Diagnóstico y caracterización de cáncer mamario en seres humanos: Una revisión

El cáncer de mama es una enfermedad de tipo clonal ya sea por mutación adquirida o por mutación de línea germinal que introduce una transformación significativa en la estructura anatómica del parénquima mamario o en los elementos que le sirven de soporte. En diversos países, las alarmantes estadísti...

Full description

Autores:: Sandra Vargas, Sandra
Vera, Miguel

Tipo de recurso:

Fecha de publicación:: 2021

Institución:: Universidad Simón Bolívar

Repositorio:: Repositorio Digital USB

Idioma:: spa

id	USIMONBOL2_de4674f12e7a41a25ae23e354d4bb6c8
oai_identifier_str	oai:bonga.unisimon.edu.co:20.500.12442/9697
network_acronym_str	USIMONBOL2
network_name_str	Repositorio Digital USB
repository_id_str
dc.title.spa.fl_str_mv	Diagnóstico y caracterización de cáncer mamario en seres humanos: Una revisión
dc.title.translated.eng.fl_str_mv	Diagnosis and characterization of breast cancer in humans: A review
title	Diagnóstico y caracterización de cáncer mamario en seres humanos: Una revisión
spellingShingle	Diagnóstico y caracterización de cáncer mamario en seres humanos: Una revisión Cáncer mamario imagenología médica Operadores inteligentes Breast cancer Medical imaging Artificial intelligence operators
title_short	Diagnóstico y caracterización de cáncer mamario en seres humanos: Una revisión
title_full	Diagnóstico y caracterización de cáncer mamario en seres humanos: Una revisión
title_fullStr	Diagnóstico y caracterización de cáncer mamario en seres humanos: Una revisión
title_full_unstemmed	Diagnóstico y caracterización de cáncer mamario en seres humanos: Una revisión
title_sort	Diagnóstico y caracterización de cáncer mamario en seres humanos: Una revisión
dc.creator.fl_str_mv	Sandra Vargas, Sandra Vera, Miguel
dc.contributor.author.none.fl_str_mv	Sandra Vargas, Sandra Vera, Miguel
dc.subject.spa.fl_str_mv	Cáncer mamario imagenología médica Operadores inteligentes
topic	Cáncer mamario imagenología médica Operadores inteligentes Breast cancer Medical imaging Artificial intelligence operators
dc.subject.eng.fl_str_mv	Breast cancer Medical imaging Artificial intelligence operators
description	El cáncer de mama es una enfermedad de tipo clonal ya sea por mutación adquirida o por mutación de línea germinal que introduce una transformación significativa en la estructura anatómica del parénquima mamario o en los elementos que le sirven de soporte. En diversos países, las alarmantes estadísticas asociadas con la muerte por este tipo de cáncer justifican el enorme esfuerzo que está haciendo la comunidad internacional para abordar este problema de salud. Mediante el presente trabajo, para construir el estado del arte actual del cáncer mamario, se realizó una revisión sistemática de diversas fuentes de información que incluyó un total de ochenta y cinco documentos o unidades de análisis. Los hallazgos fundamentales muestran que, históricamente, se ha producido una constante evolución en el desarrollo y perfeccionamiento tanto de la terapéutica como de las técnicas de detección del cáncer mamario, lo cual ha estado respaldado por la incorporación de los avances tecnológicos en la rutina clínica y en la cultura de los sujetos aquejados por esta patología. En ese sentido, el análisis de los mencionados documentos permitió detectar una importante transformación de los protocolos de diagnóstico y seguimiento de este tipo de cáncer, una profusa aplicación de las técnicas imagenológicas médicas y un visible posicionamiento de las técnicas de aprendizaje automático, especialmente de los operadores de inteligencia artificial, como elementos fundamentales para el desarrollo de un sinnúmero de estrategias bioingenieriles las cuales pueden ser muy útiles como apoyo clínico para los especialistas oncólogos que estudian el cáncer mamario.
