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

Abstract Currently, elective clinical target volume (CTV-N) definition for head and neck squamous cell carcinoma (HNSCC) is mostly based on the prevalence of nodal involvement for a given tumor location. In this work, we propose a probabilistic model for lymphatic metastatic spread that can quantify...

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Autores principales:	Roman Ludwig, Bertrand Pouymayou, Panagiotis Balermpas, Jan Unkelbach
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f8a986b47b6e4d1098879585bb0463fc
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spelling	oai:doaj.org-article:f8a986b47b6e4d1098879585bb0463fc2021-12-02T14:59:29ZA hidden Markov model for lymphatic tumor progression in the head and neck10.1038/s41598-021-91544-12045-2322https://doaj.org/article/f8a986b47b6e4d1098879585bb0463fc2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-91544-1https://doaj.org/toc/2045-2322Abstract Currently, elective clinical target volume (CTV-N) definition for head and neck squamous cell carcinoma (HNSCC) is mostly based on the prevalence of nodal involvement for a given tumor location. In this work, we propose a probabilistic model for lymphatic metastatic spread that can quantify the risk of microscopic involvement in lymph node levels (LNL) given the location of macroscopic metastases and T-category. This may allow for further personalized CTV-N definition based on an individual patient’s state of disease. We model the patient's state of metastatic lymphatic progression as a collection of hidden binary random variables that indicate the involvement of LNLs. In addition, each LNL is associated with observed binary random variables that indicate whether macroscopic metastases are detected. A hidden Markov model (HMM) is used to compute the probabilities of transitions between states over time. The underlying graph of the HMM represents the anatomy of the lymphatic drainage system. Learning of the transition probabilities is done via Markov chain Monte Carlo sampling and is based on a dataset of HNSCC patients in whom involvement of individual LNLs was reported. The model is demonstrated for ipsilateral metastatic spread in oropharyngeal HNSCC patients. We demonstrate the model's capability to quantify the risk of microscopic involvement in levels III and IV, depending on whether macroscopic metastases are observed in the upstream levels II and III, and depending on T-category. In conclusion, the statistical model of lymphatic progression may inform future, more personalized, guidelines on which LNL to include in the elective CTV. However, larger multi-institutional datasets for model parameter learning are required for that.Roman LudwigBertrand PouymayouPanagiotis BalermpasJan UnkelbachNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-17 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Roman Ludwig Bertrand Pouymayou Panagiotis Balermpas Jan Unkelbach A hidden Markov model for lymphatic tumor progression in the head and neck
description	Abstract Currently, elective clinical target volume (CTV-N) definition for head and neck squamous cell carcinoma (HNSCC) is mostly based on the prevalence of nodal involvement for a given tumor location. In this work, we propose a probabilistic model for lymphatic metastatic spread that can quantify the risk of microscopic involvement in lymph node levels (LNL) given the location of macroscopic metastases and T-category. This may allow for further personalized CTV-N definition based on an individual patient’s state of disease. We model the patient's state of metastatic lymphatic progression as a collection of hidden binary random variables that indicate the involvement of LNLs. In addition, each LNL is associated with observed binary random variables that indicate whether macroscopic metastases are detected. A hidden Markov model (HMM) is used to compute the probabilities of transitions between states over time. The underlying graph of the HMM represents the anatomy of the lymphatic drainage system. Learning of the transition probabilities is done via Markov chain Monte Carlo sampling and is based on a dataset of HNSCC patients in whom involvement of individual LNLs was reported. The model is demonstrated for ipsilateral metastatic spread in oropharyngeal HNSCC patients. We demonstrate the model's capability to quantify the risk of microscopic involvement in levels III and IV, depending on whether macroscopic metastases are observed in the upstream levels II and III, and depending on T-category. In conclusion, the statistical model of lymphatic progression may inform future, more personalized, guidelines on which LNL to include in the elective CTV. However, larger multi-institutional datasets for model parameter learning are required for that.
format	article
author	Roman Ludwig Bertrand Pouymayou Panagiotis Balermpas Jan Unkelbach
author_facet	Roman Ludwig Bertrand Pouymayou Panagiotis Balermpas Jan Unkelbach
author_sort	Roman Ludwig
title	A hidden Markov model for lymphatic tumor progression in the head and neck
title_short	A hidden Markov model for lymphatic tumor progression in the head and neck
title_full	A hidden Markov model for lymphatic tumor progression in the head and neck
title_fullStr	A hidden Markov model for lymphatic tumor progression in the head and neck
title_full_unstemmed	A hidden Markov model for lymphatic tumor progression in the head and neck
title_sort	hidden markov model for lymphatic tumor progression in the head and neck
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f8a986b47b6e4d1098879585bb0463fc
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A hidden Markov model for lymphatic tumor progression in the head and neck

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