Optical pathology of human brain metastasis of lung cancer using combined resonance Raman and spatial frequency spectroscopies

2016 ◽  
Author(s):  
Yan Zhou ◽  
Cheng-hui Liu ◽  
Yang Pu ◽  
Gangge Cheng ◽  
Lixin Zhou ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Brain Metastasis ◽  
Spatial Frequency ◽  
Human Brain ◽  
Resonance Raman ◽  
Combined Resonance

scholarly journals Combined spatial frequency spectroscopy analysis with visible resonance Raman for optical biopsy of human brain metastases of lung cancers

2019 ◽  
Vol 12 (02) ◽  
pp. 1950010 ◽  
Author(s):  
Yan Zhou ◽  
Cheng-Hui Liu ◽  
Yang Pu ◽  
Binlin Wu ◽  
Thien An Nguyen ◽  
...  
Keyword(s):  
Brain Metastases ◽  
Spatial Frequency ◽  
Human Brain ◽  
Resonance Raman ◽  
Support Vector ◽  
Svm Classifier ◽  
Label Free ◽  
Spectroscopy Analysis ◽  
Lung Cancers ◽  
Brain Tissues

The purpose of this study is to examine optical spatial frequency spectroscopy analysis (SFSA) combined with visible resonance Raman (VRR) spectroscopic method, for the first time, to discriminate human brain metastases of lung cancers adenocarcinoma (ADC) and squamous cell carcinoma (SCC) from normal tissues. A total of 31 label-free micrographic images of three types of brain tissues were obtained using a confocal micro-Raman spectroscopic system. VRR spectra of the corresponding samples were synchronously collected using excitation wavelength of 532[Formula: see text]nm from the same sites of the tissues. Using SFSA method, the difference in the randomness of spatial frequency structures in the micrograph images was analyzed using Gaussian function fitting. The standard deviations, [Formula: see text] calculated from the spatial frequencies of the micrograph images were then analyzed using support vector machine (SVM) classifier. The key VRR biomolecular fingerprints of carotenoids, tryptophan, amide II, lipids and proteins (methylene/methyl groups) were also analyzed using SVM classifier. All three types of brain tissues were identified with high accuracy in the two approaches with high correlation. The results show that SFSA–VRR can potentially be a dual-modal method to provide new criteria for identifying the three types of human brain tissues, which are on-site, real-time and label-free and may improve the accuracy of brain biopsy.

scholarly journals Systematic Review and Network Meta-Analysis of Anaplastic Lymphoma Kinase (ALK) Inhibitors for Treatment-Naïve ALK-Positive Lung Cancer

Cancers ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1966
Author(s):  
Cheng-Hao Chuang ◽  
Hsiao-Ling Chen ◽  
Hsiu-Mei Chang ◽  
Yu-Chen Tsai ◽  
Kuan-Li Wu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Brain Metastasis ◽  
Anaplastic Lymphoma Kinase ◽  
Low Dose ◽  
Meta Analysis ◽  
Phase Iii ◽  
Pairwise Comparisons ◽  
Anaplastic Lymphoma ◽  
Treatment Naïve ◽  
Alk Positive

Several anaplastic lymphoma kinase inhibitors (ALKIs) have demonstrated excellent efficacy on overall survival (OS), progression-free survival (PFS), objective response rate (ORR), and also better adverse effect (AE) profiles compared to cytotoxic chemotherapy in advanced stage anaplastic lymphoma kinase (ALK) rearrangement-positive non-small cell lung cancer (NSCLC) in phase III randomized clinical trials (RCTs). We conducted this systematic review and network meta-analysis to provide a ranking of ALKIs for treatment-naïve ALK-positive patients in terms of PFS, ORR, and AEs. In addition, a sub-group analysis of treatment benefits in patients with baseline brain metastasis was also conducted. Contrast-based analysis was performed for multiple treatment comparisons with the restricted maximum likelihood approach. Treatment rank was estimated using the surface under the cumulative ranking curve (SUCRA), as well as the probability of being the best (Prbest) reference. All next-generation ALKIs were superior to crizotinib in PFS but lorlatinib and brigatinib had increased AEs. The probability of lorlatinib being ranked first among all treatment arms was highest (SUCRA = 93.3%, Prbest = 71.8%), although there were no significant differences in pairwise comparisons with high- (600 mg twice daily) and low- (300 mg twice daily) dose alectinib. In subgroup analysis of patients with baseline brain metastasis, low-dose alectinib had the best PFS (SUCRA = 87.3%, Prbest = 74.9%). Lorlatinib was associated with the best ranking for ORR (SUCRA = 90.3%, Prbest = 71.3%), although there were no significant differences in pairwise comparisons with the other ALKIs. In addition, low-dose alectinib had the best safety performance (SUCRA = 99.4%, Prbest = 97.9%). Lorlatinib and low-dose alectinib had the best PFS and ORR in the overall population and baseline brain metastasis subgroup, respectively. Low-dose alectinib had the lowest AE risk among the available ALKIs. Further head-to-head large-scale phase III RCTs are needed to verify our conclusions.

scholarly journals Downregulation of MicroRNA-330 Correlates with the Radiation Sensitivity and Prognosis of Patients with Brain Metastasis from Lung Cancer

