{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Zeleznik R"],"funding":["American Heart Association","Deutsche Forschungsgemeinschaft","Deutsche Forschungsgemeinschaft (German Research Foundation)","NHLBI NIH HHS","NCI NIH HHS","U.S. Department of Health & Human Services | National Institutes of Health (NIH)","U.S. Department of Health &amp; Human Services | National Institutes of Health","American Heart Association (American Heart Association, Inc.)"],"pagination":["43"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC7935874"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["4(1)"],"pubmed_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."],"journal":["NPJ digital medicine"],"pubmed_title":["Deep-learning system to improve the quality and efficiency of volumetric heart segmentation for breast cancer."],"pmcid":["PMC7935874"],"funding_grant_id":["R01 HL109711","T32 HL076136","WE 6405/2-1","U01 CA190234","U01 CA209414","U01 HL123339","TA 1438/1-2","NIH/NHLBI 5U01HL123339","NIH 5R01-HL109711","U24 CA194354","NIH/NHLBI 5K24HL113128","18UNPG34030172","NIH/NHLBI 5T32HL076136","K24 HL113128"],"pubmed_authors":["Taron J","Hoffmann U","Bitterman DS","Mak R","Punglia RS","Kann BH","Aerts HJWL","Bredfeldt J","Weiss J","Guthier C","Kim DW","Hancox C","Foldyna B","Zeleznik R","Lu MT","Eslami P"],"additional_accession":[]},"is_claimable":false,"name":"Deep-learning system to improve the quality and efficiency of volumetric heart segmentation for breast cancer.","description":"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.","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Mar","modification":"2025-04-18T13:28:44.005Z","creation":"2025-04-06T23:15:19.083Z"},"accession":"S-EPMC7935874","cross_references":{"pubmed":["33674717"],"doi":["10.1038/s41746-021-00416-5"]}}