  Preoperative and noninvasive prediction of the meningioma grade is important
in clinical practice, as it directly influences the clinical decision making.
What's more, brain invasion in meningioma (i.e., the presence of tumor tissue
within the adjacent brain tissue) is an independent criterion for the grading
of meningioma and influences the treatment strategy. Although efforts have been
reported to address these two tasks, most of them rely on hand-crafted features
and there is no attempt to exploit the two prediction tasks simultaneously. In
this paper, we propose a novel task-aware contrastive learning algorithm to
jointly predict meningioma grade and brain invasion from multi-modal MRIs.
Based on the basic multi-task learning framework, our key idea is to adopt
contrastive learning strategy to disentangle the image features into
task-specific features and task-common features, and explicitly leverage their
inherent connections to improve feature representation for the two prediction
tasks. In this retrospective study, an MRI dataset was collected, for which 800
patients (containing 148 high-grade, 62 invasion) were diagnosed with
meningioma by pathological analysis. Experimental results show that the
proposed algorithm outperforms alternative multi-task learning methods,
achieving AUCs of 0:8870 and 0:9787 for the prediction of meningioma grade and
brain invasion, respectively. The code is available at
https://github.com/IsDling/predictTCL.
