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An automated machine learning framework to optimize radiomics model construction validated on twelve clinical applications

Fuente: arXiv

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Auteurs principaux:	Starmans, Martijn P. A., van der Voort, Sebastian R., Phil, Thomas, Timbergen, Milea J. M., Vos, Melissa, Padmos, Guillaume A., Kessels, Wouter, Hanff, David, Grunhagen, Dirk J., Verhoef, Cornelis, Sleijfer, Stefan, Bent, Martin J. van den, Smits, Marion, Dwarkasing, Roy S., Els, Christopher J., Fiduzi, Federico, van Leenders, Geert J. L. H., Blazevic, Anela, Hofland, Johannes, Brabander, Tessa, van Gils, Renza A. H., Franssen, Gaston J. H., Feelders, Richard A., de Herder, Wouter W., Buisman, Florian E., Willemssen, Francois E. J. A., Koerkamp, Bas Groot, Angus, Lindsay, van der Veldt, Astrid A. M., Rajicic, Ana, Odink, Arlette E., Deen, Mitchell, T., Jose M. Castillo, Veenland, Jifke, Schoots, Ivo, Renckens, Michel, Doukas, Michail, de Man, Rob A., IJzermans, Jan N. M., Miclea, Razvan L., Vermeulen, Peter B., Bron, Esther E., Thomeer, Maarten G., Visser, Jacob J., Niessen, Wiro J., Klein, Stefan
Format:	Preprint
Publié:	2021
Sujets:	Image and Video Processing Computer Vision and Pattern Recognition
Accès en ligne:	Acceder al recurso
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_version_	1866915188523925504
author	Starmans, Martijn P. A. van der Voort, Sebastian R. Phil, Thomas Timbergen, Milea J. M. Vos, Melissa Padmos, Guillaume A. Kessels, Wouter Hanff, David Grunhagen, Dirk J. Verhoef, Cornelis Sleijfer, Stefan Bent, Martin J. van den Smits, Marion Dwarkasing, Roy S. Els, Christopher J. Fiduzi, Federico van Leenders, Geert J. L. H. Blazevic, Anela Hofland, Johannes Brabander, Tessa van Gils, Renza A. H. Franssen, Gaston J. H. Feelders, Richard A. de Herder, Wouter W. Buisman, Florian E. Willemssen, Francois E. J. A. Koerkamp, Bas Groot Angus, Lindsay van der Veldt, Astrid A. M. Rajicic, Ana Odink, Arlette E. Deen, Mitchell T., Jose M. Castillo Veenland, Jifke Schoots, Ivo Renckens, Michel Doukas, Michail de Man, Rob A. IJzermans, Jan N. M. Miclea, Razvan L. Vermeulen, Peter B. Bron, Esther E. Thomeer, Maarten G. Visser, Jacob J. Niessen, Wiro J. Klein, Stefan
author_facet	Starmans, Martijn P. A. van der Voort, Sebastian R. Phil, Thomas Timbergen, Milea J. M. Vos, Melissa Padmos, Guillaume A. Kessels, Wouter Hanff, David Grunhagen, Dirk J. Verhoef, Cornelis Sleijfer, Stefan Bent, Martin J. van den Smits, Marion Dwarkasing, Roy S. Els, Christopher J. Fiduzi, Federico van Leenders, Geert J. L. H. Blazevic, Anela Hofland, Johannes Brabander, Tessa van Gils, Renza A. H. Franssen, Gaston J. H. Feelders, Richard A. de Herder, Wouter W. Buisman, Florian E. Willemssen, Francois E. J. A. Koerkamp, Bas Groot Angus, Lindsay van der Veldt, Astrid A. M. Rajicic, Ana Odink, Arlette E. Deen, Mitchell T., Jose M. Castillo Veenland, Jifke Schoots, Ivo Renckens, Michel Doukas, Michail de Man, Rob A. IJzermans, Jan N. M. Miclea, Razvan L. Vermeulen, Peter B. Bron, Esther E. Thomeer, Maarten G. Visser, Jacob J. Niessen, Wiro J. Klein, Stefan
contents	Predicting clinical outcomes from medical images using quantitative features (``radiomics'') requires many method design choices, Currently, in new clinical applications, finding the optimal radiomics method out of the wide range of methods relies on a manual, heuristic trial-and-error process. We introduce a novel automated framework that optimizes radiomics workflow construction per application by standardizing the radiomics workflow in modular components, including a large collection of algorithms for each component, and formulating a combined algorithm selection and hyperparameter optimization problem. To solve it, we employ automated machine learning through two strategies (random search and Bayesian optimization) and three ensembling approaches. Results show that a medium-sized random search and straight-forward ensembling perform similar to more advanced methods while being more efficient. Validated across twelve clinical applications, our approach outperforms both a radiomics baseline and human experts. Concluding, our framework improves and streamlines radiomics research by fully automatically optimizing radiomics workflow construction. To facilitate reproducibility, we publicly release six datasets, software of the method, and code to reproduce this study.
format	Preprint
id	arxiv_https___arxiv_org_abs_2108_08618
institution	arXiv
publishDate	2021
record_format	arxiv
spellingShingle	An automated machine learning framework to optimize radiomics model construction validated on twelve clinical applications Starmans, Martijn P. A. van der Voort, Sebastian R. Phil, Thomas Timbergen, Milea J. M. Vos, Melissa Padmos, Guillaume A. Kessels, Wouter Hanff, David Grunhagen, Dirk J. Verhoef, Cornelis Sleijfer, Stefan Bent, Martin J. van den Smits, Marion Dwarkasing, Roy S. Els, Christopher J. Fiduzi, Federico van Leenders, Geert J. L. H. Blazevic, Anela Hofland, Johannes Brabander, Tessa van Gils, Renza A. H. Franssen, Gaston J. H. Feelders, Richard A. de Herder, Wouter W. Buisman, Florian E. Willemssen, Francois E. J. A. Koerkamp, Bas Groot Angus, Lindsay van der Veldt, Astrid A. M. Rajicic, Ana Odink, Arlette E. Deen, Mitchell T., Jose M. Castillo Veenland, Jifke Schoots, Ivo Renckens, Michel Doukas, Michail de Man, Rob A. IJzermans, Jan N. M. Miclea, Razvan L. Vermeulen, Peter B. Bron, Esther E. Thomeer, Maarten G. Visser, Jacob J. Niessen, Wiro J. Klein, Stefan Image and Video Processing Computer Vision and Pattern Recognition Predicting clinical outcomes from medical images using quantitative features (``radiomics'') requires many method design choices, Currently, in new clinical applications, finding the optimal radiomics method out of the wide range of methods relies on a manual, heuristic trial-and-error process. We introduce a novel automated framework that optimizes radiomics workflow construction per application by standardizing the radiomics workflow in modular components, including a large collection of algorithms for each component, and formulating a combined algorithm selection and hyperparameter optimization problem. To solve it, we employ automated machine learning through two strategies (random search and Bayesian optimization) and three ensembling approaches. Results show that a medium-sized random search and straight-forward ensembling perform similar to more advanced methods while being more efficient. Validated across twelve clinical applications, our approach outperforms both a radiomics baseline and human experts. Concluding, our framework improves and streamlines radiomics research by fully automatically optimizing radiomics workflow construction. To facilitate reproducibility, we publicly release six datasets, software of the method, and code to reproduce this study.
title	An automated machine learning framework to optimize radiomics model construction validated on twelve clinical applications
topic	Image and Video Processing Computer Vision and Pattern Recognition
url	https://arxiv.org/abs/2108.08618