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Lymphatic Progression eXplorer

Welcome to our online interface with which we aim to visualize the patterns of lymphatic progression in head and neck squamous cell carcinoma.

Our goal

Head & neck squamous cell carcinomas (HNSCCs) frequently metastasise through the lymphatic network. Hence, radiation oncologists often have to decide whether or not to include the different lymph node levels - anatomically defined regions in the head & neck that contain several lymph nodes - into the clinical target volume (CTV). Similarly, surgeons have to decide which lymph node levels to resect.

To aid them in their decision, the prevalence of metastatic involvement for HNSCC has been reported in numerous publications. However, the detailed patterns of progression, i.e. correlations between frequently metastatic lymph node levels as well as primary tumor characteristics remain insufficiently quantified. As a consequence, clinical guidelines on elective CTV definition are mostly based on prevalence of lymph node level involvement. Data that would allow for further personalization of CTV definition based on a patient's individual state of tumor progression is not available.

To tackle this, we extracted a dataset of 287 oropharyngal SCC patients treated at the University Hospital Zürich, for whom we report on a patient-individual basis the patterns of lymphatic metastatic progression together with primary tumor and patient risk factors. The data is made available in in raw format (a CSV table).

To make this data more accessible and especially easier to understand and draw conclusions from, we developed this web-based interface and in particular the dashboard where one can interact with the underlying data in a more intuitive way.

Beyond that, the dataset can be used for inference and risk prediction using a dynamic lymphatic progression model that we have developed, too.

Note: You are looking at version 0.2.9 of the interface, which is frozen to serve as a reference for readers of our recent publication. If you want to use the latest version of this website that we try to continually develop and improve, click here.


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The publications below describe the data we extracted at our hospital and also the purpose and use of this interface.

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Roman Ludwig*, Jean-Marc Hoffmann*, Bertrand Pouymayou*, Grégoire Morand, Martina Däppen, Matthias Guckenberger, Vincent Grégoire, Panagiotis Balermpas, Jan Unkelbach; *These authors contributed equally to this work
Detailed patient-individual reporting of lymph node involvement in oropharyngeal squamous cell carcinoma with an online interface,
Submitted to Radiother. & Oncology

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Roman Ludwig*, Jean-Marc Hoffmann*, Bertrand Pouymayou*, Grégoire Morand, Martina Däppen, Matthias Guckenberger, Vincent Grégoire, Panagiotis Balermpas, Jan Unkelbach; *These authors contributed equally to this work
A dataset on patient-individual lymph node involvement in oropharyngeal squamous cell carcinoma,
Submitted to Data in Brief

These publications refer to LyProX at a specific point in time. That particular version of the interface is available here.
The data is also hosted in a GitHub repository called lyDATA.

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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Roman Ludwig, Bertrand Pouymayou, Panagiotis Balermpas, Jan Unkelbach;
A hidden Markov model for lymphatic tumor progression in the head and neck,
Sci Rep 11, 12261 (2021)

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Bertrand Pouymayou, Panagiotis Balermpas, Oliver Riesterer, Matthias Guckenberger, Jan Unkelbach;
A Bayesian network model of lymphatic tumor progression for personalized elective CTV definition in head and neck cancers,
Phys. Med. Biol. 64, 165003 (2019)

You can find the underlying python code for these models in another GitHub repository named lymph. The documentation is provided on readthedocs.

A review paper summarizing how models of tumor progression may support CTV definition:

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Jan Unkelbach, Thomas Bortfeld, Carlos E. Cardenas, Vincent Gregoire, Wille Hager, Ben Heijmen, Robert Jeraj, Stine S. Korreman, Roman Ludwig, Bertrand Pouymayou, Nadya Shusharina, Jonas Söderberg, Iuliana Toma-Dasu, Esther G.C. Troost, Eliana Vasquez Osorio;
The role of computational methods for automating and improving clinical target volume definition,
Radiotherapy and Oncology 153, 15-25 (2020)