Glioblastoma remains the most devastating brain tumor despite optimal treatment, because of the high rate of recurrence. Distant recurrence has distinct genomic alterations compared to local recurrence, which requires different treatment planning both in clinical practice and trials. To date, perfusion-weighted MRI has revealed that perfusional characteristics of tumor are associated with prognosis. However, not much research has focused on recurrence patterns in glioblastoma: namely, local and distant recurrence. Here