scholarly journals Feasibility of interstitial ex-vivo mammary autofluorescne microendoscopy

2021 ◽  
Author(s):  
Dena Monjazebi
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Gold Standard ◽  
Normal Tissue ◽  
Ex Vivo ◽  
Target Tissue ◽  
Breast Cancer Imaging ◽  
Imaging Diagnostics ◽  
The Past ◽  
Breast Tissues

In the past decades our knowledge of breast cancer has been rapidly evolving yet the basic paradigm of diagnosis and treatment of cancer has not. In cancer diagnosis, presentation of breast cancer can be a palpable lump or a suspicious mass on screening imaging, namely a mammogram. However, malignancy will be ascertained by tissue biopsy if needed. Biopsy is the gold standard breast cancer diagnostic test. Biopsy sampling is invasive, painful and costly. In addition, when the interpretation of current imaging modalities is not concordant with pathology results the biopsies may have to be repeated. Microendoscopy autofluorescence (AM) is a method of acquiring images directly from the tissues that contain fluorescent susceptible molecules (fluorophore). Studies of endoscopy in colon and esophagus showed that AM imaging is capable to recognize malignancy and can be utilized to discriminate between normal tissue and tumor. Additionally, it has been shown that, AM was able to differentiate cancer versus normal cells when a microendoscope was inserted into a breast duct. The main purpose of this study is to investigate if the same contrast exists if AM applied interstitially into the ex-vivo mastectomy breast tissues. This is a feasibility study to explore if interstitial AM has the potential to be coupled with breast cancer imaging diagnostics to provide better discrimination of the characteristics of the target tissue inside. The success in this approach could significantly reduce the number of required tissue biopsies to confirm the diagnosis.

scholarly journals Feasibility of interstitial ex-vivo mammary autofluorescne microendoscopy

2021 ◽  
Author(s):  
Dena Monjazebi
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Gold Standard ◽  
Normal Tissue ◽  
Ex Vivo ◽  
Target Tissue ◽  
Breast Cancer Imaging ◽  
Imaging Diagnostics ◽  
The Past ◽  
Breast Tissues

In the past decades our knowledge of breast cancer has been rapidly evolving yet the basic paradigm of diagnosis and treatment of cancer has not. In cancer diagnosis, presentation of breast cancer can be a palpable lump or a suspicious mass on screening imaging, namely a mammogram. However, malignancy will be ascertained by tissue biopsy if needed. Biopsy is the gold standard breast cancer diagnostic test. Biopsy sampling is invasive, painful and costly. In addition, when the interpretation of current imaging modalities is not concordant with pathology results the biopsies may have to be repeated. Microendoscopy autofluorescence (AM) is a method of acquiring images directly from the tissues that contain fluorescent susceptible molecules (fluorophore). Studies of endoscopy in colon and esophagus showed that AM imaging is capable to recognize malignancy and can be utilized to discriminate between normal tissue and tumor. Additionally, it has been shown that, AM was able to differentiate cancer versus normal cells when a microendoscope was inserted into a breast duct. The main purpose of this study is to investigate if the same contrast exists if AM applied interstitially into the ex-vivo mastectomy breast tissues. This is a feasibility study to explore if interstitial AM has the potential to be coupled with breast cancer imaging diagnostics to provide better discrimination of the characteristics of the target tissue inside. The success in this approach could significantly reduce the number of required tissue biopsies to confirm the diagnosis.

Breast Cancer Diagnosis With Mammography

2021 ◽  
pp. 107-127
Author(s):  
Abir Baâzaoui ◽  
Walid Barhoumi
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Gold Standard ◽  
Breast Cancer Diagnosis ◽  
Black Box ◽  
Diagnose Breast Cancer ◽  
Cad Systems ◽  
Helpful Tool ◽  
Cancerous Lesion ◽  
Aided Diagnosis

Breast cancer, which is the second-most common and leading cause of cancer death among women, has witnessed growing interest in the two last decades. Fortunately, its early detection is the most effective way to detect and diagnose breast cancer. Although mammography is the gold standard for screening, its difficult interpretation leads to an increase in missed cancers and misinterpreted non-cancerous lesion rates. Therefore, computer-aided diagnosis (CAD) systems can be a great helpful tool for assisting radiologists in mammogram interpretation. Nonetheless, these systems are limited by their black-box outputs, which decreases the radiologists' confidence. To circumvent this limit, content-based mammogram retrieval (CBMR) is used as an alternative to traditional CAD systems. Herein, authors systematically review the state-of-the-art on mammography-based breast cancer CAD methods, while focusing on recent advances in CBMR methods. In order to have a complete review, mammography imaging principles and its correlation with breast anatomy are also discussed.

