{"id":1225,"date":"2026-07-20T16:54:38","date_gmt":"2026-07-20T14:54:38","guid":{"rendered":"https:\/\/infoimasd.com\/?p=1225"},"modified":"2026-07-20T16:54:39","modified_gmt":"2026-07-20T14:54:39","slug":"mamorisk-prediccion-riesgo-cancer-mama-inteligencia-artificial","status":"publish","type":"post","link":"https:\/\/infoimasd.com\/index.php\/2026\/07\/20\/mamorisk-prediccion-riesgo-cancer-mama-inteligencia-artificial\/","title":{"rendered":"MamoRisk estudiar\u00e1 a 10.000 mujeres para predecir el riesgo individual de c\u00e1ncer de mama con gen\u00e9tica e inteligencia artificial"},"content":{"rendered":"\n

Espa\u00f1a da un nuevo paso hacia una medicina m\u00e1s preventiva y personalizada. El Ministerio de Ciencia, Innovaci\u00f3n y Universidades (MICIU) ha impulsado MamoRisk, el primer estudio espa\u00f1ol que validar\u00e1 un modelo de predicci\u00f3n personalizada del riesgo de desarrollar c\u00e1ncer de mama<\/strong> combinando informaci\u00f3n gen\u00e9tica, factores cl\u00ednicos y epidemiol\u00f3gicos, mamograf\u00edas e inteligencia artificial. El proyecto, financiado con 1,2 millones de euros procedentes del PERTE para la Salud de Vanguardia, gestionados por el Instituto de Salud Carlos III (ISCIII)<\/a>, pretende demostrar que es posible adaptar los programas de cribado a las caracter\u00edsticas de cada mujer<\/strong>, mejorando la detecci\u00f3n precoz y orientando medidas preventivas espec\u00edficas.<\/p>\n\n\n\n

Un estudio con 10.000 mujeres de 14 comunidades aut\u00f3nomas<\/h3>\n\n\n\n

MamoRisk analizar\u00e1 los datos de 10.000 mujeres de las que 6.000 ser\u00e1n participantes sanas y 4.000 habr\u00e1n sido diagnosticadas de c\u00e1ncer de mama. <\/strong>La investigaci\u00f3n comenzar\u00e1 en Galicia, donde ya se ha iniciado el env\u00edo de invitaciones a las primeras 400 mujeres, y posteriormente se ampliar\u00e1 a otras trece comunidades aut\u00f3nomas: Madrid, Cantabria, Castilla-La Mancha, Murcia, Extremadura, Asturias, Castilla y Le\u00f3n, Andaluc\u00eda, Arag\u00f3n, Catalu\u00f1a, La Rioja, Comunidad Valenciana y Canarias.<\/p>\n\n\n\n

El proyecto est\u00e1 liderado por el Instituto de Investigaci\u00f3n Sanitaria de Santiago de Compostela (IDIS), el Instituto de Investigaci\u00f3n Sanitaria Galicia Sur (IISGS) y el Centro Nacional de Investigaciones Oncol\u00f3gicas (CNIO)<\/a>, fundaci\u00f3n adscrita al ISCIII. La coordinaci\u00f3n cient\u00edfica recae en la doctora Manuela Gago<\/strong> del IDIS, responsable del dise\u00f1o del estudio, el reclutamiento de las participantes y la recogida de muestras biol\u00f3gicas y datos cl\u00ednicos. En el CNIO, la doctora Anna Gonz\u00e1lez-Neira<\/strong> y Guillermo Pita<\/strong> dirigir\u00e1n el an\u00e1lisis gen\u00e9tico mediante t\u00e9cnicas de secuenciaci\u00f3n masiva, mientras que el doctor Jos\u00e9 Esteban Castelao<\/strong>, del IISGS, coordinar\u00e1 el desarrollo de los algoritmos de inteligencia artificial aplicados a miles de mamograf\u00edas.<\/p>\n\n\n\n

Un modelo que combina cuatro tipos de informaci\u00f3n<\/h3>\n\n\n\n

El objetivo de MamoRisk es validar un modelo estad\u00edstico capaz de estimar el riesgo individual de desarrollar c\u00e1ncer de mama<\/strong> integrando diferentes fuentes de informaci\u00f3n. Para ello se tendr\u00e1n en cuenta:<\/p>\n\n\n\n

