Cargando…

Bayesian Networks to Support Decision-Making for Immune-Checkpoint Blockade in Recurrent/Metastatic (R/M) Head and Neck Squamous Cell Carcinoma (HNSCC)

SIMPLE SUMMARY: Tumor therapy in many human malignancies, including head and neck cancer, is increasingly demanding due to advances in diagnostics and individualized treatments. Multidisciplinary tumor boards, especially molecular tumor boards, consider a great amount of information to find the opti...

Descripción completa

Detalles Bibliográficos
Autores principales: Huehn, Marius, Gaebel, Jan, Oeser, Alexander, Dietz, Andreas, Neumuth, Thomas, Wichmann, Gunnar, Stoehr, Matthaeus
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8657168/
https://www.ncbi.nlm.nih.gov/pubmed/34884998
http://dx.doi.org/10.3390/cancers13235890
_version_ 1784612448640172032
author Huehn, Marius
Gaebel, Jan
Oeser, Alexander
Dietz, Andreas
Neumuth, Thomas
Wichmann, Gunnar
Stoehr, Matthaeus
author_facet Huehn, Marius
Gaebel, Jan
Oeser, Alexander
Dietz, Andreas
Neumuth, Thomas
Wichmann, Gunnar
Stoehr, Matthaeus
author_sort Huehn, Marius
collection PubMed
description SIMPLE SUMMARY: Tumor therapy in many human malignancies, including head and neck cancer, is increasingly demanding due to advances in diagnostics and individualized treatments. Multidisciplinary tumor boards, especially molecular tumor boards, consider a great amount of information to find the optimal treatment decision. Clinical decision support systems can help in optimizing this complex decision-making process. We designed a digital patient model based on conditional probability algorithms as Bayesian networks to support the decision-making process regarding treatment with approved immunotherapeutic agents (Nivolumab and Pembrolizumab). The model is able to process relevant clinical information to recommend a certain immunotherapeutic agent based on literature, approval, and guidelines. ABSTRACT: New diagnostic methods and novel therapeutic agents spawn additional and heterogeneous information, leading to an increasingly complex decision-making process for optimal treatment of cancer. A great amount of information is collected in organ-specific multidisciplinary tumor boards (MDTBs). By considering the patient’s tumor properties, molecular pathological test results, and comorbidities, the MDTB has to consent an evidence-based treatment decision. Immunotherapies are increasingly important in today’s cancer treatment, resulting in detailed information that influences the decision-making process. Clinical decision support systems can facilitate a better understanding via processing of multiple datasets of oncological cases and molecular genetic information, potentially fostering transparency and comprehensibility of available information, eventually leading to an optimum treatment decision for the individual patient. We constructed a digital patient model based on Bayesian networks to combine the relevant patient-specific and molecular data with depended probabilities derived from pertinent studies and clinical guidelines to calculate treatment decisions in head and neck squamous cell carcinoma (HNSCC). In a validation analysis, the model can provide guidance within the growing subject of immunotherapy in HNSCC and, based on its ability to calculate reliable probabilities, facilitates estimation of suitable therapy options. We compared actual treatment decisions of 25 patients with the calculated recommendations of our model and found significant concordance (Cohen’s κ = 0.505, p = 0.009) and 84% accuracy.
format Online
Article
Text
id pubmed-8657168
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-86571682021-12-10 Bayesian Networks to Support Decision-Making for Immune-Checkpoint Blockade in Recurrent/Metastatic (R/M) Head and Neck Squamous Cell Carcinoma (HNSCC) Huehn, Marius Gaebel, Jan Oeser, Alexander Dietz, Andreas Neumuth, Thomas Wichmann, Gunnar Stoehr, Matthaeus Cancers (Basel) Article SIMPLE SUMMARY: Tumor therapy in many human malignancies, including head and neck cancer, is increasingly demanding due to advances in diagnostics and individualized treatments. Multidisciplinary tumor boards, especially molecular tumor boards, consider a great amount of information to find the optimal treatment decision. Clinical decision support systems can help in optimizing this complex decision-making process. We designed a digital patient model based on conditional probability algorithms as Bayesian networks to