| Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci FR Schumacher, AA Al Olama, SI Berndt, S Benlloch, M Ahmed, ... Nature genetics 50 (7), 928-936, 2018 | 920 | 2018 |
| Factors contributing to healthcare professional burnout during the COVID-19 pandemic: A rapid turnaround global survey LA Morgantini, U Naha, H Wang, S Francavilla, Ö Acar, JM Flores, ... PloS one 15 (9), e0238217, 2020 | 792 | 2020 |
| Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study P Ström, K Kartasalo, H Olsson, L Solorzano, B Delahunt, DM Berney, ... The Lancet Oncology 21 (2), 222-232, 2020 | 571 | 2020 |
| Prostate cancer screening in men aged 50–69 years (STHLM3): a prospective population-based diagnostic study H Grönberg, J Adolfsson, M Aly, T Nordström, P Wiklund, Y Brandberg, ... The lancet oncology 16 (16), 1667-1676, 2015 | 466 | 2015 |
| Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge W Bulten, K Kartasalo, PHC Chen, P Ström, H Pinckaers, K Nagpal, Y Cai, ... Nature medicine 28 (1), 154-163, 2022 | 432 | 2022 |
| Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new susceptibility loci and informs genetic risk prediction DV Conti, BF Darst, LC Moss, EJ Saunders, X Sheng, A Chou, ... Nature genetics 53 (1), 65-75, 2021 | 408 | 2021 |
| MRI-targeted or standard biopsy in prostate cancer screening M Eklund, F Jäderling, A Discacciati, M Bergman, M Annerstedt, M Aly, ... New England journal of medicine 385 (10), 908-920, 2021 | 345 | 2021 |
| Prostate-specific antigen (PSA) density in the diagnostic algorithm of prostate cancer T Nordström, O Akre, M Aly, H Grönberg, M Eklund Prostate cancer and prostatic diseases 21 (1), 57-63, 2018 | 264 | 2018 |
| Comparison between the four-kallikrein panel and prostate health index for predicting prostate cancer T Nordström, A Vickers, M Assel, H Lilja, H Grönberg, M Eklund European urology 68 (1), 139-146, 2015 | 259 | 2015 |
| Breast cancer screening in the precision medicine era: risk-based screening in a population-based trial Y Shieh, M Eklund, L Madlensky, SD Sawyer, CK Thompson, ... Journal of the National Cancer Institute 109 (5), djw290, 2017 | 257 | 2017 |
| Effect of artificial intelligence-based triaging of breast cancer screening mammograms on cancer detection and radiologist workload: a retrospective simulation study K Dembrower, E Wåhlin, Y Liu, M Salim, K Smith, P Lindholm, M Eklund, ... The Lancet Digital Health 2 (9), e468-e474, 2020 | 251 | 2020 |
| External evaluation of 3 commercial artificial intelligence algorithms for independent assessment of screening mammograms M Salim, E Wåhlin, K Dembrower, E Azavedo, T Foukakis, Y Liu, K Smith, ... JAMA oncology 6 (10), 1581-1588, 2020 | 241 | 2020 |
| Introducing conformal prediction in predictive modeling. A transparent and flexible alternative to applicability domain determination U Norinder, L Carlsson, S Boyer, M Eklund Journal of chemical information and modeling 54 (6), 1596-1603, 2014 | 213 | 2014 |
| Prostate cancer screening using a combination of risk-prediction, MRI, and targeted prostate biopsies (STHLM3-MRI): a prospective, population-based, randomised, open-label, non … T Nordström, A Discacciati, M Bergman, M Clements, M Aly, M Annerstedt, ... The Lancet Oncology 22 (9), 1240-1249, 2021 | 164 | 2021 |
| Artificial intelligence for breast cancer detection in screening mammography in Sweden: a prospective, population-based, paired-reader, non-inferiority study K Dembrower, A Crippa, E Colón, M Eklund, F Strand The Lancet Digital Health 5 (10), e703-e711, 2023 | 162 | 2023 |
| Polygenic risk score improves prostate cancer risk prediction: results from the Stockholm-1 cohort study M Aly, F Wiklund, J Xu, WB Isaacs, M Eklund, M D'Amato, J Adolfsson, ... European urology 60 (1), 21-28, 2011 | 157 | 2011 |
| Bioclipse: an open source workbench for chemo-and bioinformatics O Spjuth, T Helmus, EL Willighagen, S Kuhn, M Eklund, J Wagener, ... BMC bioinformatics 8, 1-10, 2007 | 157 | 2007 |
| Comparison of a deep learning risk score and standard mammographic density score for breast cancer risk prediction K Dembrower, Y Liu, H Azizpour, M Eklund, K Smith, P Lindholm, F Strand Radiology 294 (2), 265-272, 2020 | 153 | 2020 |
| Population-based screening for cancer: hope and hype Y Shieh, M Eklund, GF Sawaya, WC Black, BS Kramer, LJ Esserman Nature reviews Clinical oncology 13 (9), 550-565, 2016 | 153 | 2016 |
| Artificial intelligence assistance significantly improves Gleason grading of prostate biopsies by pathologists W Bulten, M Balkenhol, JJA Belinga, A Brilhante, A Çakır, L Egevad, ... Modern Pathology 34 (3), 660-671, 2021 | 148 | 2021 |
