Identificación de lesiones ocupantes de espacio en imágenes médicas del riñón: Una revisión
| datacite.rights | http://purl.org/coar/access_right/c_abf2 | eng |
| dc.contributor.author | Saenz, Frank | |
| dc.contributor.author | Vera, Miguel | |
| dc.contributor.author | Rodríguez, Raúl | |
| dc.date.accessioned | 2021-10-23T04:00:22Z | |
| dc.date.available | 2021-10-23T04:00:22Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | La tecnología existente para el reconocimiento automático de imágenes ha impactado el campo de la medicina apoyando, en forma cada vez más confiable, los diagnósticos que los profesionales médicos realizan de forma manual. En el caso de la detección de lesiones ocupantes de espacio (LOE) renales, se han desarrollado muchos estudios que aplican diferentes técnicas para la segmentación de imágenes del riñón, y que han generado nuevos modelos propuestos que aportan al fortalecimiento del trabajo que se viene realizando en el reconocimiento de imágenes médicas de forma automática. El presente artículo hace una descripción de los diversos avances que se han reportado en la literatura científica con respecto a la segmentación del riñón y de sus LOE en imágenes médicas de diferentes fuentes como tomografía computarizada, resonancia magnética y ultrasonido. En ese sentido, se realizó una revisión sistemática de los artículos publicados, validando el nivel científico y el medio donde se publicó a través de la plataforma Scimago Journal & Country Rank, seleccionando fechas de publicación desde el año 2005 en adelante. Las palabras claves para realizar búsqueda fueron «Kidney Tumor», «Kidney Cancer», «Kidney Segmentation», «Renal Cell Carcinoma», «Renal Tumors», «Renal Cysts», «Automatic Segmentation Kidney». Este artículo brinda un panorama del trabajo que viene desarrollando la comunidad académica y científica con respecto al reconocimiento automático de tumores renales y el avance en el desarrollo de modelos más avanzados que ofrecen un nivel más alto de sensibilidad, especificidad y precisión en la detección de enfermedades del referido órgano, particularmente, en imágenes médicas. | spa |
| dc.description.abstract | Existing technology for automatic image recognition has impacted the medicine field by supporting, in an increasingly reliable way, the diagnoses that clinicians perform manually. In the space-occupying renal lesions (SORL) detection cases, many studies have been developed that apply different techniques for kidney segmentation and they have generated new models that contribute to the strengthening of the work that has been carried out in the recognition of medical images automatically. This article describes several advances that have been reported in the scientific literature regarding kidney segmentation and it’s SORL in medical images from different sources such as computed tomography, magnetic resonance imaging, and ultrasound. In this sense, a systematic review of the published articles was carried out, validating the scientific level and the medium where it was published through the Scimago Journal & Country Rank platform, selecting publication dates from 2005 onwards. The keywords to search were “Kidney Tumor”, “Kidney Cancer”, “Kidney Segmentation”, “Renal Cell Carcinoma”, “Renal Tumors”, “Renal Cysts”, “Automatic Segmentation Kidney”. This paper provides an overview of the work that the academic and scientific community has been developing to the automatic recognition of kidney tumors and progress in the development of more advanced models that offer a higher level of sensitivity, specificity, and precision in the detection of diseases of the referred organ, particularly, in medical images. | eng |
| dc.format.mimetype | spa | |
| dc.identifier.doi | http://doi.org/10.5281/zenodo.4407983 | |
| dc.identifier.issn | 26107988 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12442/8775 | |
| dc.identifier.url | http://saber.ucv.ve/ojs/index.php/rev_aavft/article/view/21087 | |
| dc.language.iso | spa | spa |
| dc.publisher | Saber UCV, Universidad Central de Venezuela | spa |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
| dc.rights.accessrights | info:eu-repo/semantics/openAccess | eng |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
| dc.source | Revista AVFT - Archivos Venezolanos de Farmacología y Terapéutica | spa |
| dc.source | Vol. 39, No 6 (2020) | |
| dc.subject | Riñón | spa |
| dc.subject | Tumor renal | spa |
| dc.subject | Imágenes médicas | spa |
| dc.subject | Resonancia magnética | spa |
| dc.subject | Ultrasonido | spa |
| dc.subject | Tomografía computarizada | spa |
| dc.subject | Kidney | eng |
