RT info:eu-repo/semantics/article
T1 Tumor cell load and heterogeneity estimation from diffusion-weighted MRI calibrated with histological data: an example from lung cancer
A1 Yin, Yi
A1 Sedlaczek, Oliver
A1 Muller, Benedikt
A1 Warth, Arne
A1 González-Vallinas Garrachón, Margarita
A1 Lahrmann, Bernd
A1 Grabe, Niels
A1 Kauczor, Hans-Ulrich
A1 Breuhahn, Kai
A1 Vignon Clementel, Irene E.
A1 Drasdo, Dirk
K1 Cancer
K1 DWI
K1 Histopatología
K1 Heterogeneidad
K1 3205 Medicina Interna
K1 3207.13 Oncología
AB 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.
PB Institute of Electrical and Electronics Engineers
SN 0278-0062
YR 2018
FD 2018
LK http://uvadoc.uva.es/handle/10324/42583
UL http://uvadoc.uva.es/handle/10324/42583
LA eng
NO IEEE Transactions on Medical Imaging, 2018, vol. 37, n. 1 p. 35-46
NO Producción Científica
DS UVaDOC
RD 20-sep-2024