Stefano Pedemonte, Trevor Tsue, Brent Mombourquette, Yen Nhi Truong Vu, Thomas Matthews, Rodrigo Morales Hoil, Meet Shah, Nikita Ghare, Naomi Zingman-Daniels, Susan Holley, Catherine M Appleton, Jason Su, Richard L Wahl
Purpose To evaluate the ability of a semiautonomous artificial intelligence (AI) model to identify screening mammograms not suspicious for breast cancer and reduce the number of false-positive examinations. Materials and Methods The deep learning algorithm was trained using 123 248 two-dimensional digital mammograms (6161 cancers) and a retrospective study was performed on three nonoverlapping datasets of 14 831 screening mammography examinations (1026 cancers) from two U.S. institutions and one U.K. institution (2008-2017). The stand-alone performance of humans and AI was compared. Human plus AI performance was simulated to examine reductions in the cancer detection rate, number of examinations, false-positive callbacks, and benign biopsies. Metrics were adjusted to mimic the natural distribution of a screening population, and bootstrapped CIs and P values were calculated. Results Retrospective evaluation on all datasets showed minimal changes to the cancer detection rate with use of the AI device (noninferiority margin of 0.25 cancers per 1000 examinations: U.S. dataset 1, P = .02; U.S. dataset 2, P < .001; U.K. dataset, P < .001). On U.S. dataset 1 (11 592 mammograms; 101 cancers; 3810 female patients; mean age, 57.3 years ± 10.0 [SD]), the device reduced screening examinations requiring radiologist interpretation by 41.6% (95% CI: 40.6%, 42.4%; P < .001), diagnostic examinations callbacks by 31.1% (95% CI: 28.7%, 33.4%; P < .001), and benign needle biopsies by 7.4% (95% CI: 4.1%, 12.4%; P < .001). U.S. dataset 2 (1362 mammograms; 330 cancers; 1293 female patients; mean age, 55.4 years ± 10.5) was reduced by 19.5% (95% CI: 16.9%, 22.1%; P < .001), 11.9% (95% CI: 8.6%, 15.7%; P < .001), and 6.5% (95% CI: 0.0%, 19.0%; P = .08), respectively. The U.K. dataset (1877 mammograms; 595 cancers; 1491 female patients; mean age, 63.5 years ± 7.1) was reduced by 36.8% (95% CI: 34.4%, 39.7%; P < .001), 17.1% (95% CI: 5.9%, 30.1%: P < .001), and 5.9% (95% CI: 2.9%, 11.5%; P < .001), respectively. Conclusion This work demonstrates the potential of a semiautonomous breast cancer screening system to reduce false positives, unnecessary procedures, patient anxiety, and medical expenses. Keywords: Artificial Intelligence, Semiautonomous Deep Learning, Breast Cancer, Screening Mammography Supplemental material is available for this article. Published under a CC BY 4.0 license.
