{"id":31188,"date":"2022-08-23T09:22:06","date_gmt":"2022-08-23T14:22:06","guid":{"rendered":"https:\/\/saluddigital.com\/?p=31188"},"modified":"2025-10-20T11:16:14","modified_gmt":"2025-10-20T17:16:14","slug":"ia-y-tomografias-computarizadas-ayudarian-en-el-diagnostico-temprano-de-cancer-de-pancreas","status":"publish","type":"post","link":"https:\/\/saluddigital.com\/en\/big-data\/ia-y-tomografias-computarizadas-ayudarian-en-el-diagnostico-temprano-de-cancer-de-pancreas\/","title":{"rendered":"AI and CT scans could help in early diagnosis of pancreatic cancer"},"content":{"rendered":"
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A recent study published in Gastroenterology showed that radiomics-based machine learning models could detect pancreatic cancer early in CT scans.<\/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 purpose of the study was the detection of pancreatic ductal adenocarcinoma in the stage prior to diagnosis, that is, from 3 to 36 months prior to clinical diagnosis. To do this, the authors relied on machine learning models based on radiomics to compare the performance of radiologists in case studies.<\/p>

Mayo Clinic radiologist Ajit Goenka, lead author of the study, acknowledged that pancreatic cancer is a deadly disease, and one of the leading causes of cancer-related deaths. \u201cUp to 40% in small pancreatic cancers may not show up on standard images. As a result, most patients have an advanced and non-curable disease\u201d, detailed the specialist.<\/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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To do this, Dr. Goenka and his team of researchers incorporated Artificial Intelligence (AI), to carry out radiological examinations capable of detecting pancreatic cancer at an earlier and more curable stage. The researcher explains that AI can detect normal-appearing pancreatic cancer on CT scans taken before the first signs of cancer.<\/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 study extracted images of early cancer from scans taken between three months and three years before the onset of cancer, performed for indications unrelated to this disease.<\/p>

They then used a control group of age-matched patients who did not develop cancer during the three years of follow-up. In this sense, they computationally quantified the metrics of the pancreatic tissue. Based on these data, the researchers built machine learning models to predict future risk of pancreatic cancer at a mean time of 386 days in the range of 97 to 1092 days before clinical diagnosis with accuracies between 94 and 98%.<\/p>

"By comparison, radiologists were unable to reliably differentiate between patients who developed cancer and those with a normal pancreas," explained Sovan Mukherjee, a data science analyst on Dr. Goenka's team and another of the study's authors. .<\/p>

The model was tested with variations in image noise, different scanner models, image acquisition protocols, and processing parameters, which did not affect the predictions in any way.<\/p>

\u201cOur study demonstrates that artificial intelligence can identify those asymptomatic people who may harbor hidden cancer at a stage where surgical cure may be possible. These findings may help overcome one of the key barriers to improving survival for pancreatic cancer patients,\u201d Dr. Goenka concluded.<\/p>

Check out the full study in the link below:\u00a0<\/p>

https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S0016508522007284?via%3Dihub<\/a><\/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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\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\t BIBLIOGRAPHY<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t

SCIENCE DIRECT<\/strong><\/p>

https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S0016508522007284?via%3Dihub<\/a><\/p>

