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| Fältnamn | Indikatorer | Metadata |
|---|---|---|
| 000 | 06007naa a2200457 4500 | |
| 001 | oai:lup.lub.lu.se:2a4f6af7-29e5-4227-85ee-d2b4bfa62bdb | |
| 003 | SwePub | |
| 008 | 230831s2023 | |||||||||||000 ||eng| | |
| 024 | 7 | a https://lup.lub.lu.se/record/2a4f6af7-29e5-4227-85ee-d2b4bfa62bdb2 URI |
| 024 | 7 | a https://doi.org/10.1007/s00330-022-09316-y2 DOI |
| 040 | a (SwePub)lu | |
| 041 | a engb eng | |
| 042 | 9 SwePub | |
| 072 | 7 | a art2 swepub-publicationtype |
| 072 | 7 | a ref2 swepub-contenttype |
| 100 | 1 | a Dahlblom, Victoru Lund University,Lunds universitet,Diagnostisk radiologi, Malmö,Forskargrupper vid Lunds universitet,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Radiology Diagnostics, Malmö,Lund University Research Groups,LUCC: Lund University Cancer Centre,Other Strong Research Environments,Skåne University Hospital4 aut0 (Swepub:lu)vi3480da |
| 245 | 1 0 | a Breast cancer screening with digital breast tomosynthesis : comparison of different reading strategies implementing artificial intelligence |
| 264 | c 2022-12-11 | |
| 264 | 1 | b Springer Science and Business Media LLC,c 2023 |
| 520 | a OBJECTIVES: Digital breast tomosynthesis (DBT) can detect more cancers than the current standard breast screening method, digital mammography (DM); however, it can substantially increase the reading workload and thus hinder implementation in screening. Artificial intelligence (AI) might be a solution. The aim of this study was to retrospectively test different ways of using AI in a screening workflow.METHODS: An AI system was used to analyse 14,772 double-read single-view DBT examinations from a screening trial with paired DM double reading. Three scenarios were studied: if AI can identify normal cases that can be excluded from human reading; if AI can replace the second reader; if AI can replace both readers. The number of detected cancers and false positives was compared with DM or DBT double reading.RESULTS: By excluding normal cases and only reading 50.5% (7460/14,772) of all examinations, 95% (121/127) of the DBT double reading detected cancers could be detected. Compared to DM screening, 27% (26/95) more cancers could be detected (p < 0.001) while keeping recall rates at the same level. With AI replacing the second reader, 95% (120/127) of the DBT double reading detected cancers could be detected-26% (25/95) more than DM screening (p < 0.001)-while increasing recall rates by 53%. AI alone with DBT has a sensitivity similar to DM double reading (p = 0.689).CONCLUSION: AI can open up possibilities for implementing DBT screening and detecting more cancers with the total reading workload unchanged. Considering the potential legal and psychological implications, replacing the second reader with AI would probably be most the feasible approach.KEY POINTS: • Breast cancer screening with digital breast tomosynthesis and artificial intelligence can detect more cancers than mammography screening without increasing screen-reading workload. • Artificial intelligence can either exclude low-risk cases from double reading or replace the second reader. • Retrospective study based on paired mammography and digital breast tomosynthesis screening data. | |
| 650 | 7 | a MEDICIN OCH HÄLSOVETENSKAPx Klinisk medicinx Radiologi och bildbehandling0 (SwePub)302082 hsv//swe |
| 650 | 7 | a MEDICAL AND HEALTH SCIENCESx Clinical Medicinex Radiology, Nuclear Medicine and Medical Imaging0 (SwePub)302082 hsv//eng |
| 650 | 7 | a MEDICIN OCH HÄLSOVETENSKAPx Klinisk medicinx Cancer och onkologi0 (SwePub)302032 hsv//swe |
| 650 | 7 | a MEDICAL AND HEALTH SCIENCESx Clinical Medicinex Cancer and Oncology0 (SwePub)302032 hsv//eng |
| 653 | a Humans | |
| 653 | a Female | |
| 653 | a Breast Neoplasms/diagnosis | |
| 653 | a Retrospective Studies | |
| 653 | a Artificial Intelligence | |
| 653 | a Early Detection of Cancer/methods | |
| 653 | a Breast/diagnostic imaging | |
| 653 | a Mammography/methods | |
| 653 | a Mass Screening/methods | |
| 700 | 1 | a Dustler, Magnusu Lund University,Lunds universitet,Diagnostisk radiologi, Malmö,Forskargrupper vid Lunds universitet,Medicinsk strålningsfysik, Malmö,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Radiology Diagnostics, Malmö,Lund University Research Groups,Medical Radiation Physics, Malmö,LUCC: Lund University Cancer Centre,Other Strong Research Environments4 aut0 (Swepub:lu)med-mdu |
| 700 | 1 | a Tingberg, Andersu Lund University,Lunds universitet,Medicinsk strålningsfysik, Malmö,Forskargrupper vid Lunds universitet,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Medical Radiation Physics, Malmö,Lund University Research Groups,LUCC: Lund University Cancer Centre,Other Strong Research Environments,Skåne University Hospital4 aut0 (Swepub:lu)rfa-ati |
| 700 | 1 | a Zackrisson, Sophiau Lund University,Lunds universitet,Diagnostisk radiologi, Malmö,Forskargrupper vid Lunds universitet,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,LTH profilområde: Avancerade ljuskällor,LTH profilområden,Lunds Tekniska Högskola,LU profilområde: Ljus och material,Lunds universitets profilområden,Radiology Diagnostics, Malmö,Lund University Research Groups,LUCC: Lund University Cancer Centre,Other Strong Research Environments,LTH Profile Area: Photon Science and Technology,LTH Profile areas,Faculty of Engineering, LTH,LU Profile Area: Light and Materials,Lund University Profile areas,Skåne University Hospital4 aut0 (Swepub:lu)smi-sza |
| 710 | 2 | a Diagnostisk radiologi, Malmöb Forskargrupper vid Lunds universitet4 org |
| 773 | 0 | t European Radiologyd : Springer Science and Business Media LLCg 33:5, s. 3754-3765q 33:5<3754-3765x 0938-7994x 1432-1084 |
| 856 | 4 | u http://dx.doi.org/10.1007/s00330-022-09316-yx freey FULLTEXT |
| 856 | 4 8 | u https://lup.lub.lu.se/record/2a4f6af7-29e5-4227-85ee-d2b4bfa62bdb |
| 856 | 4 8 | u https://doi.org/10.1007/s00330-022-09316-y |
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