Comparison of radiomic feature aggregation methods for patients with multiple tumors

Abstract Radiomic feature analysis has been shown to be effective at analyzing diagnostic images to model cancer outcomes. It has not yet been established how to best combine radiomic features in cancer patients with multifocal tumors. As the number of patients with multifocal metastatic cancer cont...

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Autores principales:	Enoch Chang, Marina Z. Joel, Hannah Y. Chang, Justin Du, Omaditya Khanna, Antonio Omuro, Veronica Chiang, Sanjay Aneja
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/59853435739a456ba6fef22045cf3adb
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spelling	oai:doaj.org-article:59853435739a456ba6fef22045cf3adb2021-12-02T16:51:50ZComparison of radiomic feature aggregation methods for patients with multiple tumors10.1038/s41598-021-89114-62045-2322https://doaj.org/article/59853435739a456ba6fef22045cf3adb2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-89114-6https://doaj.org/toc/2045-2322Abstract Radiomic feature analysis has been shown to be effective at analyzing diagnostic images to model cancer outcomes. It has not yet been established how to best combine radiomic features in cancer patients with multifocal tumors. As the number of patients with multifocal metastatic cancer continues to rise, there is a need for improving personalized patient-level prognosis to better inform treatment. We compared six mathematical methods of combining radiomic features of 3,596 tumors in 831 patients with multiple brain metastases and evaluated the performance of these aggregation methods using three survival models: a standard Cox proportional hazards model, a Cox proportional hazards model with LASSO regression, and a random survival forest. Across all three survival models, the weighted average of the largest three metastases had the highest concordance index (95% confidence interval) of 0.627 (0.595–0.661) for the Cox proportional hazards model, 0.628 (0.591–0.666) for the Cox proportional hazards model with LASSO regression, and 0.652 (0.565–0.727) for the random survival forest model. This finding was consistent when evaluating patients with different numbers of brain metastases and different tumor volumes. Radiomic features can be effectively combined to estimate patient-level outcomes in patients with multifocal brain metastases. Future studies are needed to confirm that the volume-weighted average of the largest three tumors is an effective method for combining radiomic features across other imaging modalities and tumor types.Enoch ChangMarina Z. JoelHannah Y. ChangJustin DuOmaditya KhannaAntonio OmuroVeronica ChiangSanjay AnejaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Enoch Chang Marina Z. Joel Hannah Y. Chang Justin Du Omaditya Khanna Antonio Omuro Veronica Chiang Sanjay Aneja Comparison of radiomic feature aggregation methods for patients with multiple tumors
description	Abstract Radiomic feature analysis has been shown to be effective at analyzing diagnostic images to model cancer outcomes. It has not yet been established how to best combine radiomic features in cancer patients with multifocal tumors. As the number of patients with multifocal metastatic cancer continues to rise, there is a need for improving personalized patient-level prognosis to better inform treatment. We compared six mathematical methods of combining radiomic features of 3,596 tumors in 831 patients with multiple brain metastases and evaluated the performance of these aggregation methods using three survival models: a standard Cox proportional hazards model, a Cox proportional hazards model with LASSO regression, and a random survival forest. Across all three survival models, the weighted average of the largest three metastases had the highest concordance index (95% confidence interval) of 0.627 (0.595–0.661) for the Cox proportional hazards model, 0.628 (0.591–0.666) for the Cox proportional hazards model with LASSO regression, and 0.652 (0.565–0.727) for the random survival forest model. This finding was consistent when evaluating patients with different numbers of brain metastases and different tumor volumes. Radiomic features can be effectively combined to estimate patient-level outcomes in patients with multifocal brain metastases. Future studies are needed to confirm that the volume-weighted average of the largest three tumors is an effective method for combining radiomic features across other imaging modalities and tumor types.
format	article
author	Enoch Chang Marina Z. Joel Hannah Y. Chang Justin Du Omaditya Khanna Antonio Omuro Veronica Chiang Sanjay Aneja
author_facet	Enoch Chang Marina Z. Joel Hannah Y. Chang Justin Du Omaditya Khanna Antonio Omuro Veronica Chiang Sanjay Aneja
author_sort	Enoch Chang
title	Comparison of radiomic feature aggregation methods for patients with multiple tumors
title_short	Comparison of radiomic feature aggregation methods for patients with multiple tumors
title_full	Comparison of radiomic feature aggregation methods for patients with multiple tumors
title_fullStr	Comparison of radiomic feature aggregation methods for patients with multiple tumors
title_full_unstemmed	Comparison of radiomic feature aggregation methods for patients with multiple tumors
title_sort	comparison of radiomic feature aggregation methods for patients with multiple tumors
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/59853435739a456ba6fef22045cf3adb
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Comparison of radiomic feature aggregation methods for patients with multiple tumors

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