scholarly journals Effect of brain radiotherapy strategies on prognosis of patients with EGFR-mutant lung adenocarcinoma with brain metastasis

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Guangchuan Deng ◽  
Yingyun Zhang ◽  
Jiaojiao Ke ◽  
Qi Wang ◽  
Hongyue Qin ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Lung Adenocarcinoma ◽  
Growth Factor Receptor ◽  
Whole Brain Radiotherapy ◽  
Progression Free Survival ◽  
Radiation Mode ◽  
Whole Brain ◽  
Lung Cancers ◽  
Radiotherapy Group ◽  
Brain Radiotherapy

Abstract Purpose Epidermal growth factor receptor (EGFR)-mutant lung cancers have a high risk of developing brain metastases (BM). Whole brain radiotherapy (WBRT), local radiotherapy, and WBRT + Boost are frequently used for treatment of BM. This retrospective study aimed to evaluate the difference in efficacy of these radiotherapy modes in patients with EGFR-mutant lung adenocarcinoma with BMs. Further, we determined the optimal radiotherapy regimen for patients based on Lung-molGPA. Methods and materials We retrospectively enrolled 232 patients with EGFR-mutant lung adenocarcinoma with BMs. Patients were divided into three groups based on the different modes of brain radiotherapy: WBRT group, local radiotherapy group, and WBRT + Boost group. Graded prognostic assessment for lung cancer using molecular markers (Lung molGPA), overall survival (OS), and intracranial progression-free survival (iPFS) were calculated. Kaplan–Meier was used to compare iPFS and OS in different groups. Results The median OS for the WBRT (n = 84), local radiotherapy (n = 65), and WBRT + Boost (n = 83) cohorts was 32.8, 59.1, and 41.7 months, respectively (P = 0.0002). After stratification according to the Lung-molGPA score, the median OS for the WBRT (n = 56), local radiotherapy (n = 19), and WBRT + Boost (n = 28) cohorts was 32.5, 30.9, and 30.8 months, respectively, in subgroup with score 1–2 (P = 0.5097). In subgroup with score 2.5–4, the median OS for the WBRT (n = 26), local radiotherapy (n = 45), and WBRT + Boost (n = 54) cohorts was 32, 68.4, and 51 months, respectively (P = 0.0041). Conclusion The present study showed that in patients with EGFR-mutant lung adenocarcinoma with BM, local radiotherapy and WBRT + Boost perform similarly well both in the subgroups with low and high scores of Lung-molGPA. Considering the side effect caused by whole brain radiotherapy, we recommended local radiotherapy as optimal brain radiation mode for those subtype lung cancer patients.

Primary chemotherapy, stereotactic radiosurgery, or whole brain radiotherapy in non-small cell lung cancer (NSCLC) patients with asymptomatic brain metastases

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. e19063-e19063
Author(s):  
K. Kim ◽  
J. Lee ◽  
M. Chang ◽  
J. Uhm ◽  
J. A. Yun ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Brain Metastases ◽  
Stereotactic Radiosurgery ◽  
Whole Brain Radiotherapy ◽  
Progression Free Survival ◽  
Primary Chemotherapy ◽  
Treatment Modalities ◽  
Whole Brain ◽  
Brain Radiotherapy ◽  
Nsclc Patients