scholarly journals Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review

2020 ◽  
Vol 10 (22) ◽  
pp. 8298
Author(s):  
Yuliana Jiménez-Gaona ◽  
María José Rodríguez-Álvarez ◽  
Vasudevan Lakshminarayanan
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Critical Review ◽  
Breast Tumor ◽  
Screening Tools ◽  
Breast Cancer Imaging ◽  
The Past ◽  
Computer Aided ◽  
Tumor Research ◽  
Aided Diagnosis

This paper provides a critical review of the literature on deep learning applications in breast tumor diagnosis using ultrasound and mammography images. It also summarizes recent advances in computer-aided diagnosis/detection (CAD) systems, which make use of new deep learning methods to automatically recognize breast images and improve the accuracy of diagnoses made by radiologists. This review is based upon published literature in the past decade (January 2010–January 2020), where we obtained around 250 research articles, and after an eligibility process, 59 articles were presented in more detail. The main findings in the classification process revealed that new DL-CAD methods are useful and effective screening tools for breast cancer, thus reducing the need for manual feature extraction. The breast tumor research community can utilize this survey as a basis for their current and future studies.

Elasto-Mammography: Elastic Property Reconstruction in Breast Tissues

2005 ◽  
Vol 874 ◽  
Author(s):  
Z. Wang ◽  
Y. Liu ◽  
L.Z. Sun ◽  
G. Wang
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Imaging Modality ◽  
Breast Cancer Diagnosis ◽  
Reconstruction Method ◽  
Breast Cancers ◽  
Primary Method ◽  
Conventional Mammography ◽  
Breast Tissues ◽  
Higher Sensitivity

AbstractMammography is the primary method for screening and detecting breast cancers. However, it frequently fails to detect small tumors and is not quite specific in terms of tumor benignity and malignancy. The objective of this paper is to develop a new imaging modality called elastomammography that generates the modulus elastograms based conventional mammographs. A new elastic reconstruction method is described based on elastography and mammography for breast tissues. Elastic distribution can be reconstructed through the measurement of displacement provided by mammographic projection. It is shown that the proposed elasto-mammography provides higher sensitivity and specificity than the conventional mammography on its own for breast cancer diagnosis.

scholarly journals Understanding the sources of errors in ex vivo Hsp90 molecular imaging for rapid-on-site breast cancer diagnosis

2021 ◽  
Author(s):  
Roujia Wang ◽  
Daniel Alvarez ◽  
Brian Crouch ◽  
Aditi Pilani ◽  
Christopher Lam ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Molecular Imaging ◽  
Cancer Diagnosis ◽  
Ex Vivo ◽  
Breast Cancer Diagnosis

scholarly journals 99mTc-Radiolabeled TPGS Nanomicelles Outperform 99mTc-Sestamibi as Breast Cancer Imaging Agent

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Fiorella C. Tesan ◽  
Melisa B. Nicoud ◽  
Mariel Nuñez ◽  
Vanina A. Medina ◽  
Diego A. Chiappetta ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gamma Camera ◽  
Ex Vivo ◽  
Scintillation Counter ◽  
Imaging Agent ◽  
Sprague Dawley ◽  
Breast Cancer Imaging ◽  
Murine Cell Line ◽  
Polyethylene Glycol 1000

D-α-Tocopheryl polyethylene glycol 1000 succinate (TPGS) is a Food and Drug Administration (FDA) approved biomaterial that can form nanosized micelles in aqueous solution. TPGS micelles stand as an interesting system to perform drug delivery as they can carry lipophilic drugs and overcome P glycoprotein efflux as well. Therefore, TPGS micelles combined with other copolymers have been reported in many cancer research studies as a carrier for therapeutic drugs. Their ability to reach tumoral tissue can also be exploited to develop imaging agents with diagnostic application. A radiolabeling method with 99mTc for TPGS nanosized micelles and their biodistribution in a healthy animal model as well as their pharmacokinetics and radiolabeling stability in vivo was previously reported. The aim of this work was to evaluate the performance of this radioactive probe as a diagnostic imaging agent compared to routinely available SPECT radiopharmaceutical, 99mTc-sestamibi. A small field of view gamma camera was used for scintigraphy studies using radiolabeled TPGS micelles in two animal models of breast cancer: syngeneic 4T1 murine cell line (injected in BALB/c mice) and chemically NMU-induced (Sprague-Dawley rats). Ex vivo radioactivity accumulation in organs of interest was measured by a solid scintillation counter, and a semiquantitative analysis was performed over acquired images as well. Results showed an absence of tumoral visualization in 4T1 model for both radioactive probes by gamma camera imaging. On the contrary, NMU-induced tumors had a clear tumor visualization by scintigraphy. A higher tumor/background ratio and more homogeneous uptake were found for radiolabeled TPGS micelles compared to 99mTc-sestamibi. In conclusion, 99mTc-radiolabeled TPGS micelles might be a potential SPECT imaging probe for diagnostic purposes.

