DeepGlioma, a multimodal deeplearning approach for intraoperative diagnostic glioma screening trained on stimulated Raman histology and large-scale genomic data, can predict molecular alterations for diffuse glioma diagnosis ToddCHollon UMich
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations., DeepGlioma for molecular prediction is intended for patients with clinical and radiographic evidence of a diffuse glioma who are undergoing surgery for tissue diagnosis and/or tumor resection. The surgical specimen is sampled from the patient’s tumor and directly loaded into a premade, disposable microscope slide with an attached coverslip.
. The size and number of strips to be acquired is set by the operator who defines the desired image size. Standard image sizes range from 1-5 mmand image acquisition time ranges from 30 seconds to 3 minutes. The strips are edge-clipped, field-flattened, and registered to generate whole slide SRH images, which are then used for both DeepGlioma training and inference.
, The learned genetic embedding vector space with the 11 most commonly mutated genes shown. Both the mutant and wildtype mutational statuses are included during training to encode the presence or absence of a mutation. Genes that co-occur in specific molecular subgroups cluster together within the vector space, such as mutations that occur in (
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