Real-world data and refinement of MRI in early detection of prostate cancer

Per Erik Thimansson

Real-world data and refinement of MRI in early detection of prostate cancer

Research output: Thesis › Doctoral Thesis (compilation)

220 Downloads (Pure)

Original language	English
Qualification	Doctor
Awarding Institution	Department of Translational Medicine
Supervisors/Advisors	Zackrisson, Sophia, Supervisor Bjartell, Anders, Assistant supervisor Baubeta Fridh, Erik, Assistant supervisor
Award date	2024 Feb 9
Place of Publication	Lund
Publisher	Lund University, Faculty of Medicine
ISBN (Print)	978-91-8021-511-4
Publication status	Published - 2024

Bibliographical note

Defence details
Date: 2024-02-09
Time: 09:00
Place: Belfragesalen, BMC D15, Klinikgatan 32 i Lund
External reviewer(s)
Name: Liss, Per
Title: Professor
Affiliation: Uppsala University, Sweden

Subject classification (UKÄ)

Radiology and Medical Imaging

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

Real-world data and refinement of MRI without manuscriptFinal published version, 46.1 MB

3 Article

A pilot study of an organised population‐based testing programme for prostate cancer
Alterbeck, M., Thimansson, E., Bengtsson, J., Baubeta, E., Zackrisson, S., Bolejko, A., Sandeman, K., Carlsson, S., Jiborn, T. & Bjartell, A., 2024, In: BJU International. 133, 1, p. 87-95
Research output: Contribution to journal › Article › peer-review

Open Access
Correlation between ADC, ADC ratio, and Gleason Grade group in prostate cancer patients undergoing radical prostatectomy: Retrospective multicenter study with different MRI scanners
Bengtsson, J., Thimansson, E., Baubeta, E., Zackrisson, S., Sundgren, P. C., Bjartell, A. & Flondell-Sité, D., 2023, In: Frontiers in Oncology. 13, 1079040.
Research output: Contribution to journal › Article › peer-review

Open Access
Deep learning algorithm performs similarly to radiologists in the assessment of prostate volume on MRI
Thimansson, E., Bengtsson, J., Baubeta, E., Engman, J., Flondell-Sité, D., Bjartell, A. & Zackrisson, S., 2023, In: European Radiology. 33, p. 2519–2528
Research output: Contribution to journal › Article › peer-review

Open Access

Cite this

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title = "Real-world data and refinement of MRI in early detection of prostate cancer",

author = "Thimansson, \{Per Erik\}",

note = "Defence details Date: 2024-02-09 Time: 09:00 Place: Belfragesalen, BMC D15, Klinikgatan 32 i Lund External reviewer(s) Name: Liss, Per Title: Professor Affiliation: Uppsala University, Sweden ",

year = "2024",

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isbn = " 978-91-8021-511-4",

series = "Lund University, Faculty of Medicine Doctoral Dissertation Series",

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school = "Department of Translational Medicine",

}

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AU - Thimansson, Per Erik

N1 - Defence details Date: 2024-02-09 Time: 09:00 Place: Belfragesalen, BMC D15, Klinikgatan 32 i Lund External reviewer(s) Name: Liss, Per Title: Professor Affiliation: Uppsala University, Sweden

PY - 2024

Y1 - 2024

M3 - Doctoral Thesis (compilation)

SN - 978-91-8021-511-4

T3 - Lund University, Faculty of Medicine Doctoral Dissertation Series

PB - Lund University, Faculty of Medicine

CY - Lund

ER -

Real-world data and refinement of MRI in early detection of prostate cancer

Bibliographical note

Subject classification (UKÄ)

UN SDGs

Access to Document

Research output

A pilot study of an organised population‐based testing programme for prostate cancer

Correlation between ADC, ADC ratio, and Gleason Grade group in prostate cancer patients undergoing radical prostatectomy: Retrospective multicenter study with different MRI scanners

Deep learning algorithm performs similarly to radiologists in the assessment of prostate volume on MRI

Cite this