<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"
	xmlns:content="http://purl.org/rss/1.0/modules/content/"
	xmlns:wfw="http://wellformedweb.org/CommentAPI/"
	xmlns:dc="http://purl.org/dc/elements/1.1/"
	xmlns:atom="http://www.w3.org/2005/Atom"
	xmlns:sy="http://purl.org/rss/1.0/modules/syndication/"
	xmlns:slash="http://purl.org/rss/1.0/modules/slash/"
	>

<channel>
	<title>machine learning in cancer &#8211; BIOENGINEER.ORG</title>
	<atom:link href="https://bioengineer.org/tag/machine-learning-in-cancer/feed/" rel="self" type="application/rss+xml" />
	<link>https://bioengineer.org</link>
	<description>Bioengineering</description>
	<lastBuildDate>Fri, 26 Dec 2025 17:37:12 +0000</lastBuildDate>
	<language>en-US</language>
	<sy:updatePeriod>
	hourly	</sy:updatePeriod>
	<sy:updateFrequency>
	1	</sy:updateFrequency>
	<generator>https://wordpress.org/?v=7.0</generator>

<image>
	<url>https://bioengineer.org/wp-content/uploads/2019/09/cropped-bioengineering-32x32.png</url>
	<title>machine learning in cancer &#8211; BIOENGINEER.ORG</title>
	<link>https://bioengineer.org</link>
	<width>32</width>
	<height>32</height>
</image> 
<site xmlns="com-wordpress:feed-additions:1">72741379</site>	<item>
		<title>Evaluating Prediction Models for Leukemia Types</title>
		<link>https://bioengineer.org/evaluating-prediction-models-for-leukemia-types/</link>
		
		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Fri, 26 Dec 2025 17:36:45 +0000</pubDate>
				<category><![CDATA[Cancer]]></category>
		<category><![CDATA[Hematological malignancies]]></category>
		<category><![CDATA[Leukemia prediction models]]></category>
		<category><![CDATA[machine learning in cancer]]></category>
		<category><![CDATA[Predictive oncology]]></category>
		<guid isPermaLink="false">https://bioengineer.org/evaluating-prediction-models-for-leukemia-types/</guid>

					<description><![CDATA[In a significant development in the field of oncology, researchers A. Tuerxun, Y. Yang, and X. Cai, along with their colleagues, have made notable strides in the predictive modeling of different types of leukemia. Their systematic review and critical appraisal, published in the Journal of Cancer Research and Clinical Oncology, sheds light on the intricate [&#8230;]]]></description>
		
		
		
		<post-id xmlns="com-wordpress:feed-additions:1">311529</post-id>	</item>
		<item>
		<title>Radiomics Predicts Lenvatinib Success in Liver Cancer</title>
		<link>https://bioengineer.org/radiomics-predicts-lenvatinib-success-in-liver-cancer/</link>
		
		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Thu, 11 Sep 2025 01:51:11 +0000</pubDate>
				<category><![CDATA[Cancer]]></category>
		<category><![CDATA[Hepatocellular Carcinoma]]></category>
		<category><![CDATA[hepatocellular carcinoma treatment]]></category>
		<category><![CDATA[lenvatinib]]></category>
		<category><![CDATA[lenvatinib efficacy prediction]]></category>
		<category><![CDATA[machine learning in cancer]]></category>
		<category><![CDATA[MRI]]></category>
		<category><![CDATA[MRI-based radiomics]]></category>
		<category><![CDATA[personalized medicine]]></category>
		<category><![CDATA[personalized oncology]]></category>
		<category><![CDATA[radiomics]]></category>
		<guid isPermaLink="false">https://bioengineer.org/radiomics-predicts-lenvatinib-success-in-liver-cancer/</guid>

					<description><![CDATA[In the realm of oncology, the quest for precision medicine has led to intriguing advancements that hold promise for cancer patients worldwide. A recent study delves into a pivotal area of hepatocellular carcinoma (HCC) treatment, leveraging cutting-edge technology to predict the response to the targeted therapy drug, lenvatinib. This innovative approach utilizes MRI-based radiomics signatures, [&#8230;]]]></description>
		
		
		
		<post-id xmlns="com-wordpress:feed-additions:1">268594</post-id>	</item>
	</channel>
</rss>