publishDate	2021
dc.date.issued.none.fl_str_mv	2021
dc.date.accessioned.none.fl_str_mv	2022-05-13T18:59:34Z
dc.date.available.none.fl_str_mv	2022-05-13T18:59:34Z
dc.type.coarversion.fl_str_mv	http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.coar.fl_str_mv	http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.driver.eng.fl_str_mv	info:eu-repo/semantics/article
dc.type.spa.spa.fl_str_mv	Artículo científico
dc.identifier.issn.none.fl_str_mv	26107988
dc.identifier.uri.none.fl_str_mv	https://hdl.handle.net/20.500.12442/9697
dc.identifier.doi.none.fl_str_mv	http://doi.org/10.5281/zenodo.5228817
identifier_str_mv	26107988
url	https://hdl.handle.net/20.500.12442/9697 http://doi.org/10.5281/zenodo.5228817
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.rights.*.fl_str_mv	Attribution-NonCommercial-NoDerivatives 4.0 Internacional
dc.rights.coar.fl_str_mv	http://purl.org/coar/access_right/c_abf2
dc.rights.uri.*.fl_str_mv	http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.accessrights.eng.fl_str_mv	info:eu-repo/semantics/openAccess
rights_invalid_str_mv	Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv	openAccess
dc.format.mimetype.spa.fl_str_mv	pdf
dc.publisher.spa.fl_str_mv	Universidad Central de Venezuela
dc.source.spa.fl_str_mv	AVFT - Archivos Venezolanos de Farmacología y Terapéutica
dc.source.none.fl_str_mv	Vol. 40, No. 4 (2021)
institution	Universidad Simón Bolívar
bitstream.url.fl_str_mv	https://bonga.unisimon.edu.co/bitstreams/da7597db-e050-4a46-8c2a-ea315f9b966d/download https://bonga.unisimon.edu.co/bitstreams/fed8a326-95c4-445f-a670-503fec7f8e5b/download https://bonga.unisimon.edu.co/bitstreams/6c0921ab-09be-4f06-bd59-72740caa18e9/download https://bonga.unisimon.edu.co/bitstreams/5a223908-bd6a-40d5-a634-9a5b1474b12f/download https://bonga.unisimon.edu.co/bitstreams/5b1eee1e-26a5-41b8-9166-5022546676bf/download https://bonga.unisimon.edu.co/bitstreams/468fe8a7-72dc-4b1e-b648-0bd4a1378b99/download https://bonga.unisimon.edu.co/bitstreams/96667f92-953f-4783-b692-96343e293a84/download
bitstream.checksum.fl_str_mv	ba10d0359faa87f3bfdaf7334693e82d 4460e5956bc1d1639be9ae6146a50347 733bec43a0bf5ade4d97db708e29b185 c911dd1e6cc984a07fac1134a26c3c28 c911dd1e6cc984a07fac1134a26c3c28 dfd3b65186d7884edb43c58f1a2093fe dfd3b65186d7884edb43c58f1a2093fe
bitstream.checksumAlgorithm.fl_str_mv	MD5 MD5 MD5 MD5 MD5 MD5 MD5
repository.name.fl_str_mv	Repositorio Digital Universidad Simón Bolívar
repository.mail.fl_str_mv	repositorio.digital@unisimon.edu.co
_version_	1812100490210574336
spelling	Sandra Vargas, Sandra12c90a24-d450-41a5-8415-76bf6944e9e6Vera, Miguelc485e4e3-5bbd-4d00-8ec7-e5bc8a0a21e32022-05-13T18:59:34Z2022-05-13T18:59:34Z202126107988https://hdl.handle.net/20.500.12442/9697http://doi.org/10.5281/zenodo.5228817El cáncer de mama es una enfermedad de tipo clonal ya sea por mutación adquirida o por mutación de línea germinal que introduce una transformación significativa en la estructura anatómica del parénquima mamario o en los elementos que le sirven de soporte. En diversos países, las alarmantes estadísticas asociadas con la muerte por este tipo de cáncer justifican el enorme esfuerzo que está haciendo la comunidad internacional para abordar este problema de salud. Mediante el presente trabajo, para construir el estado del arte actual del cáncer mamario, se realizó una revisión sistemática de diversas fuentes de información que incluyó un total de ochenta y cinco documentos o unidades de análisis. Los hallazgos fundamentales muestran que, históricamente, se ha producido una constante evolución en el desarrollo y perfeccionamiento tanto de la terapéutica como de las técnicas de detección del cáncer mamario, lo cual ha estado respaldado por la incorporación de los avances tecnológicos en la rutina clínica y en la cultura de los sujetos aquejados por esta patología. En ese sentido, el análisis de los mencionados documentos permitió detectar una importante transformación de los protocolos de diagnóstico y seguimiento de este tipo de cáncer, una profusa aplicación de las técnicas imagenológicas médicas y un visible posicionamiento de las técnicas de aprendizaje automático, especialmente de los