LyProX logo

Lymphatic Progression eXplorer

Collecting, sharing, and visualizing data on patterns
of lymphatic progression in head and neck cancer

The Purpose of LyProX

Understanding lymphatic tumor progression in head and neck cancer

LyProX is designed to collect, share, and visualize data on lymphatic metastases in head and neck squamous cell carcinomas. By providing detailed insights into progression patterns, it aims to support researchers and clinicians in working towards more personalized elective nodal treatments.

Personalizing Treatment Planning

Quantifying individual risk for better decision-making

Current clinical guidelines (e.g. by Biau et al. ) for the elective nodal clinical target volume (CTV-N) in head and neck cancer are mostly based on the prevalence of involvement in any given lymph node level (LNL). However, patients with vastly different states of lymphatic spread are often treated the same way.

The basis for a more personalized approach is the quantification of individual risk for lymphatic metastases. This, in turn, requires a comprehensive understanding of the detailed patterns of lymphatic spread. LyProX aims to provide this understanding by collecting and visualizing such data.

Schematic of the lymphatic system
Screenshot of the LyProX dashboard

Interactive Data Exploration

Hypothesis generation through visualization

With an intuitive interface, LyProX allows users to explore datasets of lymphatic progression patterns interactively. For each patient in the different cohorts, it stores in which LNLs metastases were found using different diagnostic modalities or pathological assessments after a neck dissection.

This detailed per-patient and per-level information can then be filtered and aggregated in the dashboard . For example, one may be interested in the correlation between contralateral involvement and the lateralization of the primary tumor. Or how much more likely downstream levels are to be involved if upstream levels are affected.

Making the data interactively explorable fosters hypothesis generation and encourages further research into lymphatic spread patterns. Also, the data may be downloaded as a CSV table or fetched from our data repository for further analysis.

Statistical Models of Progression

Predicting lymphatic spread patterns using machine learning

After pulishing our first datasets, we started working on probabilistic models of lymphatic tumor progression These interpretable hidden Markov models (HMMs) are trained on the data published in LyProX and accurately predict the risk of occult disease in any LNL, given a new patient's diagnosis.

Schematic of the lymphatic graph

lycosystem

An ecosystem of tools, websites, and data repositories
for lymphatic tumor progression research

rmnldwg/lymph

Github social card for lymph
Python package for statistical modelling of lymphatic metastatic spread in head & neck cancer.

lycosystem/lyprox

Github social card for lyprox
Web app for exploring patterns and correlations in the lymph node level involvements of head & neck cancer patients.

rmnldwg/lydata

Github social card for lydata
Repository for storing datasets that report detailed lymphatic progression patterns of head & neck cancer patients.

rmnldwg/lyscripts

Github social card for lyscripts
Scripts and utilities for commonly used tasks related to the modelling of lymphatic tumor progression.

lycosystem/lymixture

Github social card for lymixture
Extension to the lymph-model and lyscripts package.

Our Publications

Published papers on datasets and models of lymphatic tumor progression

[1]

Radiotherapy and Oncology 153 (2020)
J. Unkelbach, T. Bortfeld, C. Cardenas, V. Gregoire, W. Hager, B. Heijmen, R. Jeraj, S. Korreman, R. Ludwig, B. Pouymayou, N. Shusharina, J. Söderberg, I. Toma-Dasu, E. Troost, E. Vasquez Osorio

The role of computational methods for automating and improving clinical target volume definition

36
[2]

Scientific Reports 11 (2021)
R. Ludwig, B. Pouymayou, P. Balermpas, J. Unkelbach

A hidden Markov model for lymphatic tumor progression in the head and neck

14
[3]

Radiotherapy and Oncology 169 (2022)
R. Ludwig, J. Hoffmann, B. Pouymayou, M. Däppen, G. Morand, M. Guckenberger, V. Grégoire, P. Balermpas, J. Unkelbach

Detailed patient-individual reporting of lymph node involvement in oropharyngeal squamous cell carcinoma with an online interface

10
[4]

Data in Brief 43 (2022)
R. Ludwig, J. Hoffmann, B. Pouymayou, G. Morand, M. Däppen, M. Guckenberger, V. Grégoire, P. Balermpas, J. Unkelbach

A dataset on patient-individual lymph node involvement in oropharyngeal squamous cell carcinoma

6
[5]

Data in Brief 52 (2024)
R. Ludwig, A. Schubert, D. Barbatei, L. Bauwens, S. Werlen, O. Elicin, M. Dettmer, P. Zrounba, P. Balermpas, B. Pouymayou, V. Grégoire, R. Giger, J. Unkelbach

A multi-centric dataset on patient-individual pathological lymph node involvement in head and neck squamous cell carcinoma

1
[6]

Scientific Reports 14 (2024)
R. Ludwig, A. Schubert, D. Barbatei, L. Bauwens, J. Hoffmann, S. Werlen, O. Elicin, M. Dettmer, P. Zrounba, B. Pouymayou, P. Balermpas, V. Grégoire, R. Giger, J. Unkelbach

Modelling the lymphatic metastatic progression pathways of OPSCC from multi-institutional datasets

3
[7]

Data in Brief 60 (2025)
S. Benavente, R. Ludwig, P. Balermpas, J. Unkelbach

Updating “A dataset on patient-individual lymph node involvement in oropharyngeal squamous cell carcinoma” with an additional dataset from a second institution

0