Header

UZH-Logo

Maintenance Infos

A Bayesian network model of lymphatic tumor progression for personalized elective CTV definition in head and neck cancers


Pouymayou, Bertrand; Balermpas, Panagiotis; Riesterer, Oliver; Guckenberger, Matthias; Unkelbach, Jan (2019). A Bayesian network model of lymphatic tumor progression for personalized elective CTV definition in head and neck cancers. Physics in Medicine and Biology, 64(16):165003.

Abstract

Many tumors including head and neck squamous cell carcinoma (HNSCC) spread along the lymphatic network. Current imaging modalities can only detect sufficiently large metastases. Therefore, adjacent lymph node levels (LNL) are irradiated electively since they may harbor microscopic tumors. We apply Bayesian Networks (BN) to model lymphatic tumor progression. The model can subsequently be used to personalize the risk estimation of microscopic lymph node metastases in newly diagnosed patients based on their distribution of macroscopic metastases. A BN is a graphical representation of a joint probability distribution. We represent LNLs by binary random variables corresponding to the BN nodes. Each LNL is associated with a hidden microscopic state and an observed macroscopic state (e.g. 18F-FDG-PET/CT imaging). The primary tumor is represented by network input nodes. We demonstrate the concept for early T-stage oropharyngeal carcinomas and their spread to ipsilateral lymph node levels (LNL) Ib to IV. We show that the BN parameters can be efficiently learnt by merging pathology findings on microscopic tumor progression (which is limited to a few published studies) and imaging data on macroscopic tumor progression such as CT and 18F-FDG-PET (which are widely available in clinical practice). The trained network can be used to quantify how the distribution of macroscopic metastases impacts the probability of microscopic involvement of the remaining LNLs. The analysis suggests that the risk of microscopic involvement of level IV exceeds 5% only if level III harbors metastases. Excluding level IV from the elective CTV for other patients would reduce the integral dose delivered to the patient and potentially reduce acute and late side effects.

Abstract

Many tumors including head and neck squamous cell carcinoma (HNSCC) spread along the lymphatic network. Current imaging modalities can only detect sufficiently large metastases. Therefore, adjacent lymph node levels (LNL) are irradiated electively since they may harbor microscopic tumors. We apply Bayesian Networks (BN) to model lymphatic tumor progression. The model can subsequently be used to personalize the risk estimation of microscopic lymph node metastases in newly diagnosed patients based on their distribution of macroscopic metastases. A BN is a graphical representation of a joint probability distribution. We represent LNLs by binary random variables corresponding to the BN nodes. Each LNL is associated with a hidden microscopic state and an observed macroscopic state (e.g. 18F-FDG-PET/CT imaging). The primary tumor is represented by network input nodes. We demonstrate the concept for early T-stage oropharyngeal carcinomas and their spread to ipsilateral lymph node levels (LNL) Ib to IV. We show that the BN parameters can be efficiently learnt by merging pathology findings on microscopic tumor progression (which is limited to a few published studies) and imaging data on macroscopic tumor progression such as CT and 18F-FDG-PET (which are widely available in clinical practice). The trained network can be used to quantify how the distribution of macroscopic metastases impacts the probability of microscopic involvement of the remaining LNLs. The analysis suggests that the risk of microscopic involvement of level IV exceeds 5% only if level III harbors metastases. Excluding level IV from the elective CTV for other patients would reduce the integral dose delivered to the patient and potentially reduce acute and late side effects.

Statistics

Citations

Dimensions.ai Metrics
1 citation in Web of Science®
1 citation in Scopus®
Google Scholar™

Altmetrics

Downloads

1 download since deposited on 05 Feb 2020
1 download since 12 months
Detailed statistics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > University Hospital Zurich > Clinic for Radiation Oncology
Dewey Decimal Classification:610 Medicine & health
Language:English
Date:14 August 2019
Deposited On:05 Feb 2020 15:50
Last Modified:05 Feb 2020 15:50
Publisher:IOP Publishing
ISSN:0031-9155
OA Status:Closed
Publisher DOI:https://doi.org/10.1088/1361-6560/ab2a18
PubMed ID:31207591

Download

Closed Access: Download allowed only for UZH members