operadores de inteligencia artificial, como elementos fundamentales para el desarrollo de un sinnúmero de estrategias bioingenieriles las cuales pueden ser muy útiles como apoyo clínico para los especialistas oncólogos que estudian el cáncer mamario.Breast cancer is a clonal type of disease either by acquired mutation or by germ line that introduces a significant transformation in the anatomical structure of the breast parenchyma or in the elements that support it. In several countries, the alarming statistics associated with death from this type of cancer justify the enormous effort being made by the international community to address this health problem. To build the current state of the art of breast cancer, through the present work, a systematic review of diverse sources of information was carried out, which included a total of eighty-five documents or analysis units. The fundamental findings show that, historically, there has been a constant evolution in the development and improvement of both the therapeutics and the techniques of breast cancer detection, which has been supported by the incorporation of technological advances in the clinical routine and in the culture of the subjects affected by this pathology. In that sense, the analysis of the mentioned documents allowed detecting an important transformation of the protocols of diagnosis and monitoring of this type of cancer, a profuse application of the medical imaging techniques and a visible positioning of the automatic learning techniques, especially of the artificial intelligence operators, as fundamental elements for the development of an endless number of bioengineering strategies which can be very useful as clinical support for the oncology specialists who study breast cancer.pdfspaUniversidad Central de VenezuelaAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2AVFT - Archivos Venezolanos de Farmacología y TerapéuticaVol. 40, No. 4 (2021)Cáncer mamarioimagenología médicaOperadores inteligentesBreast cancerMedical imagingArtificial intelligence operatorsDiagnóstico y caracterización de cáncer mamario en seres humanos: Una revisiónDiagnosis and characterization of breast cancer in humans: A reviewinfo:eu-repo/semantics/articleArtículo científicohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1Organización Mundial de la Salud (OMS): Enfermedades no trans- misibles: perfiles de países 2018. https://www.who.int/nmh/coun- tries/es/Bardia A, Hurvitz S. Targeted therapy for premenopausal women with HR+, HER2− advanced breast cancer: focus on special consid- erations and latest advances. Clin Cancer Res 2018;24:5206-5218Sociedad americana de oncología clínica 2020 https://www.asco. orgDan L. Longo, Dennis L. Kasper, J. Larry Jameson, Anthony S. Fauci, Stephen L. Hauser, Joseph Loscalzo Harrison. Principios de Medicina Interna, 18e McGrawHill 2012Organización Panamericana de la Salud (OPS) Perfiles de país so- bre cáncer 2020 https://www.paho.org/hq/index.php?option=com_ content&view=article&id=15716:country-cancer-profiles- 2020&Itemid=72576&lang=esMacMahon, B., Cole, P. and Brown, J. (1973). Etiology of Human Breast Cancer: A Review. JNCI: Journal of the National Cancer In- stitute. 50, 21-42.1973Hashemi, S., Rafiemanesh, H., Aghamohammadi, T et al. Preva- lence of anxiety among breast cancer patients: a systematic review and meta-analysis. Breast Cancer. 27, 166-178. 2020. https://doi. org/10.1007/s12282-019-01031-9Suzuki, H., Seki, A., Hosaka, T., Matsumoto, N., Tomita, M., Taka- hashi, M., and Yamauchi, H. Effects of a structured group interven- tion on obesity among breast cancer survivors. Breast Cancer. 27, 236-242. 2020. https://doi.org/10.1007/s12282-019-01013-xNishiyama, K., Taira, N., Mizoo, T., Kochi, M et al. Infuence of breast density on breast cancer risk: a case control study in Japanese women. Breast Cancer. 27, 277-283. 2020. https://doi.org/10.1007/ s12282-019-01018-6Nakagawa, A., Fujimoto, H., Nagashima, T et al. Histological fea- tures of skin and subcutaneous tissue in patients with breast cancer who have received neoadjuvant chemotherapy and their relation- ship to post-treatment edema. Breast Cancer. 27, 77-84. 