scholarly journals Predicting Metastasis in Melanoma by Enumerating Circulating Tumor Cells Using Photoacoustic Flow Cytometry

2020 ◽  
Author(s):  
Robert H. Edgar ◽  
Ahmad Tarhini ◽  
Cindy Sander ◽  
Martin E. Sanders ◽  
Justin L. Cook ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Tumor Cells ◽  
Circulating Tumor Cells

scholarly journals Postoperative metastasis prediction based on portal vein circulating tumor cells detected by flow cytometry in periampullary or pancreatic cancer

2019 ◽  
Vol Volume 11 ◽  
pp. 7405-7425 ◽  
Author(s):  
Lianyuan Tao ◽  
Li Su ◽  
Chunhui Yuan ◽  
Zhaolai Ma ◽  
Lingfu Zhang ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Flow Cytometry ◽  
Portal Vein ◽  
Tumor Cells ◽  
Circulating Tumor Cells

scholarly journals In vivo quantitation of rare circulating tumor cells by multiphoton intravital flow cytometry

2007 ◽  
Vol 104 (28) ◽  
pp. 11760-11765 ◽  
Author(s):  
W. He ◽  
H. Wang ◽  
L. C. Hartmann ◽  
J.-X. Cheng ◽  
P. S. Low
Keyword(s):  
Flow Cytometry ◽  
Tumor Cells ◽  
Circulating Tumor Cells

scholarly journals Determination of Circulating Tumor Cells by Flow Cytometry in the Bladder Cancer Patients

2018 ◽  
Vol 2 (1) ◽  
pp. 25-30 ◽  
Author(s):  
Elif Ercan ◽  
◽  
Ender Sımsek ◽  
Ozen Ozensoy Guler ◽  
Abdullah Erdem Canda ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Bladder Cancer ◽  
Cancer Patients ◽  
Tumor Cells ◽  
Circulating Tumor Cells

scholarly journals A Novel Method for Colorectal Cancer Screening Based on Circulating Tumor Cells and Machine Learning

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1248
Author(s):  
Eleana Hatzidaki ◽  
Aggelos Iliopoulos ◽  
Ioannis Papasotiriou
Keyword(s):  
Colorectal Cancer ◽  
Machine Learning ◽  
Flow Cytometry ◽  
Cancer Screening ◽  
Tumor Cells ◽  
Circulating Tumor Cells ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Svm Classifier ◽  
Significant Information

Colorectal cancer is one of the most common types of cancer, and it can have a high mortality rate if left untreated or undiagnosed. The fact that CRC becomes symptomatic at advanced stages highlights the importance of early screening. The reference screening method for CRC is colonoscopy, an invasive, time-consuming procedure that requires sedation or anesthesia and is recommended from a certain age and above. The aim of this study was to build a machine learning classifier that can distinguish cancer from non-cancer samples. For this, circulating tumor cells were enumerated using flow cytometry. Their numbers were used as a training set for building an optimized SVM classifier that was subsequently used on a blind set. The SVM classifier’s accuracy on the blind samples was found to be 90.0%, sensitivity was 80.0%, specificity was 100.0%, precision was 100.0% and AUC was 0.98. Finally, in order to test the generalizability of our method, we also compared the performances of different classifiers developed by various machine learning models, using over-sampling datasets generated by the SMOTE algorithm. The results showed that SVM achieved the best performances according to the validation accuracy metric. Overall, our results demonstrate that CTCs enumerated by flow cytometry can provide significant information, which can be used in machine learning algorithms to successfully discriminate between healthy and colorectal cancer patients. The clinical significance of this method could be the development of a simple, fast, non-invasive cancer screening tool based on blood CTC enumeration by flow cytometry and machine learning algorithms.

scholarly journals Detection of clonal excess in lymphoproliferative disease by kappa/lambda analysis: correlation with immunoglobulin gene DNA rearrangement

