Tumor cell load and heterogeneity estimation from diffusion-weighted MRI calibrated with histological data: an example from lung cancer

Abstract : 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 to 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 (NSCLC) 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 (three-dimensional) 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.
Type de document :
Article dans une revue
IEEE Transactions on Medical Imaging, Institute of Electrical and Electronics Engineers, 2017, 〈10.1109/TMI.2017.2698525〉
Liste complète des métadonnées

https://hal.inria.fr/hal-01421398
Contributeur : Yi Yin <>
Soumis le : jeudi 22 décembre 2016 - 11:13:32
Dernière modification le : jeudi 26 avril 2018 - 10:27:53

Identifiants

Collections

Citation

Yi Yin, Oliver Sedlaczek, Benedikt Müller, Arne Warth, Margarita González-Vallinas, et al.. Tumor cell load and heterogeneity estimation from diffusion-weighted MRI calibrated with histological data: an example from lung cancer. IEEE Transactions on Medical Imaging, Institute of Electrical and Electronics Engineers, 2017, 〈10.1109/TMI.2017.2698525〉. 〈hal-01421398〉

Partager

Métriques

Consultations de la notice

422