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Decision Theory versus Conventional Statistics for Personalized Therapy of Breast Cancer

Estrogen and progesterone receptors being present or not represents one of the most important biomarkers for therapy selection in breast cancer patients. Conventional measurement by immunohistochemistry (IHC) involves errors, and numerous attempts have been made to increase precision by additional i...

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Autores principales: Kenn, Michael, Karch, Rudolf, Cacsire Castillo-Tong, Dan, Singer, Christian F., Koelbl, Heinz, Schreiner, Wolfgang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9028435/
https://www.ncbi.nlm.nih.gov/pubmed/35455687
http://dx.doi.org/10.3390/jpm12040570
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author Kenn, Michael
Karch, Rudolf
Cacsire Castillo-Tong, Dan
Singer, Christian F.
Koelbl, Heinz
Schreiner, Wolfgang
author_facet Kenn, Michael
Karch, Rudolf
Cacsire Castillo-Tong, Dan
Singer, Christian F.
Koelbl, Heinz
Schreiner, Wolfgang
author_sort Kenn, Michael
collection PubMed
description Estrogen and progesterone receptors being present or not represents one of the most important biomarkers for therapy selection in breast cancer patients. Conventional measurement by immunohistochemistry (IHC) involves errors, and numerous attempts have been made to increase precision by additional information from gene expression. This raises the question of how to fuse information, in particular, if there is disagreement. It is the primary domain of Dempster–Shafer decision theory (DST) to deal with contradicting evidence on the same item (here: receptor status), obtained through different techniques. DST is widely used in technical settings, such as self-driving cars and aviation, and is also promising to deliver significant advantages in medicine. Using data from breast cancer patients already presented in previous work, we focus on comparing DST with classical statistics in this work, to pave the way for its application in medicine. First, we explain how DST not only considers probabilities (a single number per sample), but also incorporates uncertainty in a concept of ‘evidence’ (two numbers per sample). This allows for very powerful displays of patient data in so-called ternary plots, a novel and crucial advantage for medical interpretation. Results are obtained according to conventional statistics (ODDS) and, in parallel, according to DST. Agreement and differences are evaluated, and the particular merits of DST discussed. The presented application demonstrates how decision theory introduces new levels of confidence in diagnoses derived from medical data.
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spelling pubmed-90284352022-04-23 Decision Theory versus Conventional Statistics for Personalized Therapy of Breast Cancer Kenn, Michael Karch, Rudolf Cacsire Castillo-Tong, Dan Singer, Christian F. Koelbl, Heinz Schreiner, Wolfgang J Pers Med Article Estrogen and progesterone receptors being present or not represents one of the most important biomarkers for therapy selection in breast cancer patients. Conventional measurement by immunohistochemistry (IHC) involves errors, and numerous attempts have been made to increase precision by additional information from gene expression. This raises the question of how to fuse information, in particular, if there is disagreement. It is the primary domain of Dempster–Shafer decision theory (DST) to deal with contradicting evidence on the same item (here: receptor status), obtained through different techniques. DST is widely used in technical settings, such as self-driving cars and aviation, and is also promising to deliver significant advantages in medicine. Using data from breast cancer patients already presented in previous work, we focus on comparing DST with classical statistics in this work, to pave the way for its application in medicine. First, we explain how DST not only considers probabilities (a single number per sample), but also incorporates uncertainty in a concept of ‘evidence’ (two numbers per sample). This allows for very powerful displays of patient data in so-called ternary plots, a novel and crucial advantage for medical interpretation. Results are obtained according to conventional statistics (ODDS) and, in parallel, according to DST. Agreement and differences are evaluated, and the particular merits of DST discussed. The presented application demonstrates how decision theory introduces new levels of confidence in diagnoses derived from medical data. MDPI 2022-04-02 /pmc/articles/PMC9028435/ /pubmed/35455687 http://dx.doi.org/10.3390/jpm12040570 Text en © 2022 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
Kenn, Michael
Karch, Rudolf
Cacsire Castillo-Tong, Dan
Singer, Christian F.
Koelbl, Heinz
Schreiner, Wolfgang
Decision Theory versus Conventional Statistics for Personalized Therapy of Breast Cancer
title Decision Theory versus Conventional Statistics for Personalized Therapy of Breast Cancer
title_full Decision Theory versus Conventional Statistics for Personalized Therapy of Breast Cancer
title_fullStr Decision Theory versus Conventional Statistics for Personalized Therapy of Breast Cancer
title_full_unstemmed Decision Theory versus Conventional Statistics for Personalized Therapy of Breast Cancer
title_short Decision Theory versus Conventional Statistics for Personalized Therapy of Breast Cancer
title_sort decision theory versus conventional statistics for personalized therapy of breast cancer
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9028435/
https://www.ncbi.nlm.nih.gov/pubmed/35455687
http://dx.doi.org/10.3390/jpm12040570
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