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	<title>Medical Research | Tissuepathology.com</title>
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	<title>Medical Research | Tissuepathology.com</title>
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		<title>American Center for Cancer Research: DNA methylation &#8220;fingerprints&#8221; identify primary sites in metastatic cancers</title>
		<link>https://tissuepathology.com/2026/04/21/american-center-for-cancer-research-dna-methylation-fingerprints-identify-primary-sites-in-metastatic-cancers/</link>
		
		<dc:creator><![CDATA[Erica Goodpaster]]></dc:creator>
		<pubDate>Tue, 21 Apr 2026 13:47:18 +0000</pubDate>
				<category><![CDATA[Artificial Intelligence]]></category>
		<category><![CDATA[Digital Pathology News]]></category>
		<category><![CDATA[Medical Research]]></category>
		<category><![CDATA[Pathology News]]></category>
		<category><![CDATA[Reports]]></category>
		<category><![CDATA[AI]]></category>
		<category><![CDATA[American Center for Cancer Research]]></category>
		<category><![CDATA[cancer]]></category>
		<category><![CDATA[digital pathology]]></category>
		<category><![CDATA[DNA]]></category>
		<category><![CDATA[image analysis]]></category>
		<category><![CDATA[metastatic cancers]]></category>
		<category><![CDATA[methylation]]></category>
		<category><![CDATA[Pathology]]></category>
		<guid isPermaLink="false">https://tissuepathology.com/?p=24363</guid>

					<description><![CDATA[<p>A machine learning model analyzing CpG-based DNA methylation accurately predicted the origin of many different cancer types in patients with cancers of unknown primary (CUP), according to research presented at the American Association for Cancer Research (AACR) Annual Meeting 2026, held April 17-22. CUP are metastatic malignancies in which the primary cancer site could not [&#8230;]</p>
The post <a href="https://tissuepathology.com/2026/04/21/american-center-for-cancer-research-dna-methylation-fingerprints-identify-primary-sites-in-metastatic-cancers/">American Center for Cancer Research: DNA methylation “fingerprints” identify primary sites in metastatic cancers</a> first appeared on <a href="https://tissuepathology.com">Tissuepathology.com</a>.]]></description>
		
		
		
			</item>
		<item>
		<title>Cancer Survival Rises</title>
		<link>https://tissuepathology.com/2026/01/14/cancer-survival-rises/</link>
		
		<dc:creator><![CDATA[Dr. Keith J. Kaplan]]></dc:creator>
		<pubDate>Wed, 14 Jan 2026 15:51:58 +0000</pubDate>
				<category><![CDATA[Anatomic Pathology]]></category>
		<category><![CDATA[Current Affairs]]></category>
		<category><![CDATA[Education]]></category>
		<category><![CDATA[Medical Research]]></category>
		<category><![CDATA[Pathology News]]></category>
		<category><![CDATA[Patient Advocacy]]></category>
		<category><![CDATA[Science]]></category>
		<category><![CDATA[cancer]]></category>
		<category><![CDATA[Immunotherapy]]></category>
		<category><![CDATA[Survival Rates]]></category>
		<guid isPermaLink="false">https://tissuepathology.com/?p=24173</guid>

					<description><![CDATA[<p>From CNN: The five-year cancer survival rate in the US has reached 70% for the first time, according to an American Cancer Society report released yesterday. The figure, based on diagnoses from 2015 to 2021, is up from 49% in the mid-1970s and 63% in the mid-1990s. The ACS attributes the rise to wider and earlier screening, lower smoking [&#8230;]</p>
The post <a href="https://tissuepathology.com/2026/01/14/cancer-survival-rises/">Cancer Survival Rises</a> first appeared on <a href="https://tissuepathology.com">Tissuepathology.com</a>.]]></description>
		
		
		
			</item>
		<item>
		<title>Lunit study shows higher HER2 diagnostic agreement between AI and pathologists</title>
		<link>https://tissuepathology.com/2025/12/23/lunit-study-shows-higher-her2-diagnostic-agreement-between-ai-and-pathologists/</link>
		
