scholarly journals Molecular Detection of Tumor Cells in Bronchoalveolar Lavage Fluid From Patients With Early Stage Lung Cancer

1999 ◽  
Vol 91 (4) ◽  
pp. 332-339 ◽  
Author(s):  
S. A. Ahrendt ◽  
J. T. Chow ◽  
L.-H. Xu ◽  
S. C. Yang ◽  
C. F. Eisenberger ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Bronchoalveolar Lavage ◽  
Tumor Cells ◽  
Bronchoalveolar Lavage Fluid ◽  
Molecular Detection ◽  
Early Stage ◽  
Lavage Fluid ◽  
Early Stage Lung Cancer ◽  
Stage Lung Cancer

scholarly journals Pulmonary venous blood sampling significantly increases the yield of circulating tumor cells in early-stage lung cancer

2016 ◽  
Vol 151 (3) ◽  
pp. 852-858 ◽  
Author(s):  
Rishindra M. Reddy ◽  
Vasudha Murlidhar ◽  
Lili Zhao ◽  
Svetlana Grabauskiene ◽  
Zhuo Zhang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Tumor Cells ◽  
Circulating Tumor Cells ◽  
Early Stage ◽  
Venous Blood ◽  
Blood Sampling ◽  
Early Stage Lung Cancer ◽  
Venous Blood Sampling ◽  
Stage Lung Cancer

scholarly journals Protein Glycopatterns in Bronchoalveolar Lavage Fluid as Novel Potential Biomarkers for Diagnosis of Lung Cancer

2021 ◽  
Vol 10 ◽  
Author(s):  
Lina Liu ◽  
Dan Li ◽  
Jian Shu ◽  
Li Wang ◽  
Fan Zhang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Bronchoalveolar Lavage ◽  
Bronchoalveolar Lavage Fluid ◽  
Validation Cohort ◽  
Early Stage ◽  
Lavage Fluid ◽  
Double Blind ◽  
Diagnostic Models ◽  
Diagnosis Of Lung Cancer ◽  
Potential Biomarkers

Lung cancer is one of the most prevalent and life-threatening neoplasias worldwide due to the deficiency of ideal diagnostic biomarkers. Although aberrant glycosylation has been observed in human serum and tissue, little is known about the alterations in bronchoalveolar lavage fluid (BALF) that are extremely associated with lung cancer. In this study, our aim was to systematically investigate and assess the alterations of protein glycopatterns in BALF and possibility as biomarkers for diagnosis of lung cancer. Here, lectin microarrays and blotting analysis were utilized to detect the differential expression of BALF glycoproteins from patients with 80 adenocarcinomas (ADC), 77 squamous carcinomas (SCC), 51 small cell lung cancer (SCLC), and 73 benign pulmonary diseases (BPD). These 281 specimens were then randomly divided into a training cohort and validation cohort for constructing and verifying the diagnostic models based on the glycopattern abundances. Moreover, an independent test was performed with 120 newly collected BALF samples enrolled in the double-blind cohort to further assess the clinical application potential of the diagnostic models. According to the results, there were 15 (e.g., PHA-E, EEL, and BPL) and 14 lectins (e.g., PTL-II, LCA, and SJA) that individually showed significant variations in different types and stages of lung cancer compared to BPD. Notably, the diagnostic models achieved better discriminate power in the validation cohort and exhibited high accuracies of 0.917, 0.864, 0.712, 0.671, and 0.781 in the double-blind cohort for the diagnosis of lung cancer, early stage lung cancer, ADC, SCC, and SCLC, respectively. Taken together, the present study revealed that the abnormally altered protein glycopatterns in BALF are expected to be novel potential biomarkers for the identification and early diagnosis of lung cancer, which will contribute to explain the mechanism of the development of lung cancer from the perspective of glycobiology.

scholarly journals FlowCell-enriched circulating tumor cells as a predictor of lung cancer metastasis

Human Cell ◽  
2021 ◽  
Author(s):  
Yan Lu ◽  
Yushuang Zheng ◽  
Yuhong Wang ◽  
Dongmei Gu ◽  
Jun Zhang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Tumor Cells ◽  
Circulating Tumor Cells ◽  
Cancer Metastasis ◽  
Lung Tumor ◽  
Early Stage ◽  
High Rate ◽  
Early Stage Lung Cancer ◽  
Lung Cancer Metastasis ◽  
Number Of Patients

AbstractLung cancer is the most fetal malignancy due to the high rate of metastasis and recurrence after treatment. A considerable number of patients with early-stage lung cancer relapse due to overlooked distant metastasis. Circulating tumor cells (CTCs) are tumor cells in blood circulation that originated from primary or metastatic sites, and it has been shown that CTCs are critical for metastasis and prognosis in various type of cancers. Here, we employed novel method to capture, isolate and classify CTC with FlowCell system and analyzed the CTCs from a cohort of 302 individuals. Our results illustrated that FlowCell-enriched CTCs effectively differentiated benign and malignant lung tumor and the total CTC counts increased as the tumor developed. More importantly, we showed that CTCs displayed superior sensitivity and specificity to predict lung cancer metastasis in comparison to conventional circulating biomarkers. Taken together, our data suggested CTCs can be used to assist the diagnosis of lung cancer as well as predict lung cancer metastasis. These findings provide an alternative means to screen early-stage metastasis.

scholarly journals P04.10 Relapsed or Metastatic Organotropism in Early Stage Lung Cancer after Radical Surgery

2021 ◽  
Vol 16 (3) ◽  
pp. S264-S265
Author(s):  
F. Xu ◽  
L. Yang ◽  
C. Liu ◽  
J. Ying ◽  
Y. Wang
Keyword(s):  
Lung Cancer ◽  
Radical Surgery ◽  
Early Stage ◽  
Early Stage Lung Cancer ◽  
Stage Lung Cancer

scholarly journals Structural and functional radiomics for lung cancer

2021 ◽  
Author(s):  
Guangyao Wu ◽  
Arthur Jochems ◽  
Turkey Refaee ◽  
Abdalla Ibrahim ◽  
Chenggong Yan ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Treatment Strategy ◽  
Early Stage ◽  
Quality Data ◽  
Clinical Implementation ◽  
Early Stage Lung Cancer ◽  
Treatment Response Evaluation ◽  
Advanced Stage Lung Cancer ◽  
Stage Lung Cancer

Abstract Introduction Lung cancer ranks second in new cancer cases and first in cancer-related deaths worldwide. Precision medicine is working on altering treatment approaches and improving outcomes in this patient population. Radiological images are a powerful non-invasive tool in the screening and diagnosis of early-stage lung cancer, treatment strategy support, prognosis assessment, and follow-up for advanced-stage lung cancer. Recently, radiological features have evolved from solely semantic to include (handcrafted and deep) radiomic features. Radiomics entails the extraction and analysis of quantitative features from medical images using mathematical and machine learning methods to explore possible ties with biology and clinical outcomes. Methods Here, we outline the latest applications of both structural and functional radiomics in detection, diagnosis, and prediction of pathology, gene mutation, treatment strategy, follow-up, treatment response evaluation, and prognosis in the field of lung cancer. Conclusion The major drawbacks of radiomics are the lack of large datasets with high-quality data, standardization of methodology, the black-box nature of deep learning, and reproducibility. The prerequisite for the clinical implementation of radiomics is that these limitations are addressed. Future directions include a safer and more efficient model-training mode, merge multi-modality images, and combined multi-discipline or multi-omics to form “Medomics.”

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