The VPH-PRISM (Virtual Physiological Human: Personalised Predictive Breast Cancer Therapy Through Integrated Tissue Micro-Structure Modeling) project aimed to increase our understanding of the presentation of breast cancer on different radiological modalities (ultrasound, mammography, MRI etc) by correlating these modalities to each other and to histopathology. The project was executed in an international consortium, in which Radboudumc acted as both a clinical and research site. Radboudumc research was performed within DIAG, both at the Radiology department and the Pathology department. The research that was performed within the Computational Pathology Group resulted in 5 publications in peer reviewed journals as well as numerous conference papers, and in the PhD thesis ‘Histopathological Diagnosis of Breast Cancer’ by Dr Babak Ehteshami Bejnordi. Technology was developed to automatically detect and characterize different types of breast lesions, as well as to normalize differences in stain appearance between different laboratories.
VPH-PRISM
People
Publications
- B. Ehteshami Bejnordi, M. Mullooly, R. Pfeiffer, S. Fan, P. Vacek, D. Weaver, S. Herschorn, L. Brinton, B. van Ginneken, N. Karssemeijer, A. Beck, G. Gierach, J. van der Laak and M. Sherman, "Using deep convolutional neural networks to identify and classify tumor-associated stroma in diagnostic breast biopsies", Modern Pathology, 2018;31(10):1502-1512.
- B. Bejnordi, G. Zuidhof, M. Balkenhol, M. Hermsen, P. Bult, B. van Ginneken, N. Karssemeijer, G. Litjens and J. van der Laak, "Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images", Journal of Medical Imaging, 2017;4(4):044504.
- T. Mertzanidou, J. Hipwell, S. Reis, D. Hawkes, B. Bejnordi, M. Dalmis, S. Vreemann, B. Platel, J. van der Laak, N. Karssemeijer, M. Hermsen, P. Bult and R. Mann, "3D volume reconstruction from serial breast specimen radiographs for mapping between histology and 3D whole specimen imaging", Medical Physics, 2017;44(3):935-948.
- B. Bejnordi, M. Balkenhol, G. Litjens, R. Holland, P. Bult, N. Karssemeijer and J. van der Laak, "Automated Detection of DCIS in Whole-Slide H&E Stained Breast Histopathology Images", IEEE Transactions on Medical Imaging, 2016;35(9):2141-2150.
- B. Bejnordi, G. Litjens, N. Timofeeva, I. Otte-Holler, A. Homeyer, N. Karssemeijer and J. van der Laak, "Stain specific standardization of whole-slide histopathological images", IEEE Transactions on Medical Imaging, 2016;35(2):404-415.
- T. Mertzanidou, J. Hipwell, S. Reis, B. Bejnordi, M. Hermsen, M. Dalmis, S. Vreemann, B. Platel, J. van der Laak, N. Karssemeijer, R. Mann, P. Bult and D. Hawkes, "Whole Mastectomy Volume Reconstruction from 2D Radiographs and Its Mapping to Histology", Breast Imaging, 2016;9699:367-374.
- M. Dalmis, G. Litjens, K. Holland, A. Setio, R. Mann, N. Karssemeijer and A. Gubern-Mérida, "Using deep learning to segment breast and fibroglandular tissue in MRI volumes", Medical Physics, 2017;44(2):533-546.
- S. Vreemann, M. Dalmis, P. Bult, N. Karssemeijer, M. Broeders, A. Gubern-Mérida and R. Mann, "Amount of fibroglandular tissue FGT and background parenchymal enhancement BPE in relation to breast cancer risk and false positives in a breast MRI screening program", European Radiology, 2019;29:4678-4690.