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Reliability as a Precondition for Trust—Segmentation Reliability Analysis of Radiomic Features Improves Survival Prediction
Machine learning results based on radiomic analysis are often not transferrable. A potential reason for this is the variability of radiomic features due to varying human made segmentations. Therefore, the aim of this study was to provide comprehensive inter-reader reliability analysis of radiomic fe...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8871487/ https://www.ncbi.nlm.nih.gov/pubmed/35204338 http://dx.doi.org/10.3390/diagnostics12020247 |
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author | Müller-Franzes, Gustav Nebelung, Sven Schock, Justus Haarburger, Christoph Khader, Firas Pedersoli, Federico Schulze-Hagen, Maximilian Kuhl, Christiane Truhn, Daniel |
author_facet | Müller-Franzes, Gustav Nebelung, Sven Schock, Justus Haarburger, Christoph Khader, Firas Pedersoli, Federico Schulze-Hagen, Maximilian Kuhl, Christiane Truhn, Daniel |
author_sort | Müller-Franzes, Gustav |
collection | PubMed |
description | Machine learning results based on radiomic analysis are often not transferrable. A potential reason for this is the variability of radiomic features due to varying human made segmentations. Therefore, the aim of this study was to provide comprehensive inter-reader reliability analysis of radiomic features in five clinical image datasets and to assess the association of inter-reader reliability and survival prediction. In this study, we analyzed 4598 tumor segmentations in both computed tomography and magnetic resonance imaging data. We used a neural network to generate 100 additional segmentation outlines for each tumor and performed a reliability analysis of radiomic features. To prove clinical utility, we predicted patient survival based on all features and on the most reliable features. Survival prediction models for both computed tomography and magnetic resonance imaging datasets demonstrated less statistical spread and superior survival prediction when based on the most reliable features. Mean concordance indices were C(mean) = 0.58 [most reliable] vs. C(mean) = 0.56 [all] (p < 0.001, CT) and C(mean) = 0.58 vs. C(mean) = 0.57 (p = 0.23, MRI). Thus, preceding reliability analyses and selection of the most reliable radiomic features improves the underlying model’s ability to predict patient survival across clinical imaging modalities and tumor entities. |
format | Online Article Text |
id | pubmed-8871487 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-88714872022-02-25 Reliability as a Precondition for Trust—Segmentation Reliability Analysis of Radiomic Features Improves Survival Prediction Müller-Franzes, Gustav Nebelung, Sven Schock, Justus Haarburger, Christoph Khader, Firas Pedersoli, Federico Schulze-Hagen, Maximilian Kuhl, Christiane Truhn, Daniel Diagnostics (Basel) Article Machine learning results based on radiomic analysis are often not transferrable. A potential reason for this is the variability of radiomic features due to varying human made segmentations. Therefore, the aim of this study was to provide comprehensive inter-reader reliability analysis of radiomic features in five clinical image datasets and to assess the association of inter-reader reliability and survival prediction. In this study, we analyzed 4598 tumor segmentations in both computed tomography and magnetic resonance imaging data. We used a neural network to generate 100 additional segmentation outlines for each tumor and performed a reliability analysis of radiomic features. To prove clinical utility, we predicted patient survival based on all features and on the most reliable features. Survival prediction models for both computed tomography and magnetic resonance imaging datasets demonstrated less statistical spread and superior survival prediction when based on the most reliable features. Mean concordance indices were C(mean) = 0.58 [most reliable] vs. C(mean) = 0.56 [all] (p < 0.001, CT) and C(mean) = 0.58 vs. C(mean) = 0.57 (p = 0.23, MRI). Thus, preceding reliability analyses and selection of the most reliable radiomic features improves the underlying model’s ability to predict patient survival across clinical imaging modalities and tumor entities. MDPI 2022-01-19 /pmc/articles/PMC8871487/ /pubmed/35204338 http://dx.doi.org/10.3390/diagnostics12020247 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 Müller-Franzes, Gustav Nebelung, Sven Schock, Justus Haarburger, Christoph Khader, Firas Pedersoli, Federico Schulze-Hagen, Maximilian Kuhl, Christiane Truhn, Daniel Reliability as a Precondition for Trust—Segmentation Reliability Analysis of Radiomic Features Improves Survival Prediction |
title | Reliability as a Precondition for Trust—Segmentation Reliability Analysis of Radiomic Features Improves Survival Prediction |
title_full | Reliability as a Precondition for Trust—Segmentation Reliability Analysis of Radiomic Features Improves Survival Prediction |
title_fullStr | Reliability as a Precondition for Trust—Segmentation Reliability Analysis of Radiomic Features Improves Survival Prediction |
title_full_unstemmed | Reliability as a Precondition for Trust—Segmentation Reliability Analysis of Radiomic Features Improves Survival Prediction |
title_short | Reliability as a Precondition for Trust—Segmentation Reliability Analysis of Radiomic Features Improves Survival Prediction |
title_sort | reliability as a precondition for trust—segmentation reliability analysis of radiomic features improves survival prediction |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8871487/ https://www.ncbi.nlm.nih.gov/pubmed/35204338 http://dx.doi.org/10.3390/diagnostics12020247 |
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