Deep-learning system to improve the quality and efficiency of volumetric heart segmentation for breast cancer

Abstract Although artificial intelligence algorithms are often developed and applied for narrow tasks, their implementation in other medical settings could help to improve patient care. Here we assess whether a deep-learning system for volumetric heart segmentation on computed tomography (CT) scans...

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Autores principales:	Roman Zeleznik, Jakob Weiss, Jana Taron, Christian Guthier, Danielle S. Bitterman, Cindy Hancox, Benjamin H. Kann, Daniel W. Kim, Rinaa S. Punglia, Jeremy Bredfeldt, Borek Foldyna, Parastou Eslami, Michael T. Lu, Udo Hoffmann, Raymond Mak, Hugo J. W. L. Aerts
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/8becf93bb4f941d09c80914e23c4335a
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spelling	oai:doaj.org-article:8becf93bb4f941d09c80914e23c4335a2021-12-02T15:54:14ZDeep-learning system to improve the quality and efficiency of volumetric heart segmentation for breast cancer10.1038/s41746-021-00416-52398-6352https://doaj.org/article/8becf93bb4f941d09c80914e23c4335a2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41746-021-00416-5https://doaj.org/toc/2398-6352Abstract Although artificial intelligence algorithms are often developed and applied for narrow tasks, their implementation in other medical settings could help to improve patient care. Here we assess whether a deep-learning system for volumetric heart segmentation on computed tomography (CT) scans developed in cardiovascular radiology can optimize treatment planning in radiation oncology. The system was trained using multi-center data (n = 858) with manual heart segmentations provided by cardiovascular radiologists. Validation of the system was performed in an independent real-world dataset of 5677 breast cancer patients treated with radiation therapy at the Dana-Farber/Brigham and Women’s Cancer Center between 2008–2018. In a subset of 20 patients, the performance of the system was compared to eight radiation oncology experts by assessing segmentation time, agreement between experts, and accuracy with and without deep-learning assistance. To compare the performance to segmentations used in the clinic, concordance and failures (defined as Dice < 0.85) of the system were evaluated in the entire dataset. The system was successfully applied without retraining. With deep-learning assistance, segmentation time significantly decreased (4.0 min [IQR 3.1–5.0] vs. 2.0 min [IQR 1.3–3.5]; p < 0.001), and agreement increased (Dice 0.95 [IQR = 0.02]; vs. 0.97 [IQR = 0.02], p < 0.001). Expert accuracy was similar with and without deep-learning assistance (Dice 0.92 [IQR = 0.02] vs. 0.92 [IQR = 0.02]; p = 0.48), and not significantly different from deep-learning-only segmentations (Dice 0.92 [IQR = 0.02]; p ≥ 0.1). In comparison to real-world data, the system showed high concordance (Dice 0.89 [IQR = 0.06]) across 5677 patients and a significantly lower failure rate (p < 0.001). These results suggest that deep-learning algorithms can successfully be applied across medical specialties and improve clinical care beyond the original field of interest.Roman ZeleznikJakob WeissJana TaronChristian GuthierDanielle S. BittermanCindy HancoxBenjamin H. KannDaniel W. KimRinaa S. PungliaJeremy BredfeldtBorek FoldynaParastou EslamiMichael T. LuUdo HoffmannRaymond MakHugo J. W. L. AertsNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Roman Zeleznik Jakob Weiss Jana Taron Christian Guthier Danielle S. Bitterman Cindy Hancox Benjamin H. Kann Daniel W. Kim Rinaa S. Punglia Jeremy Bredfeldt Borek Foldyna Parastou Eslami Michael T. Lu Udo Hoffmann Raymond Mak Hugo J. W. L. Aerts Deep-learning system to improve the quality and efficiency of volumetric heart segmentation for breast cancer
description	Abstract Although artificial intelligence algorithms are often developed and applied for narrow tasks, their implementation in other medical settings could help to improve patient care. Here we assess whether a deep-learning system for volumetric heart segmentation on computed tomography (CT) scans developed in cardiovascular radiology can optimize treatment planning in radiation oncology. The system was trained using multi-center data (n = 858) with manual heart segmentations provided by cardiovascular radiologists. Validation of the system was performed in an independent real-world dataset of 5677 breast cancer patients treated with radiation therapy at the Dana-Farber/Brigham and Women’s Cancer Center between 2008–2018. In a subset of 20 patients, the performance of the system was compared to eight radiation oncology experts by assessing segmentation time, agreement between experts, and accuracy with and without deep-learning assistance. To compare the performance to segmentations used in the clinic, concordance and failures (defined as Dice < 0.85) of the system were evaluated in the entire dataset. The system was successfully applied without retraining. With deep-learning assistance, segmentation time significantly decreased (4.0 min [IQR 3.1–5.0] vs. 2.0 min [IQR 1.3–3.5]; p < 0.001), and agreement increased (Dice 0.95 [IQR = 0.02]; vs. 0.97 [IQR = 0.02], p < 0.001). Expert accuracy was similar with and without deep-learning assistance (Dice 0.92 [IQR = 0.02] vs. 0.92 [IQR = 0.02]; p = 0.48), and not significantly different from deep-learning-only segmentations (Dice 0.92 [IQR = 0.02]; p ≥ 0.1). In comparison to real-world data, the system showed high concordance (Dice 0.89 [IQR = 0.06]) across 5677 patients and a significantly lower failure rate (p < 0.001). These results suggest that deep-learning algorithms can successfully be applied across medical specialties and improve clinical care beyond the original field of interest.
format	article
author	Roman Zeleznik Jakob Weiss Jana Taron Christian Guthier Danielle S. Bitterman Cindy Hancox Benjamin H. Kann Daniel W. Kim Rinaa S. Punglia Jeremy Bredfeldt Borek Foldyna Parastou Eslami Michael T. Lu Udo Hoffmann Raymond Mak Hugo J. W. L. Aerts
author_facet	Roman Zeleznik Jakob Weiss Jana Taron Christian Guthier Danielle S. Bitterman Cindy Hancox Benjamin H. Kann Daniel W. Kim Rinaa S. Punglia Jeremy Bredfeldt Borek Foldyna Parastou Eslami Michael T. Lu Udo Hoffmann Raymond Mak Hugo J. W. L. Aerts
author_sort	Roman Zeleznik
title	Deep-learning system to improve the quality and efficiency of volumetric heart segmentation for breast cancer
title_short	Deep-learning system to improve the quality and efficiency of volumetric heart segmentation for breast cancer
title_full	Deep-learning system to improve the quality and efficiency of volumetric heart segmentation for breast cancer
title_fullStr	Deep-learning system to improve the quality and efficiency of volumetric heart segmentation for breast cancer
title_full_unstemmed	Deep-learning system to improve the quality and efficiency of volumetric heart segmentation for breast cancer
title_sort	deep-learning system to improve the quality and efficiency of volumetric heart segmentation for breast cancer
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/8becf93bb4f941d09c80914e23c4335a
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Deep-learning system to improve the quality and efficiency of volumetric heart segmentation for breast cancer

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