Blood ◽  
1986 ◽  
Vol 67 (1) ◽  
pp. 80-85
Author(s):  
N Berliner ◽  
KA Ault ◽  
P Martin ◽  
DS Weinberg
Keyword(s):  
Flow Cytometry ◽  
Tumor Cells ◽  
Circulating Tumor Cells ◽  
Lymphoproliferative Disease ◽  
Enzyme Analysis ◽  
Restriction Enzyme Analysis ◽  
Immunoglobulin Gene ◽  
Dna Rearrangement ◽  
Surface Immunoglobulin ◽  
Dna Restriction

Previous studies have suggested that analysis of the distribution of surface immunoglobulin light chain isotypes by flow cytometry provides evidence for monoclonality of B cell tumors and may detect populations of circulating tumor cells in patients with lymphoproliferative disease. We have used simultaneous flow cytometry and DNA restriction enzyme analysis on 58 samples of tissue and blood to determine whether lymphocyte populations detected by “kappa/lambda” analysis are indeed monoclonal. In greater than 90% of cases, abnormalities detected by flow cytometry correlated with monoclonal rearrangements of immunoglobulin genes as detected by Southern blot analysis. By analyzing tissue and blood from the same patients, we have also demonstrated that monoclonal circulating cells detected by flow cytometry reflect peripheral circulating tumor cells, since DNA from these cells shows the same immunoglobulin rearrangement as DNA from the original tumors in these patients. Although mixing studies suggested that DNA rearrangement studies were more sensitive than was flow cytometry in detecting minor populations of monoclonal lymphocytes, we found only one case in which this affected the diagnostic accuracy of the kappa/lambda analysis, with one notable exception, that of detection of a monoclonal proliferation of B cells that did not express surface immunoglobulin. The kappa/lambda test thus offers a powerful diagnostic tool in the evaluation of lymphoproliferative disease.

scholarly journals Assessment of circulating tumor cells in peripheral blood using flow cytometry in patients with surgery for colorectal cancer – review

2020 ◽  
Vol 28 (4) ◽  
pp. 365-379
Author(s):  
Ana-Maria Muşină ◽  
Ionuţ Huţanu ◽  
Mihaela Zlei ◽  
Mădălina Ştefan ◽  
Mihaela Mentel ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Flow Cytometry ◽  
Tumor Cells ◽  
Circulating Tumor Cells ◽  
Peripheral Blood ◽  
Disease Free Survival ◽  
Final Analysis ◽  
Immunomagnetic Separation ◽  
Free Survival ◽  
Pubmed Database

AbstractIntroduction: Colorectal cancer (CRC) is the third most common neoplasia in the world. Circulating tumor cells (CTC) have a prognostic value and can be useful in monitoring solid neoplasia. Only one method for CTC identification has received the approval and this is the CellSearch® system based on the immunomagnetic separation. Multiple markers are used in CTC identification, as epithelial markers and cytokeratines. CTC identification in peripheral blood is associated with a worse prognostic and reduced free survival in CRC.Material and methods: We performed a systematic search in PubMed database for articles that reports the circulating tumor cells in CRC until July 2019. We selected studies in English and French and the main words used for search were ‘circulating tumor cells’, ‘colorectal cancer’, ‘colon cancer’, ‘rectal cancer’, ‘flow cytometry’, ‘peripheral blood’. We included studies with more than 10 patients, where samples were collected from the blood in relation with surgery and flow cytometry was used as analyzing technique.Results: We included 7 studies in final analysis, that showed in flow cytometry analysis a cut-off value of CTC that can vary from 2-4 CTC/ 7.5 ml peripheral blood with a sensitivity of 50.8% and specificity of 95%. Patients with positive CTC were associated with higher T stage and positive lymph nodes, with a worse overall survival (OS) and disease free survival (DFS) comparing with negative patients.Conclusion: CTC are considered to be a prognostic factor who needs more validation studies in order to be included in the clinical practice.

scholarly journals A Novel Combined Methodology for Isolation and Detection of Circulating Tumor Cells based on Flow Cytometry and Cellular Filtration Technologies

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Grigoriadis Nikolaos G ◽  
Kyritsis Konstantinos A ◽  
Akrivou Melpomeni G ◽  
Giassafaki Lefki-Pavlina N ◽  
Vizirianakis Ioannis S
Keyword(s):  
Flow Cytometry ◽  
Tumor Cells ◽  
Circulating Tumor Cells
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