{"entity": "researcher", "timestamp": "2026-08-23T09:27:40.557Z", "family": "Strand", "given": "Fredrik", "initials": "F", "orcid": "0000-0003-3910-7086", "affiliations": ["Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden. fredrik.strand@ki.se.", "Breast Radiology Unit, Karolinska University Hospital, Stockholm, Sweden. fredrik.strand@ki.se."], "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/researcher/2d5c9e675b9843e5a303d72ae7458449.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/researcher/2d5c9e675b9843e5a303d72ae7458449"}}, "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": "0de508f72c53467d9cd1cbfe0100a9f7", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/0de508f72c53467d9cd1cbfe0100a9f7.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/0de508f72c53467d9cd1cbfe0100a9f7"}}, "title": "Use of an AI Score Combining Cancer Signs, Masking, and Risk to Select Patients for Supplemental Breast Cancer Screening.", "authors": [{"family": "Liu", "given": "Yue", "initials": "Y", "orcid": "0000-0003-0101-1505", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/fc08ab4da20443e4a5cc557ca47ca6e5.json"}}, {"family": "Sorkhei", "given": "Moein", "initials": "M", "orcid": "0000-0001-6204-0778", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/5caf1677ad4c418d9d276e2a3708ea75.json"}}, {"family": "Dembrower", "given": "Karin", "initials": "K", "orcid": "0000-0001-5966-0749", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/6c2749569a2c4c648359bbe29ac7d407.json"}}, {"family": "Azizpour", "given": "Hossein", "initials": "H", "orcid": "0000-0001-5211-6388", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/71822b91ee9f404e95c80faa39560b40.json"}}, {"family": "Strand", "given": "Fredrik", "initials": "F", "orcid": "0000-0003-3910-7086", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2d5c9e675b9843e5a303d72ae7458449.json"}}, {"family": "Smith", "given": "Kevin", "initials": "K", "orcid": "0000-0002-6163-191X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/bbde655f55444523be5416c6b275ea7a.json"}}], "type": "journal article", "published": "2024-04-00", "journal": {"title": "Radiology", "issn": "1527-1315", "volume": "311", "issue": "1", "pages": "e232535", "issn-l": null}, "abstract": "Background Mammographic density measurements are used to identify patients who should undergo supplemental imaging for breast cancer detection, but artificial intelligence (AI) image analysis may be more effective. Purpose To assess whether AISmartDensity-an AI-based score integrating cancer signs, masking, and risk-surpasses measurements of mammographic density in identifying patients for supplemental breast imaging after a negative screening mammogram. Materials and Methods This retrospective study included randomly selected individuals who underwent screening mammography at Karolinska University Hospital between January 2008 and December 2015. The models in AISmartDensity were trained and validated using nonoverlapping data. The ability of AISmartDensity to identify future cancer in patients with a negative screening mammogram was evaluated and compared with that of mammographic density models. Sensitivity and positive predictive value (PPV) were calculated for the top 8% of scores, mimicking the proportion of patients in the Breast Imaging Reporting and Data System \"extremely dense\" category. Model performance was evaluated using area under the receiver operating characteristic curve (AUC) and was compared using the DeLong test. Results The study population included 65 325 examinations (median patient age, 53 years [IQR, 47-62 years])-64 870 examinations in healthy patients and 455 examinations in patients with breast cancer diagnosed within 3 years of a negative screening mammogram. The AUC for detecting subsequent cancers was 0.72 and 0.61 (P < .001) for AISmartDensity and the best-performing density model (age-adjusted dense area), respectively. For examinations with scores in the top 8%, AISmartDensity identified 152 of 455 (33%) future cancers with a PPV of 2.91%, whereas the best-performing density model (age-adjusted dense area) identified 57 of 455 (13%) future cancers with