2020. https://doi.org/10.1007/s12282-019-00996-xNaito, Y., Kai, Y., Ishikawa, T et al. Chemotherapy-induced nausea and vomiting in patients with breast cancer: a prospective cohort study. Breast Cancer. 27, 122-128. 2020. https://doi.org/10.1007/ s12282-019-01001-1.Izumori, A., Kokubu, Y., Sato, K et al. Usefulness of second-look ultrasonography using anatomical breast structures as indicators for magnetic resonance imaging-detected breast abnormalities. Breast Cancer. 27,129-139.2020. https://doi.org/10.1007/s12282- 019-01003-zPereira, H., Pinder, S.E., Sibbering, D et al. Pathological prognosticfactors in breast cancer. IV: Should you be a typer or a grader? A comparative study of two histological prognostic features in oper- able breast carcinoma. Histopathology. 27, 219-226. 1995. https:// doi.org/10.1111/j.1365-2559.1995.tb00213.xAswathy, M.A., Jagannath, M. Detection of breast cancer on digi- tal histopathology images: Present status and future possibili- ties. Informatics in Medicine Unlocked. 8, 74-79. 2017. https://doi. org/10.1016/j.imu.2016.11.001Yu, C., Chen, H., Li, Y et al. Breast cancer classification in patho- logical images based on hybrid features. Multimedia Tools and Ap- plications. 78, 21325-21345. 2019. https://doi.org/10.1007/s11042- 019-7468-9Spanhol, F. A., Oliveira, L. S., Petitjean, C., & Heutte, L. Breast cancer histopathological image classification using Convolutional Neural Networks. 2016 International Joint Conference on Neural Networks (IJCNN) - IEEE Xplore Digital Library, 2560-2567. 2016. https://doi.org/10.1109/IJCNN.2016.7727519Davidson, T., Rendi, M., Frederick, P et al. Breast cancer prognostic factors in the digital era: Comparison of Nottingham grade using whole slide images and glass slides. Journal of Pathology Informat- ics 10(11).2018. https://dx.doi.org/10.4103%2Fjpi.jpi_29_18Elston, C. W., & Ellis, I. O. Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experi- ence from a large study with long-term follow-up. Histopathology. 19, 403–410. 1991. https://doi.org/10.1111/j.1365-2559.1991.tb00229.xRakha, E.A., Reis-Filho, J.S., Baehner, F et al. Breast cancer prognostic classification in the molecular era: the role of histo- logical grade. Breast Cancer Research. 12(207). 2010. https://doi. org/10.1186/bcr2607Nagura, N., Hayashi, N., Takei, J et al. Breast reconstruction after risk-reducing mastectomy in BRCA mutation carriers. Breast Can - cer. 27,70-76. 2020. https://doi.org/10.1007/s12282-019-00995-yNicolini, A., Ferrari, P., and Duffy, M. J. Prognostic and Predictive Biomarkers in Breast Cancer: Past, Present and Future. Seminars in Cancer Biology. 52(1) 56-73. 2018. https://doi.org/10.1016/j.sem- cancer.2017.08.01De Sousa Almeida-Filho, B., De Luca Vespoli, H., Pessoa, E et al. Vitamin D deficiency is associated with poor breast cancer prog- nostic features in postmenopausal women. Journal of Steroid Bio- chemistry and Molecular Biology. 174, 284-289. 2017. https://doi. org/10.1016/j.jsbmb.2017.10.009Yersal, O., Barutca, S. Biological subtypes of breast cancer: Prog- nostic and therapeutic implications. World Journal of Clinical On- cology. 5(3)412–424.2014. https://dx.doi.org/10.5306%2Fwjco. v5.i3.41Farbood, A., Sahmeddini, M.A., Bayat, S. and Karami, N. The ef- fect of preoperative depression and anxiety on heart rate variability in women with breast cancer. Breast Cancer 27, 912-918. 2020. https://doi.org/10.1007/s12282-020-01087-yEl Abbass, K. A., Abdellateif, M. S., Gawish, A. M et al. The Role of breast cancer stem cells and some related molecular biomarkers in Metastatic and Non-metastatic breast cancer. Clinical Breast Can- cer. 20(4)373-384. 2020. https://doi.org/10.1016/j.clbc.2019.11.008Al-Hajj, M., Wicha, M. S., Benito-Hernandez, A et al. Prospective identification of tumorigenic breast cancer cells. Proceedings of the National Academy of Sciences. 100(7), 3983–3988. 2003. https:// doi.org/10.1073/pnas.0530291100Izci, F., Ilgun, A. S., Findikli, E., and Ozmen, V. Psychiatric Symp- toms and Psychosocial Problems in Patients with Breast Can- cer. Journal of Breast Health. 12, 94-101. 