support the decision-making process regarding treatment with approved immunotherapeutic agents (Nivolumab and Pembrolizumab). The model is able to process relevant clinical information to recommend a certain immunotherapeutic agent based on literature, approval, and guidelines. ABSTRACT: New diagnostic methods and novel therapeutic agents spawn additional and heterogeneous information, leading to an increasingly complex decision-making process for optimal treatment of cancer. A great amount of information is collected in organ-specific multidisciplinary tumor boards (MDTBs). By considering the patient’s tumor properties, molecular pathological test results, and comorbidities, the MDTB has to consent an evidence-based treatment decision. Immunotherapies are increasingly important in today’s cancer treatment, resulting in detailed information that influences the decision-making process. Clinical decision support systems can facilitate a better understanding via processing of multiple datasets of oncological cases and molecular genetic information, potentially fostering transparency and comprehensibility of available information, eventually leading to an optimum treatment decision for the individual patient. We constructed a digital patient model based on Bayesian networks to combine the relevant patient-specific and molecular data with depended probabilities derived from pertinent studies and clinical guidelines to calculate treatment decisions in head and neck squamous cell carcinoma (HNSCC). In a validation analysis, the model can provide guidance within the growing subject of immunotherapy in HNSCC and, based on its ability to calculate reliable probabilities, facilitates estimation of suitable therapy options. We compared actual treatment decisions of 25 patients with the calculated recommendations of our model and found significant concordance (Cohen’s κ = 0.505, p = 0.009) and 84% accuracy. MDPI 2021-11-23 /pmc/articles/PMC8657168/ /pubmed/34884998 http://dx.doi.org/10.3390/cancers13235890 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Huehn, Marius
Gaebel, Jan
Oeser, Alexander
Dietz, Andreas
Neumuth, Thomas
Wichmann, Gunnar
Stoehr, Matthaeus
Bayesian Networks to Support Decision-Making for Immune-Checkpoint Blockade in Recurrent/Metastatic (R/M) Head and Neck Squamous Cell Carcinoma (HNSCC)
title Bayesian Networks to Support Decision-Making for Immune-Checkpoint Blockade in Recurrent/Metastatic (R/M) Head and Neck Squamous Cell Carcinoma (HNSCC)
title_full Bayesian Networks to Support Decision-Making for Immune-Checkpoint Blockade in Recurrent/Metastatic (R/M) Head and Neck Squamous Cell Carcinoma (HNSCC)
title_fullStr Bayesian Networks to Support Decision-Making for Immune-Checkpoint Blockade in Recurrent/Metastatic (R/M) Head and Neck Squamous Cell Carcinoma (HNSCC)
title_full_unstemmed Bayesian Networks to Support Decision-Making for Immune-Checkpoint Blockade in Recurrent/Metastatic (R/M) Head and Neck Squamous Cell Carcinoma (HNSCC)
title_short Bayesian Networks to Support Decision-Making for Immune-Checkpoint Blockade in Recurrent/Metastatic (R/M) Head and Neck Squamous Cell Carcinoma (HNSCC)
title_sort bayesian networks to support decision-making for immune-checkpoint blockade in recurrent/metastatic (r/m) head and neck squamous cell carcinoma (hnscc)
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8657168/
https://www.ncbi.nlm.nih.gov/pubmed/34884998
http://dx.doi.org/10.3390/cancers13235890
work_keys_str_mv AT huehnmarius bayesiannetworkstosupportdecisionmakingforimmunecheckpointblockadeinrecurrentmetastaticrmheadandnecksquamouscellcarcinomahnscc
AT gaebeljan bayesiannetworkstosupportdecisionmakingforimmunecheckpointblockadeinrecurrentmetastaticrmheadandnecksquamouscellcarcinomahnscc
AT oeseralexander bayesiannetworkstosupportdecisionmakingforimmunecheckpointblockadeinrecurrentmetastaticrmheadandnecksquamouscellcarcinomahnscc
AT dietzandreas bayesiannetworkstosupportdecisionmakingforimmunecheckpointblockadeinrecurrentmetastaticrmheadandnecksquamouscellcarcinomahnscc
AT neumuththomas bayesiannetworkstosupportdecisionmakingforimmunecheckpointblockadeinrecurrentmetastaticrmheadandnecksquamouscellcarcinomahnscc
AT wichmanngunnar bayesiannetworkstosupportdecisionmakingforimmunecheckpointblockadeinrecurrentmetastaticrmheadandnecksquamouscellcarcinomahnscc
AT stoehrmatthaeus bayesiannetworkstosupportdecisionmakingforimmunecheckpointblockadeinrecurrentmetastaticrmheadandnecksquamouscellcarcinomahnscc