2024-03-28T19:12:50Zhttps://uvadoc.uva.es/oai/requestoai:uvadoc.uva.es:10324/425832021-06-23T09:52:44Zcom_10324_1134com_10324_931com_10324_894col_10324_1213
Tumor cell load and heterogeneity estimation from diffusion-weighted MRI calibrated with histological data: an example from lung cancer
Yin, Yi
Sedlaczek, Oliver
Muller, Benedikt
Warth, Arne
González-Vallinas Garrachón, Margarita
Lahrmann, Bernd
Grabe, Niels
Kauczor, Hans-Ulrich
Breuhahn, Kai
Vignon Clementel, Irene E.
Drasdo, Dirk
Producción Científica
Diffusion-weighted magnetic resonance imaging (DWI) is a key non-invasive imaging technique for cancer diagnosis and tumor treatment assessment, reflecting Brownian movement of water molecules in tissues. Since densely packed cells restrict molecule mobility, tumor tissues produce usually higher signal (a.k.a. less attenuated signal) on isotropic maps compared with normal tissues. However, no general quantitative relation between DWI data and the cell density has been established. In order to link low-resolution clinical cross-sectional data with high-resolution histological information, we developed an image processing and analysis chain, which was used to study the correlation between the diffusion coefficient (D value) estimated from DWI and tumor cellularity from serial histological slides of a resected non-small cell lung cancer tumor. Color deconvolution followed by cell nuclei segmentation was performed on digitized histological images to determine local and cell-type specific 2d (two-dimensional) densities. From these, the 3d cell density was inferred by a model-based sampling technique, which is necessary for the calculation of local and global 3d tumor cell count. Next, DWI sequence information was overlaid with high-resolution CT data and the resected histology using prominent anatomical hallmarks for co-registration of histology tissue blocks and non-invasive imaging modalities' data. The integration of cell numbers information and DWI data derived from different tumor areas revealed a clear negative correlation between cell density and D value. Importantly, spatial tumor cell density can be calculated based on DWI data. In summary, our results demonstrate that tumor cell count and heterogeneity can be predicted from DWI data, which may open new opportunities for personalized diagnosis and therapy optimization.
2020-09-28
2020-09-28
2018
info:eu-repo/semantics/article
IEEE Transactions on Medical Imaging, 2018, vol. 37, n. 1 p. 35-46
0278-0062
http://uvadoc.uva.es/handle/10324/42583
10.1109/TMI.2017.2698525
35
1
46
IEEE Transactions on Medical Imaging
37
1558-254X
eng
https://ieeexplore.ieee.org/document/7913723
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/4.0/
© IEEE
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Institute of Electrical and Electronics Engineers