		<dc:creator><![CDATA[Erica Goodpaster]]></dc:creator>
		<pubDate>Tue, 23 Dec 2025 13:28:43 +0000</pubDate>
				<category><![CDATA[Artificial Intelligence]]></category>
		<category><![CDATA[Digital Pathology News]]></category>
		<category><![CDATA[Image Analysis]]></category>
		<category><![CDATA[International]]></category>
		<category><![CDATA[Medical Research]]></category>
		<category><![CDATA[Pathology News]]></category>
		<category><![CDATA[Publications]]></category>
		<category><![CDATA[AI]]></category>
		<category><![CDATA[bile duct cancer]]></category>
		<category><![CDATA[Diagnostics]]></category>
		<category><![CDATA[digital pathology]]></category>
		<category><![CDATA[HER2]]></category>
		<category><![CDATA[image analysis]]></category>
		<category><![CDATA[Laboratory Investigation]]></category>
		<category><![CDATA[Lunit]]></category>
		<category><![CDATA[Pathology]]></category>
		<category><![CDATA[USCAP]]></category>
		<guid isPermaLink="false">https://tissuepathology.com/?p=24131</guid>

					<description><![CDATA[<p>Lunit said Tuesday that it has published research showing that its AI biomarker platform, Lunit SCOPE, can improve HER2 (human epidermal growth factor receptor 2) diagnosis in advanced bile duct cancer. The study was published in Laboratory Investigation, the official journal of the United States and Canadian Academy of Pathology (USCAP) (Impact Factor 4.2). Biliary tract [&#8230;]</p>
The post <a href="https://tissuepathology.com/2025/12/23/lunit-study-shows-higher-her2-diagnostic-agreement-between-ai-and-pathologists/">Lunit study shows higher HER2 diagnostic agreement between AI and pathologists</a> first appeared on <a href="https://tissuepathology.com">Tissuepathology.com</a>.]]></description>
		
		
		
			</item>
		<item>
		<title>UCL: AI tool helps predict treatment success in rectal cancer patients</title>
		<link>https://tissuepathology.com/2025/11/20/ucl-ai-tool-helps-predict-treatment-success-in-rectal-cancer-patients/</link>
		
		<dc:creator><![CDATA[Erica Goodpaster]]></dc:creator>
		<pubDate>Thu, 20 Nov 2025 13:18:14 +0000</pubDate>
				<category><![CDATA[Anatomic Pathology]]></category>
		<category><![CDATA[Artificial Intelligence]]></category>
		<category><![CDATA[Digital Pathology News]]></category>
		<category><![CDATA[Medical Research]]></category>
		<category><![CDATA[Pathology News]]></category>
		<category><![CDATA[Press Release]]></category>
		<category><![CDATA[AI]]></category>
		<category><![CDATA[digital pathology]]></category>
		<category><![CDATA[image analysis]]></category>
		<category><![CDATA[Pathology]]></category>
		<category><![CDATA[rectal cancer]]></category>
		<category><![CDATA[UCL]]></category>
		<category><![CDATA[University College of London]]></category>
		<guid isPermaLink="false">https://tissuepathology.com/?p=24059</guid>

					<description><![CDATA[<p>Artificial intelligence (AI) can predict how well patients with rectal cancer will respond to treatment by analysing standard tissue samples taken during diagnosis, finds a new study from researchers at UCL and UCLH. In most cancers, the immune landscape surrounding a tumour plays a major role in determining how cancer progresses and how patients respond [&#8230;]</p>
The post <a href="https://tissuepathology.com/2025/11/20/ucl-ai-tool-helps-predict-treatment-success-in-rectal-cancer-patients/">UCL: AI tool helps predict treatment success in rectal cancer patients</a> first appeared on <a href="https://tissuepathology.com">Tissuepathology.com</a>.]]></description>
		
		
		
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		<item>
		<title>Journal of Clinical Microbiology Publishes Article on ARUP Validation of AI for Parasite Detection</title>
		<link>https://tissuepathology.com/2025/11/03/journal-of-clinical-microbiology-publishes-article-on-arup-validation-of-ai-for-parasite-detection/</link>
		