a PPV of 1.09% (P < .001). AISmartDensity identified 32% (41 of 130) and 34% (111 of 325) of interval and next-round screen-detected cancers, whereas the best-performing density model (dense area) identified 16% (21 of 130) and 9% (30 of 325), respectively. Conclusion AISmartDensity, integrating cancer signs, masking, and risk, outperformed traditional density models in identifying patients for supplemental imaging after a negative screening mammogram. \u00a9 RSNA, 2024 Supplemental material is available for this article. See also the editorial by Kim and Chang in this issue.", "doi": "10.1148/radiol.232535", "pmid": "38591971", "labels": [], "xrefs": [], "notes": [], "created": "2026-08-20T12:03:03.370Z", "modified": "2026-08-20T12:03:03.501Z"}, {"entity": "publication", "iuid": "469f91a946fd428586cb7085ad892a93", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/469f91a946fd428586cb7085ad892a93.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/469f91a946fd428586cb7085ad892a93"}}, "title": "Toward robust mammography-based models for breast cancer risk.", "authors": [{"family": "Yala", "given": "Adam", "initials": "A", "orcid": "0000-0001-9576-2590", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/1634f0fe3a4948b4aeaf8c094c53aa67.json"}}, {"family": "Mikhael", "given": "Peter G", "initials": "PG", "orcid": "0000-0002-6030-1636", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/d5f53fdee68a49d987545d38fd093760.json"}}, {"family": "Strand", "given": "Fredrik", "initials": "F", "orcid": "0000-0003-3910-7086", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2d5c9e675b9843e5a303d72ae7458449.json"}}, {"family": "Lin", "given": "Gigin", "initials": "G", "orcid": "0000-0001-7246-1058", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/46e2c57cd06f42d6b288b2b8316e438f.json"}}, {"family": "Smith", "given": "Kevin", "initials": "K"}, {"family": "Wan", "given": "Yung-Liang", "initials": "YL", "orcid": "0000-0002-3039-996X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/1299fdc1431f42d5972f079933e1a14e.json"}}, {"family": "Lamb", "given": "Leslie", "initials": "L"}, {"family": "Hughes", "given": "Kevin", "initials": "K"}, {"family": "Lehman", "given": "Constance", "initials": "C", "orcid": "0000-0001-5839-6675", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f7bd037da9a54093abccdafcce95b6f8.json"}}, {"family": "Barzilay", "given": "Regina", "initials": "R", "orcid": "0000-0002-2921-8201", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/22680dc1d90a4ccfab05cc7de97a5f95.json"}}], "type": "journal article", "published": "2021-01-27", "journal": {"title": "Sci Transl Med", "issn": "1946-6242", "volume": "13", "issue": "578", "issn-l": "1946-6234"}, "abstract": "Improved breast cancer risk models enable targeted screening strategies that achieve earlier detection and less screening harm than existing guidelines. To bring deep learning risk models to clinical practice, we need to further refine their accuracy, validate them across diverse populations, and demonstrate their potential to improve clinical workflows. We developed Mirai, a mammography-based deep learning model designed to predict risk at multiple timepoints, leverage potentially missing risk factor information, and produce predictions that are consistent across mammography machines. Mirai was trained on a large dataset from Massachusetts General Hospital (MGH) in the United States and tested on held-out test sets from MGH, Karolinska University Hospital in Sweden, and Chang Gung Memorial Hospital (CGMH) in Taiwan, obtaining C-indices of 0.76 (95% confidence interval, 0.74 to 0.80), 0.81 (0.79 to 0.82), and 0.79 (0.79 to 0.83), respectively. Mirai obtained significantly higher 5-year ROC AUCs than the Tyrer-Cuzick model ( < 0.001) and prior deep learning models Hybrid DL ( P < 0.001) and Image-Only DL ( P < 0.001), trained on the same dataset. Mirai more accurately identified high-risk patients than prior methods across all datasets. On the MGH test set, 41.5% (34.4 to 48.5) of patients who would develop cancer within 5 years were identified as high risk, compared with 36.1% (29.1 to 42.9) by Hybrid DL ( P = 0.02) and 22.9% (15.9 to 29.6) by the Tyrer-Cuzick model ( P < 0.001).P", "doi": "10.1126/scitranslmed.aba4373", "pmid": "33504648", "labels": [], "xrefs": [{"db": "pii", "key": "13/578/eaba4373"}], "notes": [], "created": "2026-08-20T11:59:47.135Z", "modified": "2026-08-20T11:59:47.455Z"}, {"entity": "publication", "iuid": "80bfae74d4554cd2a1d6ff0c3052fcd4", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/80bfae74d4554cd2a1d6ff0c3052fcd4.