The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
Early Diagnosis of Breast Cancer
Open accessAbstract
Early-stage cancer detection could reduce breast cancer death rates significantly in the long-term. The most critical point for best prognosis is to identify early-stage cancer cells. Investigators have studied many breast diagnostic approaches, including mammography, magnetic resonance imaging, ultrasound, computerized tomography, positron emission tomography and biopsy. However, these techniques have some limitations such as being expensive, time consuming and not suitable for young women. Developing a high-sensitive and rapid early-stage breast cancer diagnostic method is urgent. In recent years, investigators have paid their attention in the development of biosensors to detect breast cancer using different biomarkers. Apart from biosensors and biomarkers, microwave imaging techniques have also been intensely studied as a promising diagnostic tool for rapid and cost-effective early-stage breast cancer detection. This paper aims to provide an overview on recent important achievements in breast screening methods (particularly on microwave imaging) and breast biomarkers along with biosensors for rapidly diagnosing breast cancer.
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Cites
Diagnostic Performance of Digital versus Film Mammography for Breast-Cancer Screening
Pisano, E. D., Gatsonis, C., Hendrick, E., Yaffe, M., Baum, J. K., Acharyya, S., Conant, E. F., Fajardo, L. L., Bassett, L., D'Orsi, C., et al. (2005) · New England Journal of Medicine · doi:10.1056/nejmoa052911 · Cited by 1,940
Screening for Breast Cancer: An Update for the U.S. Preventive Services Task Force
Nelson, H. D., Tyne, K., Naik, A., Bougatsos, C., Chan, B. K., & Humphrey, L. (2009) · Annals of Internal Medicine · doi:10.7326/0003-4819-151-10-200911170-00009 · Cited by 1,248
Nanomaterials based electrochemical sensors for biomedical applications
Chen, A., & Chatterjee, S. (2013) · Chemical Society Reviews · doi:10.1039/c3cs35518g · Cited by 956
Bertoli, G., Cava, C., & Castiglioni, I. (2015) · Theranostics · doi:10.7150/thno.11543 · Cited by 815
Diagnostic Performance of Digital versus Film Mammography for Breast-Cancer Screening
Pisano, E. D., Gatsonis, C., Hendrick, E., Yaffe, M., Baum, J. K., Acharyya, S., Conant, E. F., Fajardo, L. L., Bassett, L., D???Orsi, C., et al. (2006) · Obstetrical & Gynecological Survey · doi:10.1097/01.ogx.0000201894.94616.71 · Cited by 482
Microwave System for Head Imaging
Mohammed, B. J., Abbosh, A. M., Mustafa, S., & Ireland, D. (2013) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2013.2277562 · Cited by 384
Sarica, O., Ozturk, E., Demirkurek, H. C., & Uluc, F. (2013) · Breast Care · doi:10.1159/000355833 · Cited by 356
Azimzadeh, M., Rahaie, M., Nasirizadeh, N., Ashtari, K., & Naderi-Manesh, H. (2016) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2015.09.020 · Cited by 342
Breast Ultrasonography: State of the Art
Hooley, R. J., Scoutt, L. M., & Philpotts, L. E. (2013) · Radiology · doi:10.1148/radiol.13121606 · Cited by 324
Proteomics-based identification of RS/DJ-1 as a novel circulating tumor antigen in breast cancer.
Naour, F. L., Misek, D. E., Krause, M. C., Deneux, L., Giordano, T. J., Scholl, S., & Hanash, S. (2001) · PubMed · Cited by 306
Cited by
Breast Cancer Histopathology Image Classification Using an Ensemble of Deep Learning Models
Hameed, Z., Zahia, S., Garcia-Zapirain, B., Javier Aguirre, J., & María Vanegas, A. (2020) · Sensors · doi:10.3390/s20164373 · Cited by 277
Metadata
- Authors
- Wang, L.
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2017-07-05
- Volume
- 17
- Issue
- 7
- Pages
- 1572
- ISSN
- 1424-8220 (ISSN-L)
- OpenAlex
- W2727347885
- Cited by
- 549
- References
- 60
- FWCI
- 10.71
- Type
- journal article
- Language
- en
- Open access
- gold
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
