{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:11:48Z","timestamp":1760145108900,"version":"build-2065373602"},"reference-count":22,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,6,19]],"date-time":"2024-06-19T00:00:00Z","timestamp":1718755200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Background: After breast conserving surgery (BCS), surgical clips indicate the tumor bed and, thereby, the most probable area for tumor relapse. The aim of this study was to investigate whether a U-Net-based deep convolutional neural network (dCNN) may be used to detect surgical clips in follow-up mammograms after BCS. Methods: 884 mammograms and 517 tomosynthetic images depicting surgical clips and calcifications were manually segmented and classified. A U-Net-based segmentation network was trained with 922 images and validated with 394 images. An external test dataset consisting of 39 images was annotated by two radiologists with up to 7 years of experience in breast imaging. The network\u2019s performance was compared to that of human readers using accuracy and interrater agreement (Cohen\u2019s Kappa). Results: The overall classification accuracy on the validation set after 45 epochs ranged between 88.2% and 92.6%, indicating that the model\u2019s performance is comparable to the decisions of a human reader. In 17.4% of cases, calcifications have been misclassified as post-operative clips. The interrater reliability of the model compared to the radiologists showed substantial agreement (\u03bareader1 = 0.72, \u03bareader2 = 0.78) while the readers compared to each other revealed a Cohen\u2019s Kappa of 0.84, thus showing near-perfect agreement. Conclusions: With this study, we show that surgery clips can adequately be identified by an AI technique. A potential application of the proposed technique is patient triage as well as the automatic exclusion of post-operative cases from PGMI (Perfect, Good, Moderate, Inadequate) evaluation, thus improving the quality management workflow.<\/jats:p>","DOI":"10.3390\/jimaging10060147","type":"journal-article","created":{"date-parts":[[2024,6,19]],"date-time":"2024-06-19T04:21:28Z","timestamp":1718770888000},"page":"147","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Automatic Detection of Post-Operative Clips in Mammography Using a U-Net Convolutional Neural Network"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7278-1406","authenticated-orcid":false,"given":"Tician","family":"Schnitzler","sequence":"first","affiliation":[{"name":"Institute of Radiology, Cantonal Hospital Aarau, 5001 Aarau, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4360-822X","authenticated-orcid":false,"given":"Carlotta","family":"Ruppert","sequence":"additional","affiliation":[{"name":"Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, 8091 Zurich, Switzerland"}]},{"given":"Patryk","family":"Hejduk","sequence":"additional","affiliation":[{"name":"Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, 8091 Zurich, Switzerland"}]},{"given":"Karol","family":"Borkowski","sequence":"additional","affiliation":[{"name":"Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, 8091 Zurich, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6721-3481","authenticated-orcid":false,"given":"Jonas","family":"Kaj\u00fcter","sequence":"additional","affiliation":[{"name":"Institute of Diagnostic and Interventional Radiology, University Hospital Basel, 4031 Basel, Switzerland"}]},{"given":"Cristina","family":"Rossi","sequence":"additional","affiliation":[{"name":"Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, 8091 Zurich, Switzerland"}]},{"given":"Alexander","family":"Ciritsis","sequence":"additional","affiliation":[{"name":"Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, 8091 Zurich, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8240-5797","authenticated-orcid":false,"given":"Anna","family":"Landsmann","sequence":"additional","affiliation":[{"name":"Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, 8091 Zurich, Switzerland"}]},{"given":"Hasan","family":"Zaytoun","sequence":"additional","affiliation":[{"name":"Institute of Radiology, Cantonal Hospital Aarau, 5001 Aarau, Switzerland"}]},{"given":"Andreas","family":"Boss","sequence":"additional","affiliation":[{"name":"Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, 8091 Zurich, Switzerland"}]},{"given":"Sebastian","family":"Schindera","sequence":"additional","affiliation":[{"name":"Institute of Radiology, Cantonal Hospital Aarau, 5001 Aarau, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4362-162X","authenticated-orcid":false,"given":"Felice","family":"Burn","sequence":"additional","affiliation":[{"name":"Institute of Radiology, Cantonal Hospital Aarau, 5001 Aarau, Switzerland"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5748","DOI":"10.18632\/aging.202502","article-title":"Global incidence and mortality of breast cancer: A trend analysis","volume":"13","author":"Huang","year":"2021","journal-title":"Aging"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1002\/ijc.33588","article-title":"Cancer statistics for the year 2020: An overview","volume":"149","author":"Ferlay","year":"2021","journal-title":"Int. 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