{"id":9402,"date":"2020-09-01T09:59:40","date_gmt":"2020-09-01T14:59:40","guid":{"rendered":"https:\/\/saluddigital.com\/?p=9402"},"modified":"2025-10-21T14:32:45","modified_gmt":"2025-10-21T20:32:45","slug":"metodo-de-machine-learning-permite-diferenciar-masas-renales-en-las-tomografias-computarizadas","status":"publish","type":"post","link":"https:\/\/saluddigital.com\/en\/avance-de-la-ciencia\/metodo-de-machine-learning-permite-diferenciar-masas-renales-en-las-tomografias-computarizadas\/","title":{"rendered":"Machine learning method allows to differentiate kidney masses in CT scans"},"content":{"rendered":"
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A machine learning method with a convolutional neural network (CNN) could work for the evaluation of small solid kidney masses in computed tomography (CT).<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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The article published in the American Journal of Roentgenology<\/em> showed progress on this new diagnostic method. Researchers at Okayama University in Japan studied 1807 imaging sets of 168 solid renal masses less than or equal to 4 cm, diagnosed with CT in 159 patients.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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The masses were classified as malignant (136), and benign (32), on a 5-point scale. Takashi Tanaka, lead author of the research explained the classification system: \" \"four were used for augmentation and supervised training (48,832 images), and one was used for testing (281 images).\"<\/p>

The CNN Model did not find a significant size difference between benign and malignant masses, however, the AUC value of the spinal corticomedullary was found to be greater than that of other phases. In addition, 88% accuracy was obtained in the images of that phase.<\/p>

The results showed that the corticomedullary phase was an important predictor for determining whether the mass was malignant or not, compared to variables using other models such as the patient's age or mass size.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/i><\/span>\n\t\t\t\t\t\t\t\t<\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\tBIBLIOGRAPHY<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t

SCIENCE DAILY<\/strong><\/p>

https:\/\/www.sciencedaily.com\/releases\/2020\/01\/200110135444.htm<\/a><\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"

Un m\u00e9todo de aprendizaje autom\u00e1tico con una red neuronal convolucional (CNN), podr\u00eda funcionar para la evaluaci\u00f3n de peque\u00f1as masas renales solidas en tomograf\u00edas computarizadas (TC). En art\u00edculo publicado en el American Journal of Roentgenology se mostraron los avances sobre este nuevo m\u00e9todo de diagn\u00f3stico. Investigadores de la Universidad de Okayama en Jap\u00f3n, estudiaron 1807 conjuntos de im\u00e1genes de 168 masas renales s\u00f3lidas menores o iguales a 4 cm, diagnosticadas con TC en 159 pacientes. Las masas fueron clasificadas como malignas (136), y benignas (32), mediante una escala de 5 puntos. Takashi Tanaka, autor principal de la investigaci\u00f3n explic\u00f3 el sistema de clasificaci\u00f3n:  “cuatro se utilizaron para el aumento y el entrenamiento supervisado (48.832 im\u00e1genes), y una se utiliz\u00f3 para las pruebas (281 im\u00e1genes)”. El Modelo CNN no encontr\u00f3 una diferencia de tama\u00f1o significativa entre las masas benignas y malignas, sin embargo, s\u00ed se encontr\u00f3 que el valor de AUC de la fase cortico medular era mayor que el de otras fases. Adem\u00e1s, se obtuvo una precisi\u00f3n de 88% en las im\u00e1genes de esa fase. Los resultaron mostraron que la fase cortico medular fue un predictor importante para determinar si la masa era maligna o no, en comparaci\u00f3n con variables que utilizan otros modelos como la edad del paciente o el tama\u00f1o de la masa. BIBLIOGRAF\u00cdA SCIENCE DAILY https:\/\/www.sciencedaily.com\/releases\/2020\/01\/200110135444.htm<\/p>","protected":false},"author":1,"featured_media":9403,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[152,156],"tags":[145],"class_list":["post-9402","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-avance-de-la-ciencia","category-big-data","tag-noticias"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/9402","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/comments?post=9402"}],"version-history":[{"count":0,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/9402\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media\/9403"}],"wp:attachment":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media?parent=9402"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/categories?post=9402"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/tags?post=9402"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}