WHO 2016 subtyping and automated segmentation of glioma using multi-task deep learning
This work addresses the need for accurate and efficient glioma analysis in clinical decision-making, representing a strong specific gain in medical imaging.
The authors tackled the problem of non-invasive glioma characterization and segmentation by developing a multi-task deep learning model that predicts IDH mutation, 1p/19q co-deletion, and tumor grade while segmenting tumors from 3D MRI scans, achieving an IDH-AUC of 0.90, 1p/19q-AUC of 0.85, grade-AUC of 0.81, and a mean DICE score of 0.84 on an independent test set.
Accurate characterization of glioma is crucial for clinical decision making. A delineation of the tumor is also desirable in the initial decision stages but is a time-consuming task. Leveraging the latest GPU capabilities, we developed a single multi-task convolutional neural network that uses the full 3D, structural, pre-operative MRI scans to can predict the IDH mutation status, the 1p/19q co-deletion status, and the grade of a tumor, while simultaneously segmenting the tumor. We trained our method using the largest, most diverse patient cohort to date containing 1508 glioma patients from 16 institutes. We tested our method on an independent dataset of 240 patients from 13 different institutes, and achieved an IDH-AUC of 0.90, 1p/19q-AUC of 0.85, grade-AUC of 0.81, and a mean whole tumor DICE score of 0.84. Thus, our method non-invasively predicts multiple, clinically relevant parameters and generalizes well to the broader clinical population.