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		<title>Deep Radiomics Boost Chemotherapy Prediction in Breast Cancer</title>
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		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Mon, 11 Aug 2025 18:37:40 +0000</pubDate>
				<category><![CDATA[Cancer]]></category>
		<category><![CDATA[chemotherapy response prediction]]></category>
		<category><![CDATA[deep learning in medical imaging]]></category>
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		<category><![CDATA[Precision Medicine in Oncology]]></category>
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					<description><![CDATA[In an era where precision medicine is rapidly transforming cancer treatment paradigms, innovative approaches that harness the power of advanced imaging and artificial intelligence are at the forefront of oncological research. A recent breakthrough study spearheaded by Jiang, Low, Huang, and their team has demonstrated the potential of 18F-FDG PET/CT-based deep radiomic models to significantly [&#8230;]]]></description>
		
		
		
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		<title>New Organ Chip Platform for Precision Oncology Predicts Chemotherapy Responses in Esophageal Adenocarcinoma Patients</title>
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		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Fri, 27 Jun 2025 16:03:34 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[chemotherapy response prediction]]></category>
		<category><![CDATA[esophageal adenocarcinoma treatment]]></category>
		<category><![CDATA[organ chip technology]]></category>
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					<description><![CDATA[Esophageal adenocarcinoma (EAC) represents one of the most formidable challenges in modern oncology, recognized as the sixth leading cause of cancer-related mortality globally. With the absence of effective targeted therapies for this malignancy, patients often depend on neoadjuvant chemotherapy (NACT) as a standard treatment even prior to surgical interventions, aiming to reduce tumor burden. However, [&#8230;]]]></description>
		
		
		
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