2016. https://dx.doi. org/10.5152%2Ftjbh.2016.3041Shimoi, T., Nagai, S.E., Yoshinami, T et al. The Japanese Breast Cancer Society Clinical Practice Guidelines for systemic treatment of breast cancer, 2018 edition. Breast Cancer. 27, 322-331. 2020. https://doi.org/10.1007/s12282-020-01085-0Holm, J., Eriksson, L., Ploner, A et al. Assessment of breast can- cer risk factors reveals subtype heterogeneity. Cancer Research. 77(13).2017. https://doi.org/10.1158/0008-5472.CAN-16-2574Sun, Y.-S., Zhao, Z., Yang, Z.-N et al. Risk Factors and Preven- tions of Breast Cancer. International journal of biological sciences. 13(11), 1387-1397. 2017. https://dx.doi.org/10.7150%2Fijbs.21635.Kamińska, M., Ciszewski, T., Łopacka-Szatan, K et al. Breast cancer risk factors. Przeglad Menopauzalny. 14(3),196-202.2015. https://dx.doi.org/10.5114%2Fpm.2015.54346Barnard, M. E., Boeke, C. E., and Tamimi, R. M. Established breast cancer risk factors and risk of intrinsic tumor subtypes. Biochimi- ca et Biophysica Acta - Reviews on Cancer. 1856(1),73-85.2015. https://doi.org/10.1016/j.bbcan.2015.06.002Jalalian, A., Mashohor, S., Mahmud, R et al. Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection. EXCLI. 16,113-137. 2017. https://dx.doi. org/10.17179%2Fexcli2016-701Huang, Q., Yang, F., Liu, L., Li, X. Automatic segmentation of breast lesions for interaction in ultrasonic computer-aided diagnosis. In- formation Sciences. 314,293-310. 2015. https://doi.org/10.1016/j. ins.2014.08.021Khoulqi, I., & Idrissi, N. Breast cancer image segmentation and classification. Proceedings of the 4th International Conference on Smart City Applications - SCA ’19, Oct 2019, Casablanca, Morocco. DOI:10.1145/3368756.3369039Saeed, J. N. A survey of ultrasonography breast cancer image segmentation techniques. Academic Journal of Nawroz University (AJNU). 9(1).2020. https://doi.org/10.25007/ajnu.v9n1a523Anuranjeeta, A., Shukla, K. K., Tiwari, A., Sharma, S. Classification of Histopathological images of Breast Cancerous and Non-Cancer- ous Cells Based on Morphological features. Biomedical & Phar- macology Journal. 10(1),353-366.2017. http://dx.doi.org/10.13005/ bpj/1116Eapena, M. M., Ancelita, M. S. J. A., and Geetha, G. Segmenta- tion of Tumors from Ultrasound Images with PAORGB. Symposium: 2nd International Symposium on Big Data and Cloud Computing, Procedia Computer Science Journal 50, 663-668.2015. https://doi. org/10.1016/j.procs.2015.04.100Oraevsky, A. A., Clingman, B., Zalev, J. Clinical optoacoustic im- aging combined with ultrasound for coregistered functional and anatomical mapping of breast tumors. Photoacoustics. 12, 30-45. https://doi.org/10.1016/j.pacs.2018.08.003Daoud, M. I., Atallah, A. A., Awwad, F et al. Automatic superpixel- based segmentation method for breast ultrasound images. Ex- pert Systems with Applications. 121(1),78-96. 2019. https://doi. org/10.1016/j.eswa.2018.11.024Huang, Q., Huang, Y., Luo, Y., Yuan, F., & Li, X. Segmentation of breast ultrasound image with semantic classification of superpixels. Medical Image Analysis. 61, 2020. https://doi.org/10.1016/j.me- dia.2020.101657Vargas S, Vera M.I, Vera M, Salazar-Torres J, Huérfano Y, Valbuena O, Gelvez-Almeida E 2019 Space-occupying lesions identification in mammary glands using a mixed computational strategy 2019 J. Phys.: Conf. Ser. 1414 doi:10.1088/1742-6596/1414/1/012016Dabass, J., Arora, S., Vig, R., Hanmandlu, M. Segmentation Tech- niques for Breast Cancer Imaging Modalities-A Review. 9th Interna- tional Conference on Cloud Computing, Data Science & Engineer- ing (Confluence) - IEEE Explore Digital Library. 2019. DOI:10.1109/ confluence.2019.8776937Patel, B.C., Sinha, G.R. and Soni, D. Detection of masses in mam- mographic breast cancer images using modified histogram based adaptive thresholding (MHAT) method. International Journal Bio- medical Engineering and Technology. 29(2).134-153.2019Keatmanee, C., Chaumrattanakul, U., Kotani, K., Makhanov, S. S. Initialization of active contours for segmentation of breast cancer via fusion of ultrasound, Doppler, and elasticity images. Ultrasonics. 94,438-453.2019. https://doi.org/10.1016/j.ultras.2017.12.008Gayathri, B. K., and Raajan, P. A survey of breast cancer detection based on image segmentation techniques. 