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The Impact of Image Acquisition Parameters and ComBat Harmonization on the Predictive Performance of Radiomics: A Renal Cell Carcinoma Model

Radiomics, one of the potential methods for developing clinical biomarker, is one of the exponentially growing research fields. In addition to its potential, several limitations have been identified in this field, and most importantly the effects of variations in imaging parameters on radiomic featu...

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Detalles Bibliográficos
Autores principales: Ibrahim, Abdalla, Lu, Lin, Yang, Hao, Akin, Oguz, Schwartz, Lawrence H., Zhao, Binsheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10121203/
https://www.ncbi.nlm.nih.gov/pubmed/37091743
http://dx.doi.org/10.3390/app12199824
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author Ibrahim, Abdalla
Lu, Lin
Yang, Hao
Akin, Oguz
Schwartz, Lawrence H.
Zhao, Binsheng
author_facet Ibrahim, Abdalla
Lu, Lin
Yang, Hao
Akin, Oguz
Schwartz, Lawrence H.
Zhao, Binsheng
author_sort Ibrahim, Abdalla
collection PubMed
description Radiomics, one of the potential methods for developing clinical biomarker, is one of the exponentially growing research fields. In addition to its potential, several limitations have been identified in this field, and most importantly the effects of variations in imaging parameters on radiomic features (RFs). In this study, we investigate the potential of RFs to predict overall survival in patients with clear cell renal cell carcinoma, as well as the impact of ComBat harmonization on the performance of RF models. We assessed the robustness of the results by performing the analyses a thousand times. Publicly available CT scans of 179 patients were retrospectively collected and analyzed. The scans were acquired using different imaging vendors and parameters in different medical centers. The performance was calculated by averaging the metrics over all runs. On average, the clinical model significantly outperformed the radiomic models. The use of ComBat harmonization, on average, did not significantly improve the performance of radiomic models. Hence, the variability in image acquisition and reconstruction parameters significantly affect the performance of radiomic models. The development of radiomic specific harmonization techniques remain a necessity for the advancement of the field.
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spelling pubmed-101212032023-04-21 The Impact of Image Acquisition Parameters and ComBat Harmonization on the Predictive Performance of Radiomics: A Renal Cell Carcinoma Model Ibrahim, Abdalla Lu, Lin Yang, Hao Akin, Oguz Schwartz, Lawrence H. Zhao, Binsheng Appl Sci (Basel) Article Radiomics, one of the potential methods for developing clinical biomarker, is one of the exponentially growing research fields. In addition to its potential, several limitations have been identified in this field, and most importantly the effects of variations in imaging parameters on radiomic features (RFs). In this study, we investigate the potential of RFs to predict overall survival in patients with clear cell renal cell carcinoma, as well as the impact of ComBat harmonization on the performance of RF models. We assessed the robustness of the results by performing the analyses a thousand times. Publicly available CT scans of 179 patients were retrospectively collected and analyzed. The scans were acquired using different imaging vendors and parameters in different medical centers. The performance was calculated by averaging the metrics over all runs. On average, the clinical model significantly outperformed the radiomic models. The use of ComBat harmonization, on average, did not significantly improve the performance of radiomic models. Hence, the variability in image acquisition and reconstruction parameters significantly affect the performance of radiomic models. The development of radiomic specific harmonization techniques remain a necessity for the advancement of the field. 2022-10 2022-09-29 /pmc/articles/PMC10121203/ /pubmed/37091743 http://dx.doi.org/10.3390/app12199824 Text en https://creativecommons.org/licenses/by/4.0/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
Ibrahim, Abdalla
Lu, Lin
Yang, Hao
Akin, Oguz
Schwartz, Lawrence H.
Zhao, Binsheng
The Impact of Image Acquisition Parameters and ComBat Harmonization on the Predictive Performance of Radiomics: A Renal Cell Carcinoma Model
title The Impact of Image Acquisition Parameters and ComBat Harmonization on the Predictive Performance of Radiomics: A Renal Cell Carcinoma Model
title_full The Impact of Image Acquisition Parameters and ComBat Harmonization on the Predictive Performance of Radiomics: A Renal Cell Carcinoma Model
title_fullStr The Impact of Image Acquisition Parameters and ComBat Harmonization on the Predictive Performance of Radiomics: A Renal Cell Carcinoma Model
title_full_unstemmed The Impact of Image Acquisition Parameters and ComBat Harmonization on the Predictive Performance of Radiomics: A Renal Cell Carcinoma Model
title_short The Impact of Image Acquisition Parameters and ComBat Harmonization on the Predictive Performance of Radiomics: A Renal Cell Carcinoma Model
title_sort impact of image acquisition parameters and combat harmonization on the predictive performance of radiomics: a renal cell carcinoma model
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10121203/
https://www.ncbi.nlm.nih.gov/pubmed/37091743
http://dx.doi.org/10.3390/app12199824
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