scholarly journals Radiotracers for Molecular Imaging of Breast Cancer

10.5772/20365 ◽  
2011 ◽  
Author(s):  
Fan-Lin Kong ◽  
David J.
Keyword(s):  
Breast Cancer ◽  
Molecular Imaging

Nanotechnology-Enabled Optical Molecular Imaging of Breast Cancer

2013 ◽  
Author(s):  
Rebekah Drezek
Keyword(s):  
Breast Cancer ◽  
Molecular Imaging ◽  
Optical Molecular Imaging

Molecular Imaging of Breast Cancer: Role of RGD Peptides

2015 ◽  
Vol 15 (13) ◽  
pp. 1073-1094 ◽  
Author(s):  
Rubel Chakravarty ◽  
Sudipta Chakraborty ◽  
Ashutosh Dash
Keyword(s):  
Breast Cancer ◽  
Molecular Imaging ◽  
Rgd Peptides

Preclinical Molecular Imaging of the Translocator Protein (TSPO) in a Metastases Model Based on Breast Cancer Xenografts Propagated in the Murine Brain

2012 ◽  
Vol 12 (4) ◽  
pp. 458-466
Author(s):  
Shelby K. Wyatt ◽  
H. Charles Manning ◽  
Mingfeng Bai ◽  
Moneeb Ehtesham ◽  
Khubaib Y. Mapara ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Molecular Imaging ◽  
Translocator Protein ◽  
Model Based ◽  
Murine Brain ◽  
Breast Cancer Xenografts

Molecular imaging for monitoring treatment response in breast cancer patients

2013 ◽  
Vol 717 (1-3) ◽  
pp. 2-11 ◽  
Author(s):  
Frederike Bensch ◽  
Michel van Kruchten ◽  
Laetitia E. Lamberts ◽  
Carolien P. Schröder ◽  
Geke A.P. Hospers ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Molecular Imaging ◽  
Treatment Response ◽  
Cancer Patients ◽  
Breast Cancer Patients ◽  
Monitoring Treatment ◽  
Monitoring Treatment Response

Molecular pathways and molecular imaging in breast cancer: An update

2013 ◽  
Vol 40 (5) ◽  
pp. 581-591 ◽  
Author(s):  
Anna Rita Cervino ◽  
Marta Burei ◽  
Luigi Mansi ◽  
Laura Evangelista
Keyword(s):  
Breast Cancer ◽  
Molecular Imaging ◽  
Molecular Pathways

scholarly journals The prognostic value of quantitative analysis of CCL5 and collagen IV in luminal B (HER2–) subtype breast cancer by quantum-dot-based molecular imaging

2018 ◽  
Vol Volume 13 ◽  
pp. 3795-3803 ◽  
Author(s):  
Yong-yun Zhu ◽  
Chuang Chen ◽  
Juan-Juan Li ◽  
Sheng-Rong Sun
Keyword(s):  
Breast Cancer ◽  
Quantitative Analysis ◽  
Molecular Imaging ◽  
Quantum Dot ◽  
Prognostic Value ◽  
Collagen Iv ◽  
Luminal B

scholarly journals Coarse Raman and optical diffraction tomographic imaging enable label-free phenotyping of isogenic breast cancer cells of varying metastatic potential

2020 ◽  
Author(s):  
Santosh Kumar Paidi ◽  
Vaani Shah ◽  
Piyush Raj ◽  
Kristine Glunde ◽  
Rishikesh Pandey ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Raman Spectroscopy ◽  
Molecular Imaging ◽  
Single Cell ◽  
Cancer Cells ◽  
Breast Cancer Cells ◽  
Metastatic Potential ◽  
Optical Diffraction ◽  
Multivariate Curve Resolution ◽  
Label Free

AbstractIdentification of the metastatic potential represents one of the most important tasks for molecular imaging of cancer. While molecular imaging of metastases has witnessed substantial progress as an area of clinical inquiry, determining precisely what differentiates the metastatic phenotype has proven to be more elusive underscoring the need to marry emerging imaging techniques with tumor biology. In this study, we utilize both the morphological and molecular information provided by 3D optical diffraction tomography and Raman spectroscopy, respectively, to propose a label-free route for optical phenotyping of cancer cells at single-cell resolution. By using an isogenic panel of cell lines derived from MDA-MB-231 breast cancer cells that vary in their metastatic potential, we show that 3D refractive index tomograms can capture subtle morphological differences among the parental, circulating tumor cells, and lung metastatic cells. By leveraging the molecular specificity of Raman spectroscopy, we demonstrate that coarse Raman microscopy is capable of rapidly mapping a sufficient number of cells for training a random forest classifier that can accurately predict the metastatic potential of cells at a single-cell level. We also leverage multivariate curve resolution – alternating least squares decomposition of the spectral dataset to demarcate spectra from cytoplasm and nucleus, and test the feasibility of identifying metastatic phenotypes using the spectra only from the cytoplasmic and nuclear regions. Overall, our study provides a rationale for employing coarse Raman mapping to substantially reduce measurement time thereby enabling the acquisition of reasonably large training datasets that hold the key for label-free single-cell analysis and, consequently, for differentiation of indolent from aggressive phenotypes.

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