Impacts of EGFR-mutation status and EGFR-TKI on the efficacy of stereotactic radiosurgery for brain metastases from non-small cell lung adenocarcinoma: A retrospective analysis of 133 consecutive patients

Lung Cancer ◽  
2018 ◽  
Vol 119 ◽  
pp. 120-126 ◽  
Author(s):  
Shoji Yomo ◽  
Kyota Oda
Keyword(s):  
Brain Metastases ◽  
Lung Adenocarcinoma ◽  
Stereotactic Radiosurgery ◽  
Retrospective Analysis ◽  
Egfr Mutation ◽  
Small Cell ◽  
Small Cell Lung ◽  
Mutation Status ◽  
Egfr Tki

A practical decision-making strategy based on clinical triple features for EGFR-TKIs to treat advanced non-small cell lung cancer patients with unknown EGFR mutation status.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. e19082-e19082
Author(s):  
Di Zheng ◽  
Jiying Wang ◽  
Bing Lu
Keyword(s):  
Lung Cancer ◽  
Salvage Therapy ◽  
Egfr Mutation ◽  
Response Rate ◽  
Small Cell ◽  
Smoking History ◽  
Small Cell Lung ◽  
Mutation Status ◽  
Egfr Tki ◽  
Egfr Tkis

e19082 Background: EGFR mutated lung cancers are strongly associated with clinical characteristics of never- smoking history and adenocarcinoma histology type, and tended to develop multiple pulmonary metastases.Whether multiple pulmonary metastatic lung adenocarcinomas with never-smoking history would respond to EGFR-TKIs as those harboring EGFR active mutation remains unclear. Methods: 223 consecutive metastatic non-small cell lung cancer (NSCLC) patients with unknown EGFR status who received EGFR-TKIs as salvage therapy after failure of previous platinum-based chemotherapy in Shanghai Pulmonary hospital between 2009 and 2011 were included to the study. Available CT scans, routinely performed at baseline and one month after the start of EGFR-TKIs therapy, were reviewed independently by two investigators. For the purposes of this study, diffuse pulmonary metastatic nodules were defined as multiple nodules distributed diffusely throughout the whole lung with at least 20 nodules within the unilateral lung field. Paraffin embedded tissues were available for 45 of 223 patients for EGFR gene mutation test. Results: Of 134 never-smokers with lung adenocarcinoma,70 patients responded to EGFR-TKIs with an objective response rate (ORR) of 52.2% (70/134), and the ORR for the 62 patients with diffuse pulmonary metastatic nodules was 79% (49/62). Among the 20 patients with confirmed EGFR mutation (based on the available 45 archived specimen), the ORR was 75% (15/20). The multivariate analyses showed that the presence of diffused multiple pulmonary metastatic nodules, activating EGFR mutation and female are independent predictive factors of the response to EGFR-TKIs. Conclusions: Patient selection based on specific clinical features to recieve EGFR-TKI treatment yield high response rate comparable to that selected by EGFR mutation status. It is practical to consider EGFR-TKI as salvage therapy in non-smoking patients with lung adenocarcinoma characterized by diffuse pulmonary nodules when EGFR mutation testing is a challenge.

scholarly journals Predicting EGFR Mutation Status by a Deep Learning Approach in Patients with Non-Small Cell Lung Cancer Brain Metastases

2021 ◽  
Author(s):  
Oz Haim ◽  
Shani Abramov ◽  
Ben Shofty ◽  
Claudia Fanizzi ◽  
Francesco DiMeco ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Brain Metastases ◽  
Small Cell Lung Cancer ◽  
Egfr Mutation ◽  
Small Cell ◽  
Small Cell Lung ◽  
Mutation Status ◽  
Mutational Status ◽  
The Brain

Abstract PURPOSE: Non-small cell lung cancer (NSCLC), the most prevalent subtype of lung cancer, tends to metastasize to the brain. Between 10-60% of NSCLCs harbor an activating mutation in the epidermal growth factor receptor (EGFR), which may be targeted with selective EGFR inhibitors. However, due to a high discordance rate between the molecular profile of the primary tumor and the brain metastases (BMs), identifying an individual patient’s EGFR status of the BMs necessitates tissue diagnosis via an invasive surgical procedure. We employed a deep learning (DL) method with the aim of noninvasive detection of the EGFR mutation status in NSCLC BM. METHODS: We retrospectively collected clinical, radiological, and pathological-molecular data of all the NSCLC patients who had been diagnosed with BMs and underwent resection of their BM during 2006-2019. The study population was then divided into 2 groups based upon EGFR mutational status. We further employed a DL technique to classify the 2 groups according to their preoperative magnetic resonance imaging features. Finally, we established the accuracy of our model in predicting EGFR mutation status of BM of NSCLC. RESULTS: Fifty-nine patients were included in the study, 16 patients harbored EGFR mutations. Our model predicted mutational status with mean accuracy of 89.8%, sensitivity of 68.7%, specificity of 97.7%, and a receiver operating characteristic curve )ROC( value of 0.91 across the 5 validation datasets.CONCLUSION: DL based noninvasive molecular characterization is feasible, has high accuracy and should be further validated in large prospective cohorts.

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

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.

EGFR mutation status in brain metastases of non-small cell lung carcinoma

2012 ◽  
Vol 111 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Fanny Burel-Vandenbos ◽  
Damien Ambrosetti ◽  
Michael Coutts ◽  
Florence Pedeutour
Keyword(s):  
Brain Metastases ◽  
Lung Carcinoma ◽  
Egfr Mutation ◽  
Small Cell Lung Carcinoma ◽  
Small Cell ◽  
Small Cell Lung ◽  
Cell Lung Carcinoma ◽  
Mutation Status
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