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Glioblastoma is profoundly heterogeneous in regional microstructure andvasculature. Characterizing the spatial heterogeneity of glioblastoma couldlead to more precise treatment. With unsupervised learning techniques,glioblastoma MRI-derived radiomic features have been widely utilized for tumorsub-region segmentation and survival prediction. However, the reliability ofalgorithm outcomes is often challenged by both ambiguous intermediate processand instability introduced by the randomness of clustering algorithms,especially for data from heterogeneous patients.  In this paper, we propose an adaptive unsupervised learning approach forefficient MRI intra-tumor partitioning and glioblastoma survival prediction. Anovel and problem-specific Feature-enhanced Auto-Encoder (FAE) is developed toenhance the representation of pairwise clinical modalities and thereforeimprove clustering stability of unsupervised learning algorithms such asK-means. Moreover, the entire process is modelled by the Bayesian optimization(BO) technique with a custom loss function that the hyper-parameters can beadaptively optimized in a reasonably few steps. The results demonstrate thatthe proposed approach can produce robust and clinically relevant MRIsub-regions and statistically significant survival predictions.

Adaptive unsupervised learning with enhanced feature representation for intra-tumor partitioning and survival prediction for glioblastoma