  Brain tumor is one of the leading causes of cancer death. The high-grade
brain tumors are easier to recurrent even after standard treatment. Therefore,
developing a method to predict brain tumor recurrence location plays an
important role in the treatment planning and it can potentially prolong
patient's survival time. There is still little work to deal with this issue. In
this paper, we present a deep learning-based brain tumor recurrence location
prediction network. Since the dataset is usually small, we propose to use
transfer learning to improve the prediction. We first train a multi-modal brain
tumor segmentation network on the public dataset BraTS 2021. Then, the
pre-trained encoder is transferred to our private dataset for extracting the
rich semantic features. Following that, a multi-scale multi-channel feature
fusion model and a nonlinear correlation learning module are developed to learn
the effective features. The correlation between multi-channel features is
modeled by a nonlinear equation. To measure the similarity between the
distributions of original features of one modality and the estimated correlated
features of another modality, we propose to use Kullback-Leibler divergence.
Based on this divergence, a correlation loss function is designed to maximize
the similarity between the two feature distributions. Finally, two decoders are
constructed to jointly segment the present brain tumor and predict its future
tumor recurrence location. To the best of our knowledge, this is the first work
that can segment the present tumor and at the same time predict future tumor
recurrence location, making the treatment planning more efficient and precise.
The experimental results demonstrated the effectiveness of our proposed method
to predict the brain tumor recurrence location from the limited dataset.
