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                  <biblScope unit="volume">14</biblScope>
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                <term xml:lang="en">Immune</term>
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              <classCode scheme="halDomain" n="info.info-lg">Computer Science [cs]/Machine Learning [cs.LG]</classCode>
              <classCode scheme="halDomain" n="sdv.bbm.gtp">Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN]</classCode>
              <classCode scheme="halDomain" n="sdv.bibs">Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]</classCode>
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              <p>Primary liver cancer arises either from hepatocytic or biliary lineage cells, giving rise to hepatocellular carcinoma (HCC) or intrahepatic cholangiocarcinoma (ICCA). Combined hepatocellular- cholangiocarcinomas (cHCC-CCA) exhibit equivocal or mixed features of both, causing diagnostic uncertainty and difficulty in determining proper management. Here, we perform a comprehensive deep learning-based phenotyping of multiple cohorts of patients. We show that deep learning can reproduce the diagnosis of HCC vs. CCA with a high performance. We analyze a series of 405 cHCC-CCA patients and demonstrate that the model can reclassify the tumors as HCC or ICCA, and that the predictions are consistent with clinical outcomes, genetic alterations and in situ spatial gene expression profiling. This type of approach could improve treatment decisions and ultimately clinical outcome for patients with rare and biphenotypic cancers such as cHCC-CCA.</p>
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          <date type="start">2020-01-01</date>
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            <address>
              <addrLine>Adresse CS 40700 - 38058 Grenoble cedex</addrLine>
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            <ref type="url">http://www.univ-grenoble-alpes.fr</ref>
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            <address>
              <addrLine>46 avenue Félix Viallet 38031 Grenoble Cedex 1</addrLine>
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            <relation active="#struct-1042703" type="direct"/>
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        <org type="institution" xml:id="struct-300170" status="VALID">
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            <address>
              <addrLine>2 rue de la Milétrie – 86000 Poitiers – France</addrLine>
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            <ref type="url">http://www.chu-poitiers.fr/</ref>
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          <idno type="Wikidata">Q661056</idno>
          <orgName>Université de Poitiers =  University of Poitiers</orgName>
          <orgName type="acronym">UP</orgName>
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            <address>
              <addrLine>15, rue de l'Hôtel Dieu - 86034 Poitiers Cedex</addrLine>
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            <ref type="url">http://www.univ-poitiers.fr/</ref>
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        <org type="institution" xml:id="struct-343082" status="VALID">
          <orgName>Seoul National University Hospital</orgName>
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            <address>
              <country key="KR"/>
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        <org type="institution" xml:id="struct-545073" status="VALID">
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          <idno type="ROR">https://ror.org/05byvp690</idno>
          <orgName>University of Texas Southwestern Medical Center</orgName>
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            <address>
              <addrLine>5323 Harry Hines Blvd., Dallas, Texas, TX 75390</addrLine>
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          <orgName>Hôpital Paul Brousse</orgName>
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          <orgName type="acronym">YSM</orgName>
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        <org type="institution" xml:id="struct-355632" status="INCOMING">
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            <address>
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          <orgName>Université de Lausanne = University of Lausanne</orgName>
          <orgName type="acronym">UNIL</orgName>
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            <address>
              <addrLine>CH-1015 Lausanne</addrLine>
              <country key="CH"/>
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            <ref type="url">http://www.hec.unil.ch/</ref>
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          <orgName>Centre Hospitalier Universitaire Vaudois = Lausanne University Hospital [Lausanne]</orgName>
          <orgName type="acronym">CHUV</orgName>
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            <address>
              <addrLine>Rue du Bugnon 21CH-1011 Lausanne, Vaud</addrLine>
              <country key="CH"/>
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            <ref type="url">https://www.chuv.ch/fr/chuv-home</ref>
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            <address>
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        <org type="regrouplaboratory" xml:id="struct-98072" status="VALID">
          <orgName>University of Michigan Medical School [Ann Arbor]</orgName>
          <desc>
            <address>
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        <org type="institution" xml:id="struct-24332" status="VALID">
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          <idno type="Wikidata">Q230492</idno>
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            <address>
              <addrLine>500 Church Street Ann Arbor, MI 48109-1090</addrLine>
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