metastases of 59 patients with underlying NSCLC and known EGFR mutation status (31 EGFR wild type, 28 EGFR mutant). A subset of 4317 features (85.6%) with high stability (intraclass correlation coefficient > 0.9) were selected for further analysis. After feature selection by the least absolute shrinkage and selection operator, the radiomics classifiers were constructed by various machine learning algorithms. The prediction performance of the classifier was validated by using leave-one-out cross-validation. Diagnostic performance was compared between multiparametric MRI radiomics models and single imaging radiomics models using the area under the curve (AUC) from ROC analysis. RESULTS Thirty-seven significant radiomics features (6 from ADC, 6 from fractional anisotropy [FA], 25 from T1C) were selected. The best performing multiparametric radiomics model (AUC 0.97, 95% CI 0.94–1) showed better performance than any single radiomics model using ADC (AUC 0.79, p = 0.007), FA (AUC 0.75, p = 0.001), or T1C (AUC 0.96, p = 0.678); the accuracy, sensitivity, and specificity of this model were 94.4%, 96.6%, and 92.0%, respectively. CONCLUSION Radiomics classifiers integrating multiparametric MRI parameters may be useful to differentiate the EGFR mutation status in brain metastases from lung cancer.

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