Advanced and futuristic approaches for breast cancer diagnosis

Abstract Background Breast cancer is the most frequent cancer and one of the most common causes of death in women, impacting almost 2 million women each year. Tenacity or perseverance of breast cancer in women is very high these days with an extensive increasing rate of 3 to 5% every year. Along with hurdles faced during treatment of breast tumor, one of the crucial causes of delay in treatment is invasive and poor diagnostic techniques for breast cancer hence the early diagnosis of breast tumors will help us to improve its management and treatment in the initial stage. Main body Present review aims to explore diagnostic techniques for breast cancer that are currently being used, recent advancements that aids in prior detection and evaluation and are extensively focused on techniques that are going to be future of breast cancer detection with better efficiency and lesser pain to patients so that it helps to a physician to prevent delay in treatment of cancer. Here, we have discussed mammography and its advanced forms that are the need of current era, techniques involving radiation such as radionuclide methods, the potential of nanotechnology by using nanoparticle in breast cancer, and how the new inventions such as breath biopsy, and X-ray diffraction of hair can simply use as a prominent method in breast cancer early and easy detection tool. Conclusion It is observed significantly that advancement in detection techniques is helping in early diagnosis of breast cancer; however, we have to also focus on techniques that will improve the future of cancer diagnosis in like optical imaging and HER2 testing.

Download Full-text

Toward early diagnosis decision support for breast cancer: Ontology-based semantic interoperability.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.15_suppl.e18072 ◽

2019 ◽

Vol 37 (15_suppl) ◽

pp. e18072-e18072 ◽

Cited By ~ 1

Author(s):

Mengchun Gong ◽

Zhi Wang ◽

Yefu Liu ◽

Hong Zhou ◽

Fang Wang ◽

...

Keyword(s):

Breast Cancer ◽

Early Diagnosis ◽

Cancer Diagnosis ◽

Laboratory Tests ◽

Treatment Guidelines ◽

Breast Cancer Diagnosis ◽

Clinical Findings ◽

Semantic Interoperability ◽

Snomed Ct ◽

Imaging Results

e18072 Background: Breast cancer is ranked as the most common cancer among women in China, cases in China account for about 300,000 of all newly diagnosed breast cancers each year. Developing Clinical Decision Supporting System (CDSS) to assist early diagnosis breast cancer is valuable, however, lack of semantic interoperability in CDSS has been identified as the main obstacle for broad adoption of CDSS. Ontology is considered to be one of the effective approaches to bridge the terminology gap between various clinical systems and data sources. In this study, we describe our efforts in transforming breast cancer clinical knowledge into computable ontology to support breast cancer early diagnosis. Methods: We have built a breast cancer ontology (BCO) to support our CDSS pipeline. A number of NCCN clinical practice guidelines in Oncology for Breast Cancer and the breast cancer diagnosis and treatment guidelines of China are the main knowledge sources of the BCO. BCO is a manually curated resource that contains concepts and relations of breast cancer clinical findings, demographics, laboratory tests, imaging results, treatments, pathologies, cancer stages, body structure and follow-up information. These curated knowledges are annotated through interoperable standard vocabulary SNOMED CT. Two physicians reviewed and evaluated the BCO. Results: Concepts in BCO currently contains 79 clinical findings, 8 demographics, 42 laboratory tests, 105 imaging results, 332 treatments, 141 pathology and cancer stages, 30 body structures and 3 follow-ups. Relations are defined such as finding site, associated with, etc. The concepts hierarchies and relationships were built by using Protégé to support OWL representation. As an initial evaluation results, most of concepts could be mapped to SNOMED CT, but there are some concepts could not be exactly mapped to SNOMED CT. We created local Chinese Vocabularies for these local terms. Conclusions: Based on interoperable BCO, a group of computable rules are developed by adding statements according to early diagnosis criteria of breast cancer. Diagnosis assistance around complex patients is supported though connecting the Electronic Health Record (EHR). This is an ongoing development that the BCO is continuingly enriched with standard concepts, relations and integrate with individual instances to support broader adoptability.