2016 International Con- ference on Computing Technologies and Intelligent Data Engineer- ing, ICCTIDE’16. DOI:10.1109/icctide.2016.7725345Xian, M., Zhang, Y. and Cheng, H. D. Fully automatic segmentaon tlon of breast ultrasound images based on breast characteristics in space and frequency domains. Pattern Recognition. 48(2),485- 497.2015. https://doi.org/10.1016/j.patcog.2014.07.026Rouhi, R., Jafari, M. Classification of benign and malignant breast tumors based on hybrid level set segmentation. Expert Systems with Applications. 46, 45-59. https://doi.org/10.1016/j.eswa.2015.10.01Singh, V. K., Rashwan, H. A., Romani, S et al. Breast tumor segmen- tation and shape classification in mammograms using generative adversarial and convolutional neural network. Expert Systems with Applications. 139,2020.https://doi.org/10.1016/j.eswa.2019.112855Manzi, B.M. Segmentation and classification of breast cancer pathol- ogies in histological images based on morphological patterns. Final Master’s Project Universitat Rovira i Virgili. 2018. Retrieved from: http://repositori.urv.cat/fourrepopublic/search/item/TFM%3A357Huang, Q., Luo, Y., Zhang, Q. Breast ultrasound image segmenta- tion: a survey. International Journal of Computer Assisted Radiology and Surgery.Rodrigues, R., Braz, R., Pereira, M et al. A Two-Step Segmentation Method for Breast Ultrasound Masses Based on Multi-resolution Analysis. Journal- Published in Journal: Ultrasound in Medicine & Biology. 41(6),1737-1748.2015. https://doi.org/10.1016/j.ultrasmed- bio.2015.01.012Rodrigues, R., Braz, R., Pereira, M et al. A Two-Step Segmentation Method for Breast Ultrasound Masses Based on Multi-resolution Analysis. Journal- Published in Journal: Ultrasound in Medicine & Biology. 41(6),1737-1748.2015. https://doi.org/10.1016/j.ultrasmed- bio.2015.01.012Xian, M., Zhang, Y., Cheng, H. D et al. Automatic breast ultra- sound image segmentation: A survey. Pattern Recognition. 79,340- 355.2018. https://doi.org/10.1016/j.patcog.2018.02.012Kooi, T., Litjens, G., van Ginneken, B et al. Large scale deep learn- ing for computer aided detection of mammographic lesions. Medi- cal Image Analysis. 35,303-312.2017. https://doi.org/10.1016/j.me- dia.2016.07.00Men, K., Zhang, T., Chen, X et al. Fully automatic and robust seg- mentation of the clinical target volume for radiotherapy of breast cancer using big data and deep learning. Physica Medica. 50, 13- 19. 2018. https://doi.org/10.1016/j.ejmp.2018.05.006Sathish, D., Kamath, S., Prasad, K et al. Asymmetry analysis of breast thermograms using automated segmentation and texture features. Signal, Image and Video Processing. 11, 745-752. 2017. doi:10.1007/s11760-016-1018-yWan, T., Cao, J., Chen, J. and Qin, Z. Automated grading of breast cancer histopathology using cascaded ensemble with combination of multi-level image features. Neurocomputing. 229, 34-44. https:// doi.org/10.1016/j.neucom.2016.05.084Ragab D. A., Sharkas M., Marshall S., Ren J. Breast cancer detec- tion using deep convolutional neural networks and support vector machines. Journal PeerJ. 7, e6201. 2019. https://doi.org/10.7717/ peerj.6201Dhungel, N., Carneiro, G., and Bradley, A. P. A deep learning ap- proach for the analysis of masses in mammograms with minimal user intervention. Medical Image Analysis. 37, 114-128. 2017. https://doi.org/10.1016/j.media.2017.01.0Jiang, F., Liu, H., Yu, S., Xie, Y. Breast mass lesion classification inmammograms by transfer learning. Conference: ACM ICBCB ‘17: Proceedings of the 5th International Conference on Bioinformat- ics and Computational Biology, ICBCB ‘17, Jan 2017, Hong Kong, China. pp. 59-62. DOI: 10.1145/3035012.3035022Xu, Y., Wang, Y., Yuan, J et al. Medical breast ultrasound image seg- mentation by machine learning. Ultrasonics. 91,1-9.2019. https:// doi.org/10.1016/j.ultras.2018.07.006Sathish, D., Kamath, S., Prasad, K., Kadavigere, R. Role of normal- ization of breast thermogram images and automatic classification of breast cancer. The Visual Computer. 35, 57-70. 