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Abstract #1579

Deep learning voxelwise classification of primary central nervous system lymphoma using DSC-PWI normalized time-intensity curves

Alonso Garcia-Ruiz1, Albert Pons-Escoda2, Francesco Grussu1, Pablo Naval-Baudin2, Camilo Monreal1, Antonio Lopez-Rueda3, Laura Oleaga3, Carlos Majos2, and Raquel Perez-Lopez1
1Vall d'Hebron Institute of Oncology, Barcelona, Spain, 2Bellvitge University Hospital, L'Hospitalet de Llobregat, Spain, 3Hospital ClĂ­nic de Barcelona, Barcelona, Spain


The robustness and accuracy of current MR methods to differentiate brain tumours is limited. In this study we investigate the potential of dynamic susceptibility perfusion-weighted imaging (DSC-PWI) normalized time-intensity-curves (nTIC) to support lymphoma diagnosis by harnessing voxelwise and temporal information to train a convolutional neural network (CNN). This novel approach discriminated patients with lymphoma from glioblastoma and metastasis with an average accuracy of 0.94, using only a limited number of patients for training, outperforming standard DSC-PWI measurements. Furthermore, it provides voxel-by-voxel lymphoma probability maps to further help visual diagnosis of neuroradiologists.

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