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    Our publications on statistical models of lymphatic progression for estimating
    the probability of occult metastases in lymph node levels. These models can be
    trained using data like the one we present here.
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        <img class="is-rounded" src="{% static 'hmm-paper.png' %}" alt="paper icon">
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        Roman Ludwig, Bertrand Pouymayou, Panagiotis Balermpas, Jan Unkelbach;
        <br>
        <strong class="has-text-secondary">A hidden Markov model for lymphatic
        tumor progression in the head and neck,</strong>
        <br>
        <em>Sci Rep</em> <strong>11,</strong> 12261 (2021)
        <a href="https://doi.org/10.1038/s41598-021-91544-1" target="_blank">10.1038/s41598-021-91544-1</a>
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        <img class="is-rounded" src="{% static 'bn-paper.png' %}" alt="paper icon">
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      <p>
        Bertrand Pouymayou, Panagiotis Balermpas, Oliver Riesterer, Matthias
        Guckenberger, Jan Unkelbach;
        <br>
        <strong class="has-text-secondary">A Bayesian network model of lymphatic
        tumor progression for personalized elective CTV definition in head and
        neck cancers,</strong>
        <br>
        <em>Phys. Med. Biol.</em> <strong>64,</strong> 165003 (2019)
        <a href="https://doi.org/10.1088/1361-6560/ab2a18" target="_blank">https://doi.org/10.1088/1361-6560/ab2a18</a>
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      You can find the underlying python code for these models in another GitHub
      repository named <a href="https://github.com/rmnldwg/lymph"><code>lymph</code></a>.
      The documentation is provided on <a href="https://lymph-model.readthedocs.io">readthedocs</a>.
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