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		<title>AI Model Predicts Which Breast Cancer Patients Can Skip Unnecessary Surgery</title>
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				<category><![CDATA[Cancer]]></category>
		<category><![CDATA[AUROC]]></category>
		<category><![CDATA[Breast Cancer]]></category>
		<category><![CDATA[LASSO regularization]]></category>
		<category><![CDATA[logistic regression]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[mammography]]></category>
		<category><![CDATA[MRI]]></category>
		<category><![CDATA[neoadjuvant chemotherapy]]></category>
		<category><![CDATA[Pathological Complete Response]]></category>
		<category><![CDATA[predictive medicine]]></category>
		<category><![CDATA[Surgery]]></category>
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					<description><![CDATA[Researchers at Heidelberg University Hospital developed a machine learning model combining ultrasound, mammography, MRI and clinical data to predict pathological complete response after neoadjuvant chemotherapy in breast cancer, achieving an AUROC of 0.76.]]></description>
		
		
		
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