Download Full-text

Breast Cancer Detection Using Infrared Thermal Imaging and a Deep Learning Model

Sensors ◽

10.3390/s18092799 ◽

2018 ◽

Vol 18 (9) ◽

pp. 2799 ◽

Cited By ~ 45

Author(s):

Sebastien Mambou ◽

Petra Maresova ◽

Ondrej Krejcar ◽

Ali Selamat ◽

Kamil Kuca

Keyword(s):

Breast Cancer ◽

Deep Learning ◽

Cancer Detection ◽

Infrared Image ◽

Breast Cancer Diagnosis ◽

Diagnostic Techniques ◽

Breast Cancer Detection ◽

Thermal Activity ◽

Infrared Thermal Imaging ◽

Detection Techniques

Women’s breasts are susceptible to developing cancer; this is supported by a recent study from 2016 showing that 2.8 million women worldwide had already been diagnosed with breast cancer that year. The medical care of a patient with breast cancer is costly and, given the cost and value of the preservation of the health of the citizen, the prevention of breast cancer has become a priority in public health. Over the past 20 years several techniques have been proposed for this purpose, such as mammography, which is frequently used for breast cancer diagnosis. However, false positives of mammography can occur in which the patient is diagnosed positive by another technique. Additionally, the potential side effects of using mammography may encourage patients and physicians to look for other diagnostic techniques. Our review of the literature first explored infrared digital imaging, which assumes that a basic thermal comparison between a healthy breast and a breast with cancer always shows an increase in thermal activity in the precancerous tissues and the areas surrounding developing breast cancer. Furthermore, through our research, we realized that a Computer-Aided Diagnostic (CAD) undertaken through infrared image processing could not be achieved without a model such as the well-known hemispheric model. The novel contribution of this paper is the production of a comparative study of several breast cancer detection techniques using powerful computer vision techniques and deep learning models.

Download Full-text

Fuzzy Logic for Breast Cancer Diagnosis Using Medical Thermogram Images

Medical Imaging ◽

10.4018/978-1-5225-0571-6.ch014 ◽

2017 ◽

pp. 354-388 ◽

Cited By ~ 1

Author(s):

Surekha Kamath

Keyword(s):

Breast Cancer ◽

Fuzzy Logic ◽

Early Detection ◽

Early Diagnosis ◽

Cause Of Death ◽

Cancer Diagnosis ◽

Breast Cancer Diagnosis ◽

Detection Technique ◽

Breast Diseases ◽

Benign Neoplasms

In this chapter, how medical thermography can be utilized as early detection technique for breast cancer with fuzzy logic is explained. Breast cancer is the leading cause of death among women. This fact justifies researches to reach early diagnosis, improving patients' life expectancies. Moreover, there are other pathologies, such as cysts and benign neoplasms, that deserve investigation. In the last ten years, the infrared thermography has shown to be a promising technique to early diagnosis of breast pathologies. Works on this subject presented results that justify the thermography as a complementary exam to detect breast diseases. Various algorithms that can be utilized for Breast Cancer diagnosis utilizing medical thermography are listed and also the advantages of medical thermography over other imaging modalities is given.

Download Full-text

Survival in patients with breast cancer with bone metastasis: a Danish population-based cohort study on the prognostic impact of initial stage of disease at breast cancer diagnosis and length of the bone metastasis-free interval

BMJ Open ◽

10.1136/bmjopen-2015-007702 ◽

2015 ◽

Vol 5 (4) ◽

pp. e007702-e007702 ◽

Cited By ~ 19

Author(s):

K. Cetin ◽

C. F. Christiansen ◽

C. Svaerke ◽

J. B. Jacobsen ◽

H. T. Sorensen

Keyword(s):

Breast Cancer ◽

Cohort Study ◽

Bone Metastasis ◽

Cancer Diagnosis ◽

Breast Cancer Diagnosis ◽

Population Based ◽

Prognostic Impact ◽

Danish Population ◽

Free Interval ◽

Initial Stage

Download Full-text

Breast Ultrasound Elastography is the Most Mature and Well-documented Method for the Measurement of Tissue Stiffness: A Review

Faridpur Medical College Journal ◽

10.3329/fmcj.v16i1.55737 ◽

2021 ◽

Vol 16 (1) ◽

pp. 42-44

Author(s):

Hafizur Rahman

Keyword(s):