    \n
  • Factores cl\u00ednicos y epidemiol\u00f3gicos, como la edad, antecedentes familiares o variables reproductivas;<\/li>\n\n\n\n
  • Factores relacionados con el estilo de vida;<\/li>\n\n\n\n
  • Informaci\u00f3n gen\u00e9tica obtenida mediante una muestra de saliva;<\/li>\n\n\n\n
  • Caracter\u00edsticas observadas en las mamograf\u00edas, analizadas tanto por radi\u00f3logos como por algoritmos de inteligencia artificial.<\/li>\n<\/ul>\n\n\n\n

    Posteriormente, la predicci\u00f3n obtenida por el modelo se comparar\u00e1 con la evoluci\u00f3n real de las participantes para comprobar su capacidad de identificar correctamente a las mujeres con mayor riesgo.<\/p>\n\n\n\n

    La gen\u00e9tica, una pieza clave<\/h3>\n\n\n\n

    En los \u00faltimos a\u00f1os se han identificado centenares de variantes gen\u00e9ticas<\/strong> relacionadas con el riesgo de c\u00e1ncer de mama. Las mutaciones en genes como BRCA1 y BRCA2 son las m\u00e1s conocidas por conferir un riesgo elevado<\/strong>, pero existen muchas otras variantes de menor impacto que, combinadas entre s\u00ed, tambi\u00e9n contribuyen a modificar la probabilidad de desarrollar la enfermedad. Precisamente uno de los objetivos de MamoRisk es integrar toda esa informaci\u00f3n gen\u00e9tica con el resto de factores de riesgo para obtener una estimaci\u00f3n mucho m\u00e1s precisa que la que ofrecen los modelos tradicionales.<\/p>\n\n\n\n

    Hacia un cribado adaptado a cada mujer<\/h3>\n\n\n\n

    Actualmente, los programas p\u00fablicos de detecci\u00f3n precoz del c\u00e1ncer de mama se basan fundamentalmente en la edad de las mujeres. Sin embargo, el riesgo no es igual para toda la poblaci\u00f3n. Si el modelo desarrollado por MamoRisk demuestra su utilidad cl\u00ednica, podr\u00eda permitir que las mujeres con mayor probabilidad de desarrollar la enfermedad recibieran un seguimiento m\u00e1s intensivo<\/strong> con pruebas adaptadas a su perfil de riesgo. Aquellas con menor riesgo podr\u00edan mantener controles menos frecuentes sin comprometer la seguridad. Este enfoque supone uno de los principales objetivos de la Medicina Personalizada de Precisi\u00f3n, una estrategia que busca adaptar tanto la prevenci\u00f3n como el diagn\u00f3stico y los tratamientos a las caracter\u00edsticas biol\u00f3gicas de cada paciente.<\/p>\n\n\n\n

    Inteligencia artificial para mejorar la prevenci\u00f3n<\/h3>\n\n\n\n

    Adem\u00e1s de la informaci\u00f3n gen\u00e9tica, el proyecto incorporar\u00e1 herramientas de inteligencia artificial capaces de analizar miles de mamograf\u00edas y detectar patrones<\/strong> que pueden pasar desapercibidos durante una lectura convencional. Todos los datos cl\u00ednicos, epidemiol\u00f3gicos, gen\u00e9ticos y mamogr\u00e1ficos se integrar\u00e1n finalmente en un \u00fanico modelo predictivo, acompa\u00f1ado adem\u00e1s de un an\u00e1lisis de coste-efectividad que permitir\u00e1 evaluar su posible incorporaci\u00f3n futura al Sistema Nacional de Salud.<\/p>\n\n\n\n

    <\/p>\n","protected":false},"excerpt":{"rendered":"

    Espa\u00f1a da un nuevo paso hacia una medicina m\u00e1s preventiva y personalizada. El Ministerio de Ciencia, Innovaci\u00f3n y Universidades (MICIU) ha impulsado MamoRisk, el primer estudio espa\u00f1ol que validar\u00e1 un…<\/p>\n","protected":false},"author":1,"featured_media":1226,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[11],"tags":[353,103,492,498,473,31,173,495,500,494,6,499,496],"class_list":["post-1225","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-investigacion","tag-cancer-de-mama","tag-cnio","tag-idis","tag-iis-galicia-sur","tag-instituto-de-salud-carlos-iii","tag-inteligencia-artificial","tag-investigacion-biomedica","tag-isciii","tag-mamorisk","tag-medicina-de-precision","tag-ministerio-de-ciencia","tag-oncologia","tag-perte-salud-de-vanguardia"],"yoast_head":"\nMamoRisk 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