{"title":"A Semiautonomous Deep Learning System to Reduce False Positives in Screening Mammography.","authors":"Stefano Pedemonte, Trevor Tsue, Brent Mombourquette, Yen Nhi Truong Vu, Thomas Matthews, Rodrigo Morales Hoil, Meet Shah, Nikita Ghare, Naomi Zingman-Daniels, Susan Holley, Catherine M Appleton, Jason Su, Richard L Wahl","doi":"10.1148/ryai.230033","DOIUrl":"10.1148/ryai.230033","url":null,"abstract":"<p><p>Purpose To evaluate the ability of a semiautonomous artificial intelligence (AI) model to identify screening mammograms not suspicious for breast cancer and reduce the number of false-positive examinations. Materials and Methods The deep learning algorithm was trained using 123 248 two-dimensional digital mammograms (6161 cancers) and a retrospective study was performed on three nonoverlapping datasets of 14 831 screening mammography examinations (1026 cancers) from two U.S. institutions and one U.K. institution (2008-2017). The stand-alone performance of humans and AI was compared. Human plus AI performance was simulated to examine reductions in the cancer detection rate, number of examinations, false-positive callbacks, and benign biopsies. Metrics were adjusted to mimic the natural distribution of a screening population, and bootstrapped CIs and <i>P</i> values were calculated. Results Retrospective evaluation on all datasets showed minimal changes to the cancer detection rate with use of the AI device (noninferiority margin of 0.25 cancers per 1000 examinations: U.S. dataset 1, <i>P</i> = .02; U.S. dataset 2, <i>P</i> < .001; U.K. dataset, <i>P</i> < .001). On U.S. dataset 1 (11 592 mammograms; 101 cancers; 3810 female patients; mean age, 57.3 years ± 10.0 [SD]), the device reduced screening examinations requiring radiologist interpretation by 41.6% (95% CI: 40.6%, 42.4%; <i>P</i> < .001), diagnostic examinations callbacks by 31.1% (95% CI: 28.7%, 33.4%; <i>P</i> < .001), and benign needle biopsies by 7.4% (95% CI: 4.1%, 12.4%; <i>P</i> < .001). U.S. dataset 2 (1362 mammograms; 330 cancers; 1293 female patients; mean age, 55.4 years ± 10.5) was reduced by 19.5% (95% CI: 16.9%, 22.1%; <i>P</i> < .001), 11.9% (95% CI: 8.6%, 15.7%; <i>P</i> < .001), and 6.5% (95% CI: 0.0%, 19.0%; <i>P</i> = .08), respectively. The U.K. dataset (1877 mammograms; 595 cancers; 1491 female patients; mean age, 63.5 years ± 7.1) was reduced by 36.8% (95% CI: 34.4%, 39.7%; <i>P</i> < .001), 17.1% (95% CI: 5.9%, 30.1%: <i>P</i> < .001), and 5.9% (95% CI: 2.9%, 11.5%; <i>P</i> < .001), respectively. Conclusion This work demonstrates the potential of a semiautonomous breast cancer screening system to reduce false positives, unnecessary procedures, patient anxiety, and medical expenses. <b>Keywords:</b> Artificial Intelligence, Semiautonomous Deep Learning, Breast Cancer, Screening Mammography <i>Supplemental material is available for this article.</i> Published under a CC BY 4.0 license.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":9.8,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11140506/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140872447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ghee Rye Lee, Adam E Flanders, Tyler Richards, Felipe Kitamura, Errol Colak, Hui Ming Lin, Robyn L Ball, Jason Talbott, Luciano M Prevedello
{"title":"Erratum for: Performance of the Winning Algorithms of the RSNA 2022 Cervical Spine Fracture Detection Challenge.","authors":"Ghee Rye Lee, Adam E Flanders, Tyler Richards, Felipe Kitamura, Errol Colak, Hui Ming Lin, Robyn L Ball, Jason Talbott, Luciano M Prevedello","doi":"10.1148/ryai.249002","DOIUrl":"10.1148/ryai.249002","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":9.8,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11140496/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140865979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Artificial Intelligence for Breast Cancer Screening: Trade-offs between Sensitivity and Specificity.","authors":"Manisha Bahl, Synho Do","doi":"10.1148/ryai.240184","DOIUrl":"10.1148/ryai.240184","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":9.8,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11140497/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140877471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Divyanshu Tak, Zezhong Ye, Anna Zapaischykova, Yining Zha, Aidan Boyd, Sridhar Vajapeyam, Rishi Chopra, Hasaan Hayat, Sanjay P Prabhu, Kevin X Liu, Hesham Elhalawani, Ali Nabavizadeh, Ariana Familiar, Adam C Resnick, Sabine Mueller, Hugo J W L Aerts, Pratiti Bandopadhayay, Keith L Ligon, Daphne A Haas-Kogan, Tina Y Poussaint, Benjamin H Kann
Felipe C Kitamura, Luciano M Prevedello, Errol Colak, Safwan S Halabi, Matthew P Lungren, Robyn L Ball, Jayashree Kalpathy-Cramer, Charles E Kahn, Tyler Richards, Jason F Talbott, George Shih, Hui Ming Lin, Katherine P Andriole, Maryam Vazirabad, Bradley J Erickson, Adam E Flanders, John Mongan