Breast Cancer ◽

Cancer Detection ◽

Cancer Diagnosis ◽

Imaging Technique ◽

Breast Cancer Diagnosis ◽

Breast Cancer Detection ◽

Ultrasound Elastography ◽

Tissue Stiffness ◽

Detection Techniques ◽

Non Invasive

Breast cancer is the most common malignancy and one of the leading causes of death in females worldwide. North America has one of the highest incidence breast cancer rates in the world, making breast cancer awareness a high priority. Only in the USA, 527 women are expected to be diagnosed with breast cancer while 110 women will die of it per day. Central to the importance of breast cancer diagnosis is the fact that almost one-third of the latter group could survive if their cancer is detected and treated early. In a worldwide context, this translates into nearly 400,000 lives that could be saved every year as a result of early detection. As such; developing technique that can help to detect and diagnose breast cancer at early stage can have a great impact on survival and quality of life of breast cancer patients. Conventional breast cancer screening and detection techniques such as clinical breast examination and X- ray mammography are known to have low sensitivity. Breast magnetic resonance imaging (MRI) is more sensitive modality for breast cancer detection, however, MRI is costly and has been shown to have low specificity for breast cancer diagnosis. Dynamic contrast-enhanced MRI has been demonstrated to provide a good sensitivity and specificity for differentiation of benign versus malignant lesions, due to altered angiogenesis mechanisms in tumors. However, in addition to being costly, requires injection of exogenous contrast agents to provide such contrast. An alternate imaging technique for breast cancer detection employs tissue stiffness as contrast mechanism. The technique is founded on the fact that alterations in breast tissue stiffness are frequently associated with pathology. Ultrasound elastography is the most mature and well-documented method for the measurement of tissue stiffness. Elastographybased imaging technique has received substantial attention in recent years for non-invasive assessment of tissue mechanical properties. These techniques take advantage of changed soft tissue elasticity in various pathologies to yield qualitative and quantitative information that can be used for diagnostic purpose. Measurements are acquired in specialized imaging modes that can detect tissue stiffness in response to an applied mechanical force. Ultrasoundbased methods are of particular interest due to its many inherent advantages, such as wide availability including at the bedside and relatively low cost. While ultrasound elastography has shown promising results for non-invasive assessment of breast stiffness is emerging. Faridpur Med. Coll. J. 2021;16(1):42-44

Download Full-text

Fuzzy Logic for Breast Cancer Diagnosis Using Medical Thermogram Images

Fuzzy Expert Systems for Disease Diagnosis - Advances in Medical Technologies and Clinical Practice ◽

10.4018/978-1-4666-7240-6.ch007 ◽

2015 ◽

pp. 168-199

Author(s):

Surekha Kamath

Keyword(s):

Breast Cancer ◽

Fuzzy Logic ◽

Early Detection ◽

Early Diagnosis ◽

Cause Of Death ◽

Cancer Diagnosis ◽

Breast Cancer Diagnosis ◽

Detection Technique ◽

Breast Diseases ◽

Benign Neoplasms

Download Full-text

Plasma-Derived Extracellular Vesicles Circular RNAs Serve as Biomarkers for Breast Cancer Diagnosis

Frontiers in Oncology ◽

10.3389/fonc.2021.752651 ◽

2021 ◽

Vol 11 ◽

Author(s):

Li Lin ◽

Geng-Xi Cai ◽

Xiang-Ming Zhai ◽

Xue-Xi Yang ◽

Min Li ◽

...

Keyword(s):

Breast Cancer ◽

Early Diagnosis ◽

Women’S Health ◽

Women's Health ◽

Cancer Patients ◽

Extracellular Vesicles ◽

Cancer Diagnosis ◽

Expression Profiles ◽

Breast Cancer Diagnosis ◽

Breast Cancer Patients

Breast cancer is the second cause of cancer-associated death among women and seriously endangers women’s health. Therefore, early identification of breast cancer would be beneficial to women’s health. At present, circular RNA (circRNA) not only exists in the extracellular vesicles (EVs) in plasma, but also presents distinct patterns under different physiological and pathological conditions. Therefore, we assume that circRNA could be used for early diagnosis of breast cancer. Here, we developed classifiers for breast cancer diagnosis that relied on 259 samples, including 144 breast cancer patients and 115 controls. In the discovery stage, we compared the genome-wide long RNA profiles of EVs in patients with breast cancer (n=14) and benign breast (n=6). To further verify its potential in early diagnosis of breast cancer, we prospectively collected plasma samples from 259 individuals before treatment, including 144 breast cancer patients and 115 controls. Finally, we developed and verified the predictive classifies based on their circRNA expression profiles of plasma EVs by using multiple machine learning models. By comparing their circRNA profiles, we found 439 circRNAs with significantly different levels between cancer patients and controls. Considering the cost and practicability of the test, we selected 20 candidate circRNAs with elevated levels and detected their levels by quantitative real-time polymerase chain reaction. In the training cohort, we found that BCExoC, a nine-circRNA combined classifier with SVM model, achieved the largest AUC of 0.83 [95% CI 0.77-0.88]. In the validation cohort, the predictive efficacy of the classifier achieved 0.80 [0.71-0.89]. Our work reveals the application prospect of circRNAs in plasma EVs as non-invasive liquid biopsies in the diagnosis and management of breast cancer.

Download Full-text