{"entity": "researcher", "timestamp": "2026-08-23T09:27:21.818Z", "family": "Salim", "given": "Mattie", "initials": "M", "orcid": "0000-0003-3239-1803", "affiliations": ["Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden.", "Breast Radiology Unit, Karolinska University Hospital, Stockholm, Sweden."], "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/researcher/cf2455f107f0432d890c7eff3b04363a.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/researcher/cf2455f107f0432d890c7eff3b04363a"}}, "publications": [{"entity": "publication", "iuid": "b31f23dbf42e4cbe8d36eabc87f99fb9", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/b31f23dbf42e4cbe8d36eabc87f99fb9.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/b31f23dbf42e4cbe8d36eabc87f99fb9"}}, "title": "AI-based selection of individuals for supplemental MRI in population-based breast cancer screening: the randomized ScreenTrustMRI trial.", "authors": [{"family": "Salim", "given": "Mattie", "initials": "M", "orcid": "0000-0003-3239-1803", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/cf2455f107f0432d890c7eff3b04363a.json"}}, {"family": "Liu", "given": "Yue", "initials": "Y"}, {"family": "Sorkhei", "given": "Moein", "initials": "M", "orcid": "0000-0001-6204-0778", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/5caf1677ad4c418d9d276e2a3708ea75.json"}}, {"family": "Ntoula", "given": "Dimitra", "initials": "D"}, {"family": "Foukakis", "given": "Theodoros", "initials": "T", "orcid": "0000-0001-8952-9987", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/c8d949ec9ece444da6676f68f062c787.json"}}, {"family": "Fredriksson", "given": "Irma", "initials": "I"}, {"family": "Wang", "given": "Yanlu", "initials": "Y"}, {"family": "Eklund", "given": "Martin", "initials": "M", "orcid": "0000-0001-5032-5266", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/06721510ebc8462386e0e6fc6c84315b.json"}}, {"family": "Azizpour", "given": "Hossein", "initials": "H"}, {"family": "Smith", "given": "Kevin", "initials": "K"}, {"family": "Strand", "given": "Fredrik", "initials": "F", "orcid": "0000-0003-3910-7086", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2d5c9e675b9843e5a303d72ae7458449.json"}}], "type": "journal article", "published": "2024-09-00", "journal": {"title": "Nat. Med.", "issn": "1546-170X", "volume": "30", "issue": "9", "pages": "2623-2630", "issn-l": "1078-8956"}, "abstract": "Screening mammography reduces breast cancer mortality, but studies analyzing interval cancers diagnosed after negative screens have shown that many cancers are missed. Supplemental screening using magnetic resonance imaging (MRI) can reduce the number of missed cancers. However, as qualified MRI staff are lacking, the equipment is expensive to purchase and cost-effectiveness for screening may not be convincing, the utilization of MRI is currently limited. An effective method for triaging individuals to supplemental MRI screening is therefore needed. We conducted a randomized clinical trial, ScreenTrustMRI, using a recently developed artificial intelligence (AI) tool to score each mammogram. We offered trial participation to individuals with a negative screening mammogram and a high AI score (top 6.9%). Upon agreeing to participate, individuals were assigned randomly to one of two groups: those receiving supplemental MRI and those not receiving MRI. The primary endpoint of ScreenTrustMRI is advanced breast cancer defined as either interval cancer, invasive component larger than 15 mm or lymph node positive cancer, based on a 27-month follow-up time from the initial screening. Secondary endpoints, prespecified in the study protocol to be reported before the primary outcome, include cancer detected by supplemental MRI, which is the focus of the current paper. Compared with traditional breast density measures used in a previous clinical trial, the current AI method was nearly four times more efficient in terms of cancers detected per 1,000 MRI examinations (64 versus 16.5). Most additional cancers detected were invasive and several were multifocal, suggesting that their detection was timely. Altogether, our results show that using an AI-based score to select a small proportion (6.9%) of individuals for supplemental MRI after negative mammography detects many missed cancers, making the cost per cancer detected comparable with screening mammography. ClinicalTrials.gov registration: NCT04832594 .", "doi": "10.1038/s41591-024-03093-5", "pmid": "38977914", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC11405258"}, {"db": "pii", "key": "10.1038/s41591-024-03093-5"}, {"db": "ClinicalTrials.gov", "key": "NCT04832594"}], "notes": [], "created": "2026-08-20T09:02:00.392Z", "modified": "2026-08-20T09:02:00.629Z"}, {"entity": "publication", "iuid": "c85c05ff2cde4090ac3bd674ec2b2781", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/c85c05ff2cde4090ac3bd674ec2b2781.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/c85c05ff2cde4090ac3bd674ec2b2781"}}, "title": "Differences and similarities in false interpretations by AI CAD and radiologists in screening mammography.", "authors": [{"family": "Salim", "given": "Mattie", "initials": "M", "orcid": "0000-0003-3239-1803", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/cf2455f107f0432d890c7eff3b04363a.json"}}, {"family": "Dembrower", "given": "Karin", "initials": "K"}, {"family": "Eklund", "given": "Martin", "initials": "M"}, {"family": "Smith", "given": "Kevin", "initials": "K"}, {"family": "Strand", "given": "Fredrik", "initials": "F"}], "type": "journal article", "published": "2023-11-00", "journal": {"title": "Br J Radiol", "issn": "1748-880X", "volume": "96", "issue": "1151", "pages": "20230210", "issn-l": null}, "abstract": "We aimed to evaluate the false interpretations between artificial intelligence (AI) and radiologists in screening mammography to get a better understanding of how the distribution of diagnostic mistakes might change when moving from entirely radiologist-driven to AI-integrated breast cancer screening.\n\nThis retrospective case-control study was based on a mammography screening cohort from 2008 to 2015. The final study population included screening examinations for 714 women diagnosed with breast cancer and 8029 randomly selected healthy controls. Oversampling of controls was applied to attain a similar cancer proportion as in the source screening cohort. We examined how false-positive (FP) and false-negative (FN) assessments by AI, the first reader (RAD 1) and the second reader (RAD 2), were associated with age, density, tumor histology and cancer invasiveness in a single- and double-reader setting.\n\nFor each reader, the FN assessments were distributed between low- and high-density females with 53 (42%) and 72 (58%) for AI; 59 (36%) and 104 (64%) for RAD 1 and 47 (36%) and 84 (64%) for RAD 2. The corresponding numbers for FP assessments were 1820 (47%) and 2016 (53%) for AI; 1568 (46%) and 1834 (54%) for RAD 1 and 1190 (43%) and 1610 (58%) for RAD 2. For ductal cancer, the FN assessments were 79 (77%) for AI CAD; with 120 (83%) for RAD 1 and with 96 (16%) for RAD 2. For the double-reading simulation, the FP assessments were distributed between younger and older females with 2828 (2.5%) and 1554 (1.4%) for RAD 1 + RAD 2; 3850 (3.4%) and 2940 (2.6%) for AI+RAD 1 and 3430 (3%) and 2772 (2.5%) for AI+RAD 2.\n\nThe most pronounced decrease in FN assessments was noted for females over the age of 55 and for high density-women. In conclusion, AI could have an important complementary role when combined with radiologists to increase sensitivity for high-density and older females.\n\nOur results highlight the potential impact of integrating AI in breast cancer screening, particularly to improve interpretation accuracy. The use of AI could enhance screening outcomes for high-density and older females.", "doi": "10.1259/bjr.20230210", "pmid": "37660400", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC10607417"}], "notes": [], "created": "2026-08-20T12:38:35.215Z", "modified": "2026-08-20T12:38:35.301Z"}]}