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Martin Eklund

Showing results (71-80 of 207) with videos related to

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Histopathology|May 14, 2020
The utility of artificial intelligence in the assessment of prostate pathologyLars Egevad, Peter Ström, Kimmo Kartasalo, et al.
European Urology Open Science|August 2, 2021
The STHLM3-model, Risk-based Prostate Cancer Testing Identifies Men at High Risk Without Inducing Negative Psychosocial EffectsMarie Koitsalu, Martin Eklund, Jan Adolfsson, et al.
Radiology|December 18, 2019
Comparison of a Deep Learning Risk Score and Standard Mammographic Density Score for Breast Cancer Risk PredictionKarin Dembrower, Yue Liu, Hossein Azizpour, et al.
European Urology Oncology|November 17, 2023
Prognosis of Gleason Score 9-10 Prostatic Adenocarcinoma in Needle Biopsies: A Nationwide Population-based StudyLars Egevad, Chiara Micoli, Hemamali Samaratunga, et al.
Journal of Chemical Information and Modeling|October 16, 2014
Benchmarking study of parameter variation when using signature fingerprints together with support vector machinesJonathan Alvarsson, Martin Eklund, Claes Andersson, et al.
Journal of Clinical Pathology|February 9, 2020
Prognostic value of perineural invasion in prostate needle biopsies: a population-based study of patients treated by radical prostatectomyPeter Ström, Tobias Nordström, Brett Delahunt, et al.
Plos One|July 11, 2018
Predictors of participation in risk-based prostate cancer screeningMarie Koitsalu, Martin Eklund, Jan Adolfsson, et al.
Journal of Clinical Pathology|March 13, 2025
Time trends in Gleason score distribution among Gleason score 8 and 9-10 cancersLars Egevad, Chiara Micoli, Brett Delahunt, et al.
Prostate Cancer and Prostatic Diseases|July 10, 2020
Ethnic variation in prostate cancer detection: a feasibility study for use of the Stockholm3 test in a multiethnic U.S. cohortHari T Vigneswaran, Andrea Discacciati, Peter H Gann, et al.
JNCI Cancer Spectrum|May 7, 2020
Response to Carter et alMartin Eklund, Kristine Broglio, Christina Yau, et al.
Pageof 21

Showing results (71-80 of 207) with videos related to

Sort By:
Pageof 21
Histopathology|May 14, 2020
The utility of artificial intelligence in the assessment of prostate pathologyLars Egevad, Peter Ström, Kimmo Kartasalo, et al.
European Urology Open Science|August 2, 2021
The STHLM3-model, Risk-based Prostate Cancer Testing Identifies Men at High Risk Without Inducing Negative Psychosocial EffectsMarie Koitsalu, Martin Eklund, Jan Adolfsson, et al.
Radiology|December 18, 2019
Comparison of a Deep Learning Risk Score and Standard Mammographic Density Score for Breast Cancer Risk PredictionKarin Dembrower, Yue Liu, Hossein Azizpour, et al.
European Urology Oncology|November 17, 2023
Prognosis of Gleason Score 9-10 Prostatic Adenocarcinoma in Needle Biopsies: A Nationwide Population-based StudyLars Egevad, Chiara Micoli, Hemamali Samaratunga, et al.
Journal of Chemical Information and Modeling|October 16, 2014
Benchmarking study of parameter variation when using signature fingerprints together with support vector machinesJonathan Alvarsson, Martin Eklund, Claes Andersson, et al.
Journal of Clinical Pathology|February 9, 2020
Prognostic value of perineural invasion in prostate needle biopsies: a population-based study of patients treated by radical prostatectomyPeter Ström, Tobias Nordström, Brett Delahunt, et al.
Plos One|July 11, 2018
Predictors of participation in risk-based prostate cancer screeningMarie Koitsalu, Martin Eklund, Jan Adolfsson, et al.
Journal of Clinical Pathology|March 13, 2025
Time trends in Gleason score distribution among Gleason score 8 and 9-10 cancersLars Egevad, Chiara Micoli, Brett Delahunt, et al.
Prostate Cancer and Prostatic Diseases|July 10, 2020
Ethnic variation in prostate cancer detection: a feasibility study for use of the Stockholm3 test in a multiethnic U.S. cohortHari T Vigneswaran, Andrea Discacciati, Peter H Gann, et al.
JNCI Cancer Spectrum|May 7, 2020
Response to Carter et alMartin Eklund, Kristine Broglio, Christina Yau, et al.
Pageof 21