scholarly journals A Novel Bimodal Imaging Agent Targeting HER2 Molecule of Breast Cancer

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Wei Lv ◽  
Yamei Shen ◽  
Hengli Yang ◽  
Rui Yang ◽  
Wenbin Cai ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Near Infrared ◽  
Ex Vivo ◽  
Ultrasound Contrast Agent ◽  
Breast Cancer Imaging ◽  
Her2 Positive ◽  
Her2 Breast Cancer ◽  
Bimodal Imaging ◽  
Nir Fluorescence ◽  
Targeting Ability

Nanobubble (NB), a newly developed nanoscaled ultrasound contrast agent (UCA) for molecular imaging, has been widely researched for these years. Targeting it with functional molecule, nanobubble can adhere selectively to cellular epitopes and receptors outside the vasculature via enhanced permeability and retention (EPR) effect of tumor blood vessel. To enhance the targeting rate of our previous prepared NBs-Affibody for HER2 (+) breast cancer imaging, we introduced a near-infrared fluorescent (NIRF) dye, IR783, in this study to enhance tumor-specific targeting rate and provide a promising modality for dual-mode imaging. The prepared IR783-NBs-Affibody presented a uniform nanoscale size around 482.7 ± 54.3 nm, good biosecurity, and stability over time. The encapsulation efficiency (EE) of IR-783 was 15.09% in the conjugates leading to a successful NIR fluorescence and ultrasound enhancement imaging ex vivo. IR783-NBs-Affibody was able to automatically accumulate on BT474 cells with a highly increased targeting rate of 85.4% compared with previous NBs-Affibody of 26.6%, while Affibody-guided HER2 binding was only found in HER2-positive cell lines (BT474 and T-47D). The newly developed IR783-NBs-Affibody is characterized with favorable HER2 targeting ability and bimodal imaging capability for breast cancer. Thus, IR783-NBs-Affibody holds great potential in molecular diagnosis for patients with breast cancer.

Comparative Study by Analytical Multidisciplinary Methods and Oxidative Stress in Breast Cancer Diagnosis

2018 ◽  
Vol 69 (8) ◽  
pp. 2254-2259
Author(s):  
Irina Jari ◽  
Alexandru Naum ◽  
Liliana Gheorghe Moisii ◽  
Cipriana Stefanescu ◽  
Dragos Negru ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Oxidative Stress ◽  
Cancer Diagnosis ◽  
Gold Standard ◽  
Breast Imaging ◽  
Diagnostic Performance ◽  
Area Under The Curve ◽  
Breast Mri ◽  
Breast Cancer Diagnosis ◽  
And Oxidative Stress

To evaluate the diagnostic performance of mammography, elastography and breast magnetic resonance imaging (MRI), as tools for breast cancer diagnosis, against pathological diagnosis as the gold standard. Other risk factors such as obesity and oxidative stress are also disccused. In this comparison study, a total of 169 female patients (mean age 51 years, range 35-77 years) were enrolled between January 2016 and June 2017. After the physical examination of the breasts, patients were further randomized into three groups to mammography, elastography, or breast MRI. Only women with detected lesions classified into breast imaging and reporting data system (BI-RADS) category or Tsukuba elasticity score from 2 to 5 were included. Histopathology was used as the gold standard for diagnosis. The diagnostic performance of each modality was calculated. Of a total of 50 pathologically confirmed cancers, 25 were detected by mammography, 11 by elastography, and 14 by breast MRI, which resulted in sensitivities of 84% (PPV = 78%), 75% (PPV = 64%) and 86% (PPV = 75%), respectively. Mammography, elastography, and breast MRI led to 6, 5, and 4 false positive findings, which resulted in specificities of 86% (NPV = 90%), 87% (NPV = 92%) and 89% (NPV = 94%), respectively. The area under the curve (AUC) values for the mammography, elastography and breast MRI were 0.849 (95% CI, 0.758-0.939), 0.809 (95% CI, 0.670-0.948) and 0.876 (95% CI, 0.769-0.983). The DOR values were 32 (95% CI, 8-125), 20 (95% CI, 4-99) and 51 (95% CI, 8-315). The breast MRI proved a slight advantage over mammography as a diagnostic tool in breast cancer diagnosis.

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