e19063 Background: Approximately 25 to 30% of patients with lung cancer develop brain metastases at some stage and 12∼18% at the time of initial presentation. Whole brain radiotherapy (WBRT) has long been a mainstay of treatment of brain metastases. Another treatment approach, Stereotactic radiosurgery (SRS) is a method of delivering high doses of focal irradiation to a tumor while minimizing the irradiation to the adjacent normal tissue. However, the prognosis of NSCLC patients with asymptomatic brain metastases, who are not treated with SRS or WBRT, has not been fully investigated yet. This study aimed to analyze the outcome for various treatment modalities in NSCLC patients with asymptomatic brain metastases. Methods: We reviewed the medical records of 129 patients with a histopathologically proven NSCLC and a synchronous brain metastases between January 2003 and December 2007. The patients were categorized as primary chemotherapy, primary SRS, and primary WBRT group: primary chemotherapy (78 patients), primary SRS (24 patients), and primary WBRT (27 patients). Results: With median follow-up of 30.0 months (7.2 -70.7), the median overall survival (OS) for the entire patients was 15.6 months (0.5–50.7) and the progression free survival (PFS) was 6.1 months (0.3- 53.0). The OS was 22.4m for primary SRS group, 13.9m for primary chemotherapy group, and 17.7m for primary WBRT group; p=0.86). However, patients treated with primary SRS showed trend toward prolonged survival compared to those of primary WBRT p=0.06). Subset analysis of 110 adenocarcinoma patients showed that the median OS for patients treated with primary SRS was longer than those of primary WRBT (29.3m vs 17.7m p=0.01) or primary chemotherapy (29.3m vs 14.6m p=0.04). Conclusions: These results suggest that for NSCLC patients with asymptomatic brain metastases at first diagnosis, SRS rather than primary chemotherapy or WBRT might be considered as initial treatment, especially for patients with adenocarcinoma. No significant financial relationships to disclose.

MRI-based radiomics signature for the prediction of response of lung cancer brain metastases after whole-brain radiotherapy.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e21096-e21096
Author(s):  
Ruizhe Xu ◽  
Ye Tian ◽  
Bo Zhang
Keyword(s):  
Lung Cancer ◽  
Brain Metastases ◽  
Whole Brain Radiotherapy ◽  
Gray Level ◽  
Local Response ◽  
Prediction Of Response ◽  
Whole Brain ◽  
Brain Radiotherapy ◽  
Pre Treatment ◽  
Radiomics Signature

e21096 MRI-based Radiomics signature for the Prediction of Response of Lung Cancer Brain Metastases After Whole-Brain Radiotherapy Background: Local response prediction for brain metastases (BMs) from lung cancer after Whole-Brain Radiotherapy (WBRT) is challenging, as existing criteria are based solely on unidimensional measurements. This study sought to determine whether radiomic features of lung cancer BMs derived from pre-treatment magnetic resonance imaging (MRI) could be used to predict local response following WBRT. Methods: A total of 88 Lung Cancer patients with BMs treated with WBRT were analyzed. After volumes of interest were drawn, 944 radiomic features including first-order, shape, Gray Level Co-occurrence Matrix (GLCM), Gray Level Dependence Matrix (GLDM), Gray Level Run Length Matrix (GLRLM), Gray Level Size Zone Matrix (GLSZM), Neighborhood Gray Tone Difference Matrix (NGTDM), and Laplacian of Gaussian (LoG) features were extracted, using the baseline pre-treatment post-contrast T1 (T1c) and T2 fluid-attenuated inversion recovery (FLAIR) MRI sequences, respectively. Local response status was determined by contrasting the baseline and follow-up MRI according to the RANO-BM criteria. The independent samples t test or Mann-Whitney U test, and then least absolute shrinkage and selection operator (LASSO) were used for dimensionality reduction and feature selection. An adaboost classifier was trained using the selected radiomic features and evaluated using the area under the receiver operating characteristic curve (AUC) in both the training and testing sets. Other discrimination metrics, including classification accuracy, positive predictive value (PPV), negative predictive value (NPV), sensitivity, and specificity, were also calculated. Results: The optimal radiomics signature was developed based on a multivariable logistic regression with 4, 5, 6 radiomic features on T1c, T2 FLAIR and T1c+T2 FLAIR, respectively. The radiomics model based on T1c features presented the AUC of (0.920 vs. 0.805, respectively) for both the training and testing sets, followed by T2 FLAIR features (0.893 vs. 0.701, respectively), and T1c+T2 FLAIR features (0.971 vs. 0.857, respectively). The classification accuracy of the radiomics model also well predicted the local response of BMs for both the the training and testing sets (T1c: 82.9% vs. 77.8%, T2 FLAIR: 82.9% vs. 77.8%, T1c+T2 FLAIR: 90.0% vs. 77.8%, respectively). Conclusions: Radiomics holds promise for predicting local tumor response following WBRT in patients with lung cancer and brain metastases. A predictive model built on radiomic features from an institutional cohort performed well on cross-validation testing. These results warrant further validation in independent datasets. Such work could prove invaluable for guiding management of individual patients and assessing outcomes of novel interventions.

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