2017 ◽  
Vol 42 (6) ◽  
pp. 2220-2229 ◽  
Author(s):  
Li-Peng Jiang ◽  
Zhi-Tu Zhu ◽  
Yue Zhang ◽  
Chun-Yan He
Keyword(s):  
Lung Cancer ◽  
Logistic Regression ◽  
Radiation Therapy ◽  
Survival Rate ◽  
Brain Metastasis ◽  
Cox Regression ◽  
Radiation Sensitivity ◽  
Extracranial Metastasis ◽  
Radiation Resistant ◽  
N Stage

Background: The present study sought to explore the role of microRNA-330 (miR-330) in predicting the radiation response and prognosis of patients with brain metastasis (BM) from lung cancer (LC). Methods: Patients with BM from LC were identified and classified into radiation-sensitive and radiation-resistant groups according to the overall survival rate, local and distant recurrence rate after conventional whole-brain radiation therapy. Quantitative realtime polymerase chain reaction (qRT-PCR) was used to detect miR-330 expression in serum. Receiver operating characteristic (ROC) curves were used to evaluate the prognostic value of miR-330 for the radiation sensitivity of brain metastasis from LC. Related clinical factors for radiation sensitivity were assessed by logistic regression analysis, and a survival analysis was conducted using COX regression and the Kaplan-Meier method. Results: MiR-330 exhibited lower expression in the radiation-sensitive group than in the radiation-resistant group. The area under the ROC curve of miR-330 for predicting radiation sensitivity was 0.898 (optimal cut-off value, 0.815), with a sensitivity of 71.7% and a specificity of 90.1%. After radiation therapy, patients with low miR-330 expression, compared to patients with high miR-330 expression, displayed a lower survival rate and a median survival time. MiR-330 expression was correlated with extracranial metastasis, maximum BM diameter, tumor-node-metastasis (TNM) stage and node (N) stage. Logistic regression and COX regression analyses revealed that extracranial metastasis, TNM stage, N stage and miR-330 expression were factors that influenced both radiation sensitivity and individual prognostic factors in patients with BM from LC. Conclusions: These findings indicate that the downregulation of miR-330 correlates with radiation sensitivity and poor prognosis in patients with BM from LC.

scholarly journals OTHR-07. Systematic Review and Meta-analysis of Lung Cancer Brain Metastasis and Primary Tumor PD-L1 Expression Discordance

2021 ◽  
Vol 3 (Supplement_3) ◽  
pp. iii15-iii16
Author(s):  
Raees Tonse ◽  
Muni Rubens ◽  
Haley Appel ◽  
Martin C Tom ◽  
Matthew D Hall ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Systematic Review ◽  
Brain Metastasis ◽  
Tumor Cell ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Primary Tumor ◽  
Small Cell ◽  
Receptor Expression ◽  
Small Cell Lung

Abstract Background Novel immunotherapeutic strategies, such as those targeting the PD-1/PD-L1 axis, are promising in patients with metastatic lung cancer and are often administered when tumors show PD-L1 positivity. The objective of this study was to analyze PD-L1 receptor discordance in tumor cell between the primary tumor and lung cancer brain metastasis (LCBM). Methods A systematic review of series published prior to April 2021 obtained from the Medline database of biopsied or resected LCBM evaluating PD-L1 discordance was performed using PRISMA guidelines. Weighted random effects models were used to calculate pooled estimates. Results Six full-text articles (n=247 patients) with a median of 32 patients in each study (range: 24–73 patients) reported PD-L1 receptor expression analyses of both primary lung tumors and brain metastases. The majority of patients (81%) were smokers, with 67% non-small cell lung cancer and 33% small cell lung cancer. The pooled estimate for overall PD-LI receptor concordance between primary and LCBM was 76% (95% CI: 52%-90). The positivity rate varied when analyzed by various cutoff levels of PD-L1 expression; for <1% expression, it was 41% (95% CI: 22%-62%) for primary vs. 58% (95% CI: 35%-78%) for LCBM; for PD-L1 expression of 1–50%, it was 24% (95% CI: 13%-40%) vs. 19% (95% CI: 10%-33%); and for PD-L1 >50% it was 12% (95% CI: 4%-33%) vs. 21% (95% CI: 14%-29%) (p=0.425). The pooled estimate for overall PD-LI receptor discordance between primary and LCBM was 17% (95% CI: 10%-27%). Meta-regression analysis showed that age, sex, smoking status, and histology were not associated with PD-LI receptor discordance. Conclusions PD-L1 status discordance in tumor cell occurs in approximately 20% of LCBM, with the greatest discordance in the <1% expression category. Awareness of this discordance is important for the selection of immune checkpoint inhibitor therapy as well as in the analysis of patterns of failures.

scholarly journals Frequency of Silent Brain Metastasis Before Prophylactic Cranial Irradiation in Small Cell Lung Cancer

2017 ◽  
Vol 18 (1) ◽  
pp. 11-13 ◽  
Author(s):  
Ufuk Yilmaz ◽  
Esra Korkmaz Kirakli ◽  
Umit Gurlek ◽  
Yasemin Ozdogan ◽  
Bahri Gumus ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Brain Metastasis ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Cranial Irradiation ◽  
Prophylactic Cranial Irradiation ◽  
Small Cell ◽  
Small Cell Lung
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