| dc.subject | Kidney tumor | eng |
| dc.subject | Medical images | eng |
| dc.subject | Magnetic resonance | eng |
| dc.subject | Computerized tomography | eng |
| dc.title | Identificación de lesiones ocupantes de espacio en imágenes médicas del riñón: Una revisión | spa |
| dc.title.translated | Space-occupying lesions identification in medical imaging of the kidney: A review | eng |
| dc.type.driver | info:eu-repo/semantics/article | eng |
| dc.type.spa | Artículo científico | spa |
| dcterms.references | Quiroga W, Fernández F, Citarella D, Rangel J, Estrada A, Patiño I. Guía de manejo del carcinoma de células renales. Revista Urología Colombiana [Internet]. 2016; 35(2):169-189. Recuperado de: https:// www.redalyc.org/articulo.oa?id=149146287016. | spa |
| dcterms.references | Sanz E, García R, Rodríguez R, Arias F, Lennie A, Mayayo T. Estudio ultrasonográfico de las masas renales de pequeño tamaño. Arch. Esp. Urol., 59(4), 333-342, 2006. | spa |
| dcterms.references | Fernández J, Zuluaga A, Valle Diaz F. Caracterización por la imagen de las masas renales. Atlas por la imagen. Actas Urol Esp 33(5): 482-498, 2009. | spa |
| dcterms.references | Tonolini M, Lerardi A, Carrafiello G. Letter to the editor: spontaneous renal haemorrhage in end-stage renal disease. Insights Imaging 6: 693–695 (2015). https://doi.org/10.1007/s13244-015-0439-4. | eng |
| dcterms.references | Ameri C, Contreras P, Villasante N, Ríos H, Richards N, Mazza O. Masa ocupante renal sólida hasta 4 cm: análisis de 78 casos. Rev Arg de Urol. 7(1): 28-39, 2006. | spa |
| dcterms.references | Global Cancer Observatory: International Agency for Research on Cancer [Online]. Available:https://gco.iarc.fr/today/onlineanal ysistable?v=2018&mode=cancer&mode_population=continents &population=900&populations=900&key=asr&sex=0&cancer=39 &type=0&statistic=5&prevalence=0&population_group=0&ages_ g r o u p % 5 B % 5 D = 0 & a g e s _ g r o u p % 5 B % 5 D = 1 7 & g r o u p _ cancer=1&include_nmsc=1&include_nmsc_other=1 | eng |
| dcterms.references | Sociedad Española de Oncología Médica: Las cifras del cáncer en España 2020 https://seom.org/seomcms/images/stories/recursos/ Cifras_del_cancer_2020.pdf | spa |
| dcterms.references | Boletín Epidemiológico Semanal. Semana epidemiológica 08 (17 al 23 de febrero de 2019). Instituto Nacional de Salud. Dirección de vigilancia y análisis del Riesgo en Salud Pública. Publicación en línea: ISSN 2357-6189. 2019 | spa |
| dcterms.references | Schmidt L, Linehan W. Predisposición genética al cáncer de riñón. Seminarios en oncología, 43(5): 566–574. 2016 | spa |
| dcterms.references | Millet I, Doyon F, Hoa D, Thuret R, Merigeaud S, Serre I, Taourel P. Characterization of small solid renal lesions: can benign and malignant tumors be differentiated with ct? American journal of roentgenology 197, 887–896. 2011. | eng |
| dcterms.references | Huérfano Y, Vera M, Del Mar A, et al., Imagenología médica: Fundamentos y alcances AVFT 35(3): 71-76. 201 | spa |
| dcterms.references | Agnello, F., Albano, D., Micci, G. et al. CT and MR imaging of cystic renal lesions. Insights Imaging 11(5) 2020. https://doi.org/10.1186/ s13244-019-0826-3 | spa |
| dcterms.references | Gimpel Ch, Fred E, Breysem L, et al. Imaging of Kidney Cysts and Cystic Kidney Diseases in Children: An International Working Group Consensus Statement. 2019. https://doi.org/10.1148/radiol.2018181243 | eng |
| dcterms.references | Hélénon O, Crosnier A, Verkarre V, Merran S, Méjean A, Correas J. Simple and complex renal cysts in adults: Classification system for renal cystic masses. Diagnostic and Interventional Imaging 99(1):189-218. 2018 | spa |
| dcterms.references | Bartlett S, Solomon C, Tassell B, et al. Complex renal cysts associated with crizotinib treatment Cancer Medicine 4(6):887–896. 2015. | eng |
| dcterms.references | Eknoyan G A. Clinical view of simple and complex renal cysts. JASN 20 (9)1874-1876 2009 | eng |
| dcterms.references | Israel G, Bosniak M. An update of the Bosniak renal cyst classification system 66(3)484-488. 2005 | eng |
| dcterms.references | Graumann O, Osther S, Karstoft J, Horlyck A, Sloth P. Evaluation of Bosniak category IIF complex renal cysts Insights Imaging 4(1):471- 480. 2013. | eng |
| dcterms.references | Ahmad M, Sabr M, Roshy E. Assessment of apparent diffusion coefficient value as prognostic factor for renal cell carcinoma aggressiveness Egyptian Journal of Radiology and Nuclear Medicine 50(32). 2019. https://doi.org/10.1186/s43055-019-0038-3 | eng |
| dcterms.references | Ishigami K, Jones A, Dahmoush L, Leite L, Pakalniskis M, Barloon T. Imaging spectrum of renal oncocytomas: a pictorial review with pathologic correlation Insights Imaging 6(1):53-64. 2015. | eng |