2019. https://doi. org/10.1007/s00371-017-1447-9Jaglan P., Dass R., Duhan M. A Comparative Analysis of Various Image Segmentation Techniques. Krishna C., Dutta M., Kumar R. (eds) Proceedings of 2nd International Conference on Communica- tion, Computing and Networking. Lecture Notes in Networks and Systems, vol 46. Springer, Singapore. https://doi.org/10.1007/978- 981-13-1217-5_36Hallad, S., Hubballi, R. Comparing Three Neural Network Tech- niques in the Classification of Breast Cancer. International Journal of Advance Research, Ideas and Innovations in Technology. 3(4). 2017.Taneja, A., Ranjan, P., Ujjlayan, A. A performance study of image segmentation techniques. 2015 4th International Conference on Re- liability, Infocom Technologies and Optimization, Trends and Future Directions, IEEE Xplore Digital Library, ICRITO’15. DOI: 10.1109/ ICRITO.2015.7359305Gardezi S.J.S., Elazab A, Lei B, Wang T. Breast Cancer Detection and Diagnosis Using Mammographic Data: Systematic Review. JMIR Journal of Medical Internet Research. 21(7), 2019. http://doi. org/10.2196/14464Hatipoglu, N., Bilgin, G. Cell segmentation in histopathological im- ages with deep learning algorithms by utilizing spatial relationships. Medical & Biological. 55, 1829-1848. 2017. https://doi.org/10.1007/ s11517-017-1630-1Kumar, N., Verma, R., Sharma, S et al. A Dataset and a Technique for Generalized Nuclear Segmentation for Computational Pathol- ogy. IEEE Transactions on Medical Imaging. 36(7). 2017. https:// doi.org/10.1109/TMI.2017.26774Hu, Z., Tang, J., Wang, Z et al. Deep learning for image-based cancer detection and diagnosis − A survey. Pattern Recognition. 83,134-149.2018. https://doi.org/10.1016/j.patcog.2018.05.014Gecer, B., Aksoy, S., Mercan, E et al. Detection and classification of cancer in whole slide breast histopathology images using deep con- volutional networks. Pattern Recognition. 84, 345-356. 2018. https:// doi.org/10.1016/j.patcog.2018.07.022Spanhol, F. A., Oliveira, L. S., Petitjean, C., & Heutte, L. Deep features for breast cancer histopathological image classification. 2017 IEEE International Conference on Systems, Man and Cyber- netics (SMC). Banff, Canada, Oct 2017. https://doi.org/10.1109/ SMC.2017.8122889Aresta, G., Araújo, T., Kwok, S et al. BACH: Grand challenge on breast cancer histology images. Medical Image Analysis. 56,122- 139.2019. https://doi.org/10.1016/j.media.2019.05.01Ting, F. F., Tan, Y. J., & Sim, K. S. Convolutional neural network improvement for breast cancer classification. Expert Systems with Applications. 120, 103-115. 2018. https://doi.org/10.1016/j. eswa.2018.11.008Guo, Z., Liu, H., Ni, H et al. A Fast and Refined Cancer Regions Segmentation Framework in Whole-slide Breast Pathological Imag- es. Scientific Reports. 9, 882. 2019. https://doi.org/10.1038/s41598- 018-37492-9Veena M., Padma M.C. (2019) Detection of Breast Cancer Using Digital Breast Tomosynthesis. In: Sridhar V., Padma M., Rao K. (eds) Emerging Research in Electronics, Computer Science and Technology. Lecture Notes in Electrical Engineering, vol 545. 721- 730. Springer, Singapore. https://doi.org/10.1007/978-981-13-5802- 9_63Raghavendraa, U., Gudigara, A., Rao, T et al. Computer-aided di- agnosis for the identification of breast cancer using thermogram im- ages: A comprehensive review. 102, 2019. https://doi.org/10.1016/j. infrared.2019.103041Mahalakshmi, B., Vidhya, D., Niroja, N., & Mohankumar, P. De- termination of Breast Cancer Using Neural Networks. 2019 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS). DOI:10.1109/in- cos45849.2019.8951395Abdallah, Y.M.Y., Elgak, S., Zain, H et al. Breast cancer detection using image enhancement and segmentation algorithms. Biomedi- cal Research. 29(20), 3732-3736. 2018. https://doi.org/10.4066/ biomedicalresearch.29-18-1106Hu, Y., Guo Y., Wang, Y et al. Automatic tumor segmentation in breast ultrasound images using a dilated fully convolutional network combined with an active contour model. Medical Physics. 46(1), 215-228. 2019. https://doi.org/10.1002/mp.13268Dallali, A., Khediri, S. E., Slimen, A. and Kachouri, A. Breast tumors segmentation using Otsu method and K-means. 