Non-Invasive Breast Cancer Diagnosis through Electrochemical Biosensing at Different Molecular Levels

Breast cancer is the commonest cancer to affect women in developed countries and is increasing in frequency in the Western world. Approximately 50,000 women and 400 men are diagnosed with breast cancer in the United Kingdom each year. Eighty per cent of these individuals will survive for at least 5 years after diagnosis. In 2012, 11,762 women died of breast cancer in the United Kingdom. Age-standardized rates of new invasive breast cancer diagnosis have increased from 75 to 126 per 100,000 population in the United Kingdom between 1977 and 2010.

Download Full-text

Non-Invasive Biomarkers for Early Detection of Breast Cancer

Cancers ◽

10.3390/cancers12102767 ◽

2020 ◽

Vol 12 (10) ◽

pp. 2767

Author(s):

Jiawei Li ◽

Xin Guan ◽

Zhimin Fan ◽

Lai-Ming Ching ◽

Yan Li ◽

...

Keyword(s):

Breast Cancer ◽

Cancer Diagnosis ◽

Early Stage ◽

False Negative ◽

Breast Cancer Diagnosis ◽

Diagnostic Biomarkers ◽

Circulating Micrornas ◽

Non Invasive ◽

Common Cancer ◽

Volatile Organic

Breast cancer is the most common cancer in women worldwide. Accurate early diagnosis of breast cancer is critical in the management of the disease. Although mammogram screening has been widely used for breast cancer screening, high false-positive and false-negative rates and radiation from mammography have always been a concern. Over the last 20 years, the emergence of “omics” strategies has resulted in significant advances in the search for non-invasive biomarkers for breast cancer diagnosis at an early stage. Circulating carcinoma antigens, circulating tumor cells, circulating cell-free tumor nucleic acids (DNA or RNA), circulating microRNAs, and circulating extracellular vesicles in the peripheral blood, nipple aspirate fluid, sweat, urine, and tears, as well as volatile organic compounds in the breath, have emerged as potential non-invasive diagnostic biomarkers to supplement current clinical approaches to earlier detection of breast cancer. In this review, we summarize the current progress of research in these areas.

Download Full-text

Neighborhood socioeconomic deprivation, tumor subtypes, and causes of death after non-metastatic invasive breast cancer diagnosis: a multilevel competing-risk analysis

Breast Cancer Research and Treatment ◽

10.1007/s10549-014-3135-z ◽

2014 ◽

Vol 147 (3) ◽

pp. 661-670 ◽

Cited By ~ 14

Author(s):

Min Lian ◽

Maria Pérez ◽

Ying Liu ◽

Mario Schootman ◽

Ann Frisse ◽

...

Keyword(s):

Breast Cancer ◽

Risk Analysis ◽

Cancer Diagnosis ◽

Invasive Breast Cancer ◽

Causes Of Death ◽

Breast Cancer Diagnosis ◽

Socioeconomic Deprivation ◽

Competing Risk ◽

Competing Risk Analysis ◽

Tumor Subtypes

Download Full-text

Accuracy of administrative databases in detecting primary breast cancer diagnoses: a systematic review

BMJ Open ◽

10.1136/bmjopen-2017-019264 ◽

2018 ◽

Vol 8 (7) ◽

pp. e019264 ◽

Cited By ~ 5

Author(s):

Iosief Abraha ◽

Alessandro Montedori ◽

Diego Serraino ◽

Massimiliano Orso ◽

Gianni Giovannini ◽

...

Keyword(s):

Breast Cancer ◽

Systematic Review ◽

Cancer Diagnosis ◽

Invasive Breast Cancer ◽

Primary Breast Cancer ◽

Breast Cancer Diagnosis ◽

Administrative Databases ◽

Cochrane Library ◽

Icd 10 ◽

Incident Cases

ObjectiveTo define the accuracy of administrative datasets to identify primary diagnoses of breast cancer based on the International Classification of Diseases (ICD) 9th or 10th revision codes.DesignSystematic review.Data sources: MEDLINE, EMBASE, Web of Science and the Cochrane Library (April 2017).Eligibility criteriaThe inclusion criteria were: (a) the presence of a reference standard; (b) the presence of at least one accuracy test measure (eg, sensitivity) and (c) the use of an administrative database.Data extractionEligible studies were selected and data extracted independently by two reviewers; quality was assessed using the Standards for Reporting of Diagnostic accuracy criteria.Data analysisExtracted data were synthesised using a narrative approach.ResultsFrom 2929 records screened 21 studies were included (data collection period between 1977 and 2011). Eighteen studies evaluated ICD-9 codes (11 of which assessed both invasive breast cancer (code 174.x) and carcinoma in situ (ICD-9 233.0)); three studies evaluated invasive breast cancer-related ICD-10 codes. All studies except one considered incident cases.The initial algorithm results were: sensitivity ≥80% in 11 of 17 studies (range 57%–99%); positive predictive value was ≥83% in 14 of 19 studies (range 15%–98%) and specificity ≥98% in 8 studies. The combination of the breast cancer diagnosis with surgical procedures, chemoradiation or radiation therapy, outpatient data or physician claim may enhance the accuracy of the algorithms in some but not all circumstances. Accuracy for breast cancer based on outpatient or physician’s data only or breast cancer diagnosis in secondary position diagnosis resulted low.ConclusionBased on the retrieved evidence, administrative databases can be employed to identify primary breast cancer. The best algorithm suggested is ICD-9 or ICD-10 codes located in primary position.Trial registration numberCRD42015026881.

Download Full-text

Trends in pre-operative needle biopsy use in invasive breast cancer diagnosis: a Dutch nationwide population study

Acta Oncologica ◽

10.1080/0284186x.2020.1830168 ◽

2020 ◽

Vol 59 (12) ◽

pp. 1469-1473

Author(s):

J. P. Bulte ◽

D. Simsek ◽

P. Bult ◽

J. H. W. de Wilt ◽

L. J. A. Strobbe

Keyword(s):

Breast Cancer ◽

Population Study ◽

Cancer Diagnosis ◽

Invasive Breast Cancer ◽

Needle Biopsy ◽

Breast Cancer Diagnosis

Download Full-text

Breast Cancer Diagnosis Using Relational Discriminant Analysis of Malignancy-Associated Changes

Handbook of Research on Machine Learning Innovations and Trends - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-2229-4.ch002 ◽

2017 ◽

pp. 22-42

Author(s):

Dmitry Klyushin ◽

Natalia Boroday ◽

Kateryna Golubeva ◽

Maryna Prysiazhna ◽

Maksym Shlykov

Keyword(s):

Breast Cancer ◽

Cancer Diagnosis ◽

Breast Cancer Diagnosis ◽

Cancer Diagnostics ◽

High Rate ◽

Statistical Machine Learning ◽

Statistical Depth ◽

Non Invasive ◽

Novel Method ◽

Novel Algorithms

The chapter is devoted to description of a novel method of breast cancer diagnostics based on the analysis of the distribution of the DNA concentration in interphase nuclei of epitheliocytes of buccal epithelium with the aid of novel algorithms of statistical machine learning, namely: novel proximity measure between multivariate samples, novel algorithm of construction of tolerance ellipsoids, novel statistical depth and novel method of multivariate ordering. In contrast to common methods of diagnostics used in oncology, this method is a non-invasive and offers a high rate of accuracy and sensitivity.

Download Full-text