Identifying and modelling clinical subpopulations from the Malmö breast tomosynthesis screening trial
(2020) 15th International Workshop on Breast Imaging, IWBI 2020 In Proceedings of SPIE - The International Society for Optical Engineering 11513.- Abstract
Virtual Clinical Trials (VCT) are an effective tool to evaluate the performance of novel imaging systems using computer simulations. VCT results depend on the selection of virtual patient populations. In the case of breast imaging, virtual patients should be matched to a desired clinical population in terms of selected anatomical or demographic descriptors. We are developing a virtual population of women who participated in the Malmö Breast Tomosynthesis Screening Trial (MBTST). We have used clinical values of the compressed breast thickness and volumetric breast density to develop a multidimensional distribution of women in MBTST. Breast density and thickness values were obtained from anonymized, previously collected tomosynthesis... (More)
Virtual Clinical Trials (VCT) are an effective tool to evaluate the performance of novel imaging systems using computer simulations. VCT results depend on the selection of virtual patient populations. In the case of breast imaging, virtual patients should be matched to a desired clinical population in terms of selected anatomical or demographic descriptors. We are developing a virtual population of women who participated in the Malmö Breast Tomosynthesis Screening Trial (MBTST). We have used clinical values of the compressed breast thickness and volumetric breast density to develop a multidimensional distribution of women in MBTST. Breast density and thickness values were obtained from anonymized, previously collected tomosynthesis images of 14,746 women. In this paper, we compare several approaches to identify clinical subpopulations and select virtual patients that represent various groups of clinical subjects. We performed two methods to identify clinical subpopulations by clustering clinical data using the K-means algorithm or woman's age. The obtained clusters have been explored and compared using the silhouette mean. The K-means algorithm yielded grouping of MBTST data into two clusters; however, that grouping was, shown to be suboptimal by the silhouette analysis. The agebased clustering showed significant overlap in terms of breast thickness and density. We also compared two approaches to select sets of representative phantoms. Our analysis has emphasized benefits and limitations of different clustering methods. The preferred method depends on the specific task that should be addressed using VCTs. Simulation of representative phantoms is ongoing. Potential correlations with pathological findings and/or parenchymal properties will be investigated.
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- author
- Torlegård, B. ; Tingberg, A. LU ; Zackrisson, S. LU ; Barufaldi, B. ; Maidment, A. D.A. ; Dustler, M. LU and Bakic, P. R. LU
- organization
- publishing date
- 2020
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Anatomy simulation, Breast anatomy, Breast density, Compressed breast thickness, Malmö Breast Tomosynthesis Screening Trial, Virtual Clinical Trials, Virtual populations
- host publication
- 15th International Workshop on Breast Imaging, IWBI 2020
- series title
- Proceedings of SPIE - The International Society for Optical Engineering
- editor
- Bosmans, Hilde ; Marshall, Nicholas and Van Ongeval, Chantal
- volume
- 11513
- article number
- 1151316
- publisher
- SPIE
- conference name
- 15th International Workshop on Breast Imaging, IWBI 2020
- conference location
- Leuven, Belgium
- conference dates
- 2020-05-25 - 2020-05-27
- external identifiers
-
- scopus:85086143114
- ISSN
- 1996-756X
- 0277-786X
- ISBN
- 9781510638310
- DOI
- 10.1117/12.2564095
- project
- Simultaneous Digital Breast Tomosynthesis and Mechanical Imaging
- language
- English
- LU publication?
- yes
- id
- bbacb37d-16a9-4ee1-aa49-fbf1073c1139
- date added to LUP
- 2021-01-11 10:46:19
- date last changed
- 2024-09-05 14:00:20
@inproceedings{bbacb37d-16a9-4ee1-aa49-fbf1073c1139, abstract = {{<p>Virtual Clinical Trials (VCT) are an effective tool to evaluate the performance of novel imaging systems using computer simulations. VCT results depend on the selection of virtual patient populations. In the case of breast imaging, virtual patients should be matched to a desired clinical population in terms of selected anatomical or demographic descriptors. We are developing a virtual population of women who participated in the Malmö Breast Tomosynthesis Screening Trial (MBTST). We have used clinical values of the compressed breast thickness and volumetric breast density to develop a multidimensional distribution of women in MBTST. Breast density and thickness values were obtained from anonymized, previously collected tomosynthesis images of 14,746 women. In this paper, we compare several approaches to identify clinical subpopulations and select virtual patients that represent various groups of clinical subjects. We performed two methods to identify clinical subpopulations by clustering clinical data using the K-means algorithm or woman's age. The obtained clusters have been explored and compared using the silhouette mean. The K-means algorithm yielded grouping of MBTST data into two clusters; however, that grouping was, shown to be suboptimal by the silhouette analysis. The agebased clustering showed significant overlap in terms of breast thickness and density. We also compared two approaches to select sets of representative phantoms. Our analysis has emphasized benefits and limitations of different clustering methods. The preferred method depends on the specific task that should be addressed using VCTs. Simulation of representative phantoms is ongoing. Potential correlations with pathological findings and/or parenchymal properties will be investigated.</p>}}, author = {{Torlegård, B. and Tingberg, A. and Zackrisson, S. and Barufaldi, B. and Maidment, A. D.A. and Dustler, M. and Bakic, P. R.}}, booktitle = {{15th International Workshop on Breast Imaging, IWBI 2020}}, editor = {{Bosmans, Hilde and Marshall, Nicholas and Van Ongeval, Chantal}}, isbn = {{9781510638310}}, issn = {{1996-756X}}, keywords = {{Anatomy simulation; Breast anatomy; Breast density; Compressed breast thickness; Malmö Breast Tomosynthesis Screening Trial; Virtual Clinical Trials; Virtual populations}}, language = {{eng}}, publisher = {{SPIE}}, series = {{Proceedings of SPIE - The International Society for Optical Engineering}}, title = {{Identifying and modelling clinical subpopulations from the Malmö breast tomosynthesis screening trial}}, url = {{http://dx.doi.org/10.1117/12.2564095}}, doi = {{10.1117/12.2564095}}, volume = {{11513}}, year = {{2020}}, }