2018 4th Interna- tional Conference on Advanced Technologies for Signal and Im- age Processing (ATSIP) – IEEE Xplore Digital Journal. Mar 2018, Sousse, Tunisia. DOI:10.1109/atsip.2018.8364469Sun, L., He, J., Yin, X et al. An image segmentation framework for extracting tumors from breast magnetic resonance images. Journal of Innovative Optical Health Sciences. 11(4). https://doi.org/10.1142/ S1793545818500141Lakshmanan, B., & Saravanakumar, S. Nucleus Segmentation in Breast Histopathology Images. 2018 International Conference on Current Trends Towards Converging Technologies (ICCTCT). DOI:10.1109/icctct.2018.8550929Sudharshan, P. J., Petitjean, C., Spanhol, F. Multiple instance learning for histopathological breast cancer image classification. Expert Systems with Applications. 117(1), 103-111. https://doi. org/10.1016/j.eswa.2018.09.049Bardou, D., Zhang K., Ahmad, S.M. Classification of Breast Cancer Based on Histology Images Using Convolutional Neural Networks. IEEE Xplore Digital Journal. 6, pp. 24680-24693. 2018. https://doi. org/10.1109/ACCESS.2018.2831280Becker, A.S., Mueller, M., Stoffel, E et al. Classification of breast cancer in ultrasound imaging using a generic deep learning analysis software: a pilot study. BJR British Journal of Radiology of BIR the British Institute of Radiology. 99(1083), 24680-24693. 2018. https:// doi.org/10.1259/bjr.20170576Chiao, J. Y., Chen, K. Y., Liao, K. Y et al. Detection and clas- sification the breast tumors using mask R-CNN on sonograms. Medicine (Baltimore). 98(19).2019. https://doi.org/10.1097/ MD.0000000000015200Tanha, J., Salarabadi, H., Aznab, M., Farahi, A., Zoberi M. Relation- ship among prognostic indices of breast cancer using classification technique. Informatics in Medicine Unlocked. 18, 2020. https://doi. org/10.1016/j.imu.2019.100265Roy, K., Ghosh, S., Mukherjee, a et al. Breast Tumor Segmenta- tion using Image Segmentation Algorithms. 2019 International Con- ference on Opto-Electronics and Applied Optics (Optronix) IEEE Xplore Digital Library. Kolkata, India, 2019, pp. 1-5. DOI: 10.1109/ OPTRONIX.2019.8862339Cheng, J., Ni, D., Chou, Y et al. Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans. Scientific Report. 6, 24454. 2016. https://doi.org/10.1038/srep24454Mehdy, M. M., Ng, P. Y., Shair, E. F et al. Artificial Neural Networks in Image Processing for Early Detection of Breast Cancer. Compu- tational and Mathematical Methods in Medicine. Article ID 2610628. 2017. https://doi.org/10.1155/2017/261062ORIGINALPDF.pdfPDF.pdfPDFapplication/pdf707514https://bonga.unisimon.edu.co/bitstreams/da7597db-e050-4a46-8c2a-ea315f9b966d/downloadba10d0359faa87f3bfdaf7334693e82dMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://bonga.unisimon.edu.co/bitstreams/fed8a326-95c4-445f-a670-503fec7f8e5b/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-8381https://bonga.unisimon.edu.co/bitstreams/6c0921ab-09be-4f06-bd59-72740caa18e9/download733bec43a0bf5ade4d97db708e29b185MD53TEXT2021_AVFT_Diagostico-y-caracterizacion_cancer-mamario.pdf.txt2021_AVFT_Diagostico-y-caracterizacion_cancer-mamario.pdf.txtExtracted texttext/plain58519https://bonga.unisimon.edu.co/bitstreams/5a223908-bd6a-40d5-a634-9a5b1474b12f/downloadc911dd1e6cc984a07fac1134a26c3c28MD54PDF.pdf.txtPDF.pdf.txtExtracted texttext/plain58519https://bonga.unisimon.edu.co/bitstreams/5b1eee1e-26a5-41b8-9166-5022546676bf/downloadc911dd1e6cc984a07fac1134a26c3c28MD56THUMBNAIL2021_AVFT_Diagostico-y-caracterizacion_cancer-mamario.pdf.jpg2021_AVFT_Diagostico-y-caracterizacion_cancer-mamario.pdf.jpgGenerated Thumbnailimage/jpeg6520https://bonga.unisimon.edu.co/bitstreams/468fe8a7-72dc-4b1e-b648-0bd4a1378b99/downloaddfd3b65186d7884edb43c58f1a2093feMD55PDF.pdf.jpgPDF.pdf.jpgGenerated Thumbnailimage/jpeg6520https://bonga.unisimon.edu.co/bitstreams/96667f92-953f-4783-b692-96343e293a84/downloaddfd3b65186d7884edb43c58f1a2093feMD5720.500.12442/9697oai:bonga.unisimon.edu.co:20.500.12442/96972024-08-14 21:53:06.961http://creativecommons.org/licenses/by-nc-nd/4.0/Attribution-NonCommercial-NoDerivatives 4.0 Internacionalopen.accesshttps://bonga.unisimon.edu.coRepositorio Digital Universidad Simón Bolívarrepositorio.digital@unisimon.edu.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

Diagnóstico y caracterización de cáncer mamario en seres humanos: Una revisión

Publicaciones similares