		<dc:creator><![CDATA[Erica Goodpaster]]></dc:creator>
		<pubDate>Mon, 03 Nov 2025 16:10:34 +0000</pubDate>
				<category><![CDATA[Artificial Intelligence]]></category>
		<category><![CDATA[Digital Pathology News]]></category>
		<category><![CDATA[Image Analysis]]></category>
		<category><![CDATA[Medical Research]]></category>
		<category><![CDATA[Pathology News]]></category>
		<category><![CDATA[Press Release]]></category>
		<category><![CDATA[Publications]]></category>
		<category><![CDATA[AI]]></category>
		<category><![CDATA[ARUP Laboratories]]></category>
		<category><![CDATA[convolutional neural network]]></category>
		<category><![CDATA[digital pathology]]></category>
		<category><![CDATA[image analysis]]></category>
		<category><![CDATA[Journal of Clinical Microbiology]]></category>
		<category><![CDATA[Parasite Detection]]></category>
		<category><![CDATA[Pathology]]></category>
		<category><![CDATA[Techcyte]]></category>
		<guid isPermaLink="false">https://tissuepathology.com/?p=24020</guid>

					<description><![CDATA[<p>October 21, 2025 Salt Lake City—ARUP Laboratories today announced the publication of an article in the Journal of Clinical Microbiology describing the company’s validation of a deep convolutional neural network (CNN) to detect parasites in concentrated wet mounts of stool. For decades, detection of gastrointestinal parasites has relied on traditional microscopy, which is a labor-intensive process [&#8230;]</p>
The post <a href="https://tissuepathology.com/2025/11/03/journal-of-clinical-microbiology-publishes-article-on-arup-validation-of-ai-for-parasite-detection/">Journal of Clinical Microbiology Publishes Article on ARUP Validation of AI for Parasite Detection</a> first appeared on <a href="https://tissuepathology.com">Tissuepathology.com</a>.]]></description>
		
		
		
			</item>
		<item>
		<title>Ohio State researchers developing AI model to detect cancer early</title>
		<link>https://tissuepathology.com/2025/10/20/ohio-state-researchers-developing-ai-model-to-detect-cancer-early/</link>
		
		<dc:creator><![CDATA[Erica Goodpaster]]></dc:creator>
		<pubDate>Mon, 20 Oct 2025 11:35:10 +0000</pubDate>
				<category><![CDATA[Artificial Intelligence]]></category>
		<category><![CDATA[Clinical Pathology]]></category>
		<category><![CDATA[Digital Pathology News]]></category>
		<category><![CDATA[Image Analysis]]></category>
		<category><![CDATA[Medical Research]]></category>
		<category><![CDATA[Pathology News]]></category>
		<category><![CDATA[Science]]></category>
		<category><![CDATA[Whole slide]]></category>
		<category><![CDATA[AI]]></category>
		<category><![CDATA[breast cancer]]></category>
		<category><![CDATA[digital pathology]]></category>
		<category><![CDATA[image analysis]]></category>
		<category><![CDATA[invasive lobular carcinoma]]></category>
		<category><![CDATA[James Cancer Hospital]]></category>
		<category><![CDATA[lobular breast cancer]]></category>
		<category><![CDATA[OSUCCC-James]]></category>
		<category><![CDATA[Pathology]]></category>
		<category><![CDATA[The James]]></category>
		<category><![CDATA[The Ohio State University]]></category>
		<category><![CDATA[The Ohio State University Comprehensive Cancer Center – James Cancer Hospital and Solove Research Institute]]></category>
		<guid isPermaLink="false">https://tissuepathology.com/?p=23991</guid>

					<description><![CDATA[<p>The Ohio State University&#8217;s James Cancer Hospital makes advancements on lobular breast cancer screening While researchers work to improve lobular breast cancer screening, innovative treatments are improving outcomes for patients with the condition, which is diagnosed in more than 40,000 Americans each year. Lobular breast cancer — or invasive lobular carcinoma (ILC) — is a form [&#8230;]</p>
The post <a href="https://tissuepathology.com/2025/10/20/ohio-state-researchers-developing-ai-model-to-detect-cancer-early/">Ohio State researchers developing AI model to detect cancer early</a> first appeared on <a href="https://tissuepathology.com">Tissuepathology.com</a>.]]></description>
		
		
		
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