MAYO CLINIC<\/strong><\/p>

https:\/\/newsnetwork.mayoclinic.org\/discussion\/ai-applied-to-prediagnostic-cts-may-help-diagnose-pancreatic-cancer-at-earlier-more-treatable-stage\/<\/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 estudio reciente publicado en Gastroenterology mostr\u00f3 que modelos de aprendizaje autom\u00e1tico basados en radi\u00f3mica podr\u00edan detectar tempranamente c\u00e1ncer de p\u00e1ncreas en escaneos de tomograf\u00eda computarizada. El prop\u00f3sito del estudio fue la detecci\u00f3n de adenocarcinoma ductal pancre\u00e1tico en la etapa previa al diagn\u00f3stico, es decir de 3 a 36 meses previos al diagn\u00f3stico cl\u00ednico. Para ello los autores se apoyaron en modelos de aprendizaje autom\u00e1tico basados en radi\u00f3mica para comprar el rendimiento de los radi\u00f3logos en estudios de caso. El radi\u00f3logo Ajit Goenka de Mayo Clinic, autor principal del estudio reconoci\u00f3 que el c\u00e1ncer de p\u00e1ncreas es una enfermedad mortal, y una de las principales causas de muertes relacionadas con c\u00e1ncer. \u201cEs posible que hasta el 40% de los c\u00e1nceres de p\u00e1ncreas peque\u00f1os no aparezcan en las im\u00e1genes est\u00e1ndar. Como resultado, la mayor\u00eda de los pacientes presentan una enfermedad avanzada y no curable\u201d, detall\u00f3 el especialista. Para ello el Dr. Goenka y su equipo de investigadores incorporaron Inteligencia Artificial (IA), para realizar ex\u00e1menes radiol\u00f3gicos capaces de detectar c\u00e1ncer de p\u00e1ncreas en un estado m\u00e1s temprano y curable. El investigador explica que la IA puede detectar el c\u00e1ncer en p\u00e1ncreas de apariencia normal en tomograf\u00edas computarizadas tomadas antes los primeros s\u00edntomas de c\u00e1ncer. El estudio extrajo im\u00e1genes de c\u00e1ncer temprano de tomograf\u00edas realizadas entre tres meses y tres a\u00f1os antes de la aparici\u00f3n del hacer, realizadas por indicaciones no relacionadas a esta enfermedad. Posteriormente utilizaron un grupo de control de pacientes de la misma edad que no desarrollaron c\u00e1ncer durante los tres a\u00f1os de seguimiento. En este sentido cuantificaron computacionalmente las m\u00e9tricas del tejido pancre\u00e1tico.    Con base en estos datos los investigadores construyeron modelos de aprendizaje autom\u00e1tico para predecir el riesgo futuro de c\u00e1ncer de p\u00e1ncreas en un tiempo medio de 386 d\u00edas en rango de 97 a 1092 d\u00edas antes del diagn\u00f3stico cl\u00ednico con precisiones de entre 94 a 98%. \u201cEn comparaci\u00f3n, los radi\u00f3logos no pudieron diferenciar de manera confiable entre los pacientes que desarrollaron c\u00e1ncer y los que ten\u00edan un p\u00e1ncreas normal\u201d, explic\u00f3 Sovan Mukherjee, analista de ciencia de datos en el equipo del Dr. Goenka y otro de los autores del estudio. El modelo fue probado con variaciones en el ruido de la imagen, diferentes modelos de esc\u00e1ner, protocolos de adquisici\u00f3n de im\u00e1genes y par\u00e1metros de procesamiento, que no afectaron de ninguna manera las predicciones. \u201cNuestro estudio demuestra que la inteligencia artificial puede identificar a aquellas personas asintom\u00e1ticas que pueden albergar un c\u00e1ncer oculto en una etapa en la que la cura quir\u00fargica puede ser posible. Estos hallazgos pueden ayudar a superar una de las barreras clave para mejorar la supervivencia de los pacientes con c\u00e1ncer de p\u00e1ncreas\u201d, concluy\u00f3 el Dr. Goenka. Consulta el estudio completo en el encalce a continuaci\u00f3n:  https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S0016508522007284?via%3Dihub BIBLIOGRAF\u00cdA SCIENCE DIRECT https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S0016508522007284?via%3Dihub MAYO CLINIC https:\/\/newsnetwork.mayoclinic.org\/discussion\/ai-applied-to-prediagnostic-cts-may-help-diagnose-pancreatic-cancer-at-earlier-more-treatable-stage\/<\/p>","protected":false},"author":1,"featured_media":31190,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[156,3400,160],"tags":[145],"class_list":["post-31188","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-big-data","category-diagnostico","category-noticias","tag-noticias"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/31188","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=31188"}],"version-history":[{"count":0,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/31188\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media\/31190"}],"wp:attachment":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media?parent=31188"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/categories?post=31188"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/tags?post=31188"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}