| dcterms.references | Walsh B, Sutijono D, Moore C. Emergency ultrasound diagnosis of traumatic renal cyst rupture Critical Ultrasound Journal 1:127–128. 2010. | eng |
| dcterms.references | Peters et al. Incidence of synchronous and metachronous adrenal metastases following tumor nephrectomy in renal cell cancer patients: a retrospective bi-center analysis. Springer Plus 2:293-297. 2013. | eng |
| dcterms.references | Rhee H, Blazak J, Tham CM, Ng KL, Shepherd B, Lawson M, Preston J, Vela I, Thomas P, Wood S. Pilot study: use of gallium-68 PSMA PET for detection of metastatic lesions in patients with renal tumour. EJNMMI Res. 2016 Dec;6(1):76. doi: 10.1186/s13550-016-0231-6. Epub 2016 Oct 22. PMID: 27771904; PMCID: PMC5075321. | eng |
| dcterms.references | Mauri G, Nicosia L, Varano G, Bonomo G, Della Vigna P, Monfardini L, Orsi F. Tips and tricks for a safe and effective image-guided percutaneous renal tumour ablation. Insights Imaging 8:357–363 2017. | eng |
| dcterms.references | Jin C, Shi F, Xiang D, Jiang X, Zhang B, Wang X, Zhu W, Gao E, Chen X, 3D Fast Automatic Segmentation of Kidney Based on Modified AAM and Random Forest IEEE Transactions on Medical Imaging 35(6), 195-1407. 2016. | eng |
| dcterms.references | Shehata M, Mahmoud A, Soliman A, Khalifa F, Ghazal M, Abou ElGhar M, et al. (2018) 3D kidney segmentation from abdominal diffusion MRI using an appearance-guided deformable boundary. PLoS ONE 13(7). https://doi.org/10.1371/journal.pone.0200082. | spa |
| dcterms.references | Myronenko A, Hatamizadeh A 3D Kidneys and Kidney Tumor Semantic Segmentation using Boundary-Aware Networks arXiv:1909.06684v1 [eess.IV] 14 Sep 2019 | eng |
| dcterms.references | Kim Y, Ge Y, Tao C, et al. Automated Segmentation of Kidneys from MR Images in Patients with Autosomal Dominant Polycystic Kidney Disease Clin J Am Soc Nephrol 11:576–584, 2016. doi: 10.2215/ CJN.08300815. | eng |
| dcterms.references | Yang G, Li G, Pan T, Automatic Segmentation of Kidney and Renal Tumor in CT Images Based on Pyramid Pooling and Gradually Enhanced Feature Modules 24th International Conference on Pattern Recognition, ICPR 2018, Aug 2018, Beijing, China. 3790-3795, DOI: 10.1109/ICPR.2018.8545143. | eng |
| dcterms.references | Kline T, Korfiatis P, Edwards M, et al. Automatic total kidney volume measurement on follow-up magnetic resonance images to facilitate monitoring of autosomal dominant polycystic kidney disease progression Nephrol Dial Transplant (2016) 31:241–248 doi: 10.1093/ ndt/gfv314 | spa |
| dcterms.references | Vu M, Grimbergen G, Simkó A, Nyholm T, Löfstedt T, End-to-End Cascaded U-Nets with a Localization Network for Kidney TumoSegmentation arXiv:1910.07521v1 [eess.IV] 16 Oct 2019 | eng |
| dcterms.references | Yin S, Zhang Z, Li H, Peng Q, You X, Furth S, Tasian G, Fan Y Fully-automatic Segmentation of Kidneys in Clinical Ultrasound Images using a Boundary Distance Regression Network Proc IEEE Int Symp Biomed Imaging. 2019: 1741–1744. doi:10.1109/ ISBI.2019.8759170. | eng |
| dcterms.references | Santini G, Moreau N, Rubeaux M Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge. arXiv:1909.00735v1 [eess.IV] 2 Sep 2019. | eng |
| dcterms.references | Nithya A, Appathurai A, Venkatadri N, Ramji D, Palagan C Kidney disease detection and segmentation using artificial neural network and multi-kernel k-means clustering for ultrasound images Measurement 149 (2020) 106952 https://doi.org/10.1016. | eng |
| dcterms.references | Lituiev D, Cha S, Chin An Automated Localization and Segmentation of Mononuclear Cell Aggregates in Kidney Histological Images Using Deep Learning 2019 medRxiv preprint doi: https://doi. org/10.1101/19002634. | eng |
| dcterms.references | Rana S, Jain S, Virmani J. SVM-Based Characterization of Focal Kidney Lesions from B-Mode Ultrasound Images Research Journal of Pharmaceutical, Biological and Chemical Sciences 7(4) 837-846. 2016 | eng |
| dcterms.references | Heller N, Isensee F, Maeir-Hein K, et al. The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 Challenge arXiv:1912.01054v2 [eess.IV]. 2019. | eng |
| dcterms.references | Heller N, Sathianathen N, Arveen Kalapara et al. The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes arXiv:1904.00445v1 [q-bio.QM] 31 Mar 2019. | eng |
| dcterms.references | Raudales-Díaz I. Imágenes diagnósticas: Conceptos y generalidades. Rev Fac Cienc Med. 2014;11(1):35–43. Available from: http://www.bvs.hn/RFCM/ pdf/2014/pdf/RFCMVol11-1-2014-6.pdf | spa |
| oaire.version | info:eu-repo/semantics/publishedVersion | eng |
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