- Author
-
I. Jansen
- Title
- Bladder cancer diagnostics
- Subtitle
- A digital slide into the future
- Supervisors
- Co-supervisors
- Award date
- 4 November 2020
- Number of pages
- 150
- ISBN
- 9789463809740
- Document type
- PhD thesis
- Faculty
- Faculty of Medicine (AMC-UvA)
- Abstract
-
Up to 78% of patients with non-muscle invasive bladder cancer will develop a recurrence within 5-years after initial diagnosis. Therefore, lifelong follow-up with cystoscopy and upper tract imaging is recommended by the guidelines. Adequate risk assessment is extremely difficult and current risk tools fail to correctly predict the chance of recurrence for patients with non-muscle invasive bladder cancer. The current risk stratification systems are based on clinical and pathological tumor characteristics; however, these parameters are operator dependent and prone to interobserver variability. The histological assessment is an important parameter in the risk stratification, however, the agreement between pathologists for the assessment of aggressiveness (grading) and tumor ingrowth (staging of the tumor are only up to 50% and 60%, respectively.
The objective of this thesis is to develop methods to improve the risk stratification of patients with non-muscle invasive bladder cancer (NMIBC). We therefore investigated the feasibility of reconstructing bladder tumors in 3D out of two-dimensional (2D) histological slides, and whether this can be combined with three-dimensional (3D) mass spectrometry imaging data. We assessed the association of intravesical tumor location on one- and fiveyear recurrence-free survival (RFS) in patients with NMIBC. Finally, we developed a deep learning network to automatically detect and grade urothelial cell carcinoma on histological slides and to predict the one- and five-year RFS in these patients. - Note
- Please note that the sections 'About the author' and 'Dankwoord' are not included in the thesis downloads.
- Persistent Identifier
- https://hdl.handle.net/11245.1/e70abd46-09c4-4429-9418-aac6fbc27bd4
- Downloads
-
Thesis (complete)
Front matter
Chapter 1: General introduction & thesis outline
Chapter 2: Histopathology: Ditch the slides because digital and 3D are on show
Chapter 3: Three-dimensional histopathological reconstructions of bladder tumors
Chapter 4: Strategies for managing multi-patient 3D mass spectrometry imaging data
Chapter 5: The association of intravesical tumor location on recurrence-free survival in patients with non-muscle invasive bladder cancer
Chapter 6: Automated detection and grading of non-muscle invasive urothelial cell carcinoma of the bladder
Chapter 7: Deep learning based recurrence prediction in patients with non-muscle invasive bladder cancer
Chapter 8: Discussion, conclusions and future perspectives
Summary; Nederlandse samenvatting; Portfolio; Authors contributions; Authors affiliations; List of publications
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