Fredrik Strand is a Swedish breast radiologist and associate professor at Karolinska Institutet, Stockholm. He holds MD, PhD and MSc(Eng) degrees. He is head of the research and education committee at the Swedish society of breast imaging. Fredrik is the principal investigator of two AI-based clinical trials: ScreenTrustCAD and ScreenTrustMRI. His research evolves around exploring AI on multimodal radiology images – for early detection and for therapy choice and prognosis. Fredrik and his team curate high-quality datasets and radiologist annotations to develop new AI models for breast imaging models, performing reader studies and external validations. He leads the Swedish multicenter VAI-B, Validation platform for AI in Breast imaging, which is a platform hosting images from various clinical centers linked to clinical outcomes. Fredrik is engaged in two EU-funded projects, RadioVal and EUCAIM; and he is also involved in the EUCanScreen initiative. Fredrik’s long-term aim is to explore the full power of AI on multi-modal images, combined with the expertise of medical professionals, to improve outcomes for women with breast cancer.

ORIGINAL RESEARCH:
Salim M, Liu Y, Sorkhei M, Ntoula D, Foukakis T, Fredriksson I, Wang Y, Eklund M, Azizpour H, Smith K, Strand F. AI-based selection of individuals for supplemental MRI in population-based breast cancer screening: the randomized ScreenTrustMRI trial. Nature medicine 2024 30:9 2623-2630

Dembrower K, Crippa A, Colón E, Eklund M, Strand F. ScreenTrustCAD Trial Consortium. Artificial intelligence for breast cancer detection in screening mammography in Sweden: a prospective, population-based, paired-reader, non-inferiority study The Lancet. Digital health 2023 5:10 e703-e711

Yala A, Mikhael PG, Strand F, Lin G, Satuluru S, Kim T, Banerjee I, Gichoya J, Trivedi H, Lehman CD, Hughes K, Sheedy DJ, Matthis LM, Karunakaran B, Hegarty KE, Sabino S, Silva TB, Evangelista MC, Caron RF, Souza B, Mauad EC, Patalon T, Handelman-Gotlib S, Guindy M, Barzilay R. Multi-Institutional Validation of a Mammography-Based Breast Cancer Risk Model. Journal of clinical oncology : official journal of the American Society of Clinical Oncology 2022 40:16 1732-1740

Yala A, Mikhael PG, Lehman C, Lin G, Strand F, Wan YL, Hughes K, Satuluru S, Kim T, Banerjee I, Gichoya J, Trivedi H, Barzilay R. Optimizing risk-based breast cancer screening policies with reinforcement learning Nature medicine 2022 28:1 136-143