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/80bfae74d4554cd2a1d6ff0c3052fcd4"}}, "title": "Comparison of a Deep Learning Risk Score and Standard Mammographic Density Score for Breast Cancer Risk Prediction.", "authors": [{"family": "Dembrower", "given": "Karin", "initials": "K", "orcid": "0000-0001-5966-0749", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/6c2749569a2c4c648359bbe29ac7d407.json"}}, {"family": "Liu", "given": "Yue", "initials": "Y"}, {"family": "Azizpour", "given": "Hossein", "initials": "H", "orcid": "0000-0001-5211-6388", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/71822b91ee9f404e95c80faa39560b40.json"}}, {"family": "Eklund", "given": "Martin", "initials": "M", "orcid": "0000-0001-5032-5266", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/06721510ebc8462386e0e6fc6c84315b.json"}}, {"family": "Smith", "given": "Kevin", "initials": "K"}, {"family": "Lindholm", "given": "Peter", "initials": "P"}, {"family": "Strand", "given": "Fredrik", "initials": "F", "orcid": "0000-0003-3910-7086", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2d5c9e675b9843e5a303d72ae7458449.json"}}], "type": "comparative study", "published": "2020-02-00", "journal": {"title": "Radiology", "issn": "1527-1315", "volume": "294", "issue": "2", "pages": "265-272", "issn-l": null}, "abstract": "Background Most risk prediction models for breast cancer are based on questionnaires and mammographic density assessments. By training a deep neural network, further information in the mammographic images can be considered. Purpose To develop a risk score that is associated with future breast cancer and compare it with density-based models. Materials and Methods In this retrospective study, all women aged 40-74 years within the Karolinska University Hospital uptake area in whom breast cancer was diagnosed in 2013-2014 were included along with healthy control subjects. Network development was based on cases diagnosed from 2008 to 2012. The deep learning (DL) risk score, dense area, and percentage density were calculated for the earliest available digital mammographic examination for each woman. Logistic regression models were fitted to determine the association with subsequent breast cancer. False-negative rates were obtained for the DL risk score, age-adjusted dense area, and age-adjusted percentage density. Results A total of 2283 women, 278 of whom were later diagnosed with breast cancer, were evaluated. The age at mammography (mean, 55.7 years vs 54.6 years; P < .001), the dense area (mean, 38.2 cm2 vs 34.2 cm2; P < .001), and the percentage density (mean, 25.6% vs 24.0%; P < .001) were higher among women diagnosed with breast cancer than in those without a breast cancer diagnosis. The odds ratios and areas under the receiver operating characteristic curve (AUCs) were higher for age-adjusted DL risk score than for dense area and percentage density: 1.56 (95% confidence interval [CI]: 1.48, 1.64; AUC, 0.65), 1.31 (95% CI: 1.24, 1.38; AUC, 0.60), and 1.18 (95% CI: 1.11, 1.25; AUC, 0.57), respectively (P < .001 for AUC). The false-negative rate was lower: 31% (95% CI: 29%, 34%), 36% (95% CI: 33%, 39%; P = .006), and 39% (95% CI: 37%, 42%; P < .001); this difference was most pronounced for more aggressive cancers. Conclusion Compared with density-based models, a deep neural network can more accurately predict which women are at risk for future breast cancer, with a lower false-negative rate for more aggressive cancers. \u00a9 RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Bahl in this issue.", "doi": "10.1148/radiol.2019190872", "pmid": "31845842", "labels": [], "xrefs": [], "notes": [], "created": "2026-08-20T12:03:01.468Z", "modified": "2026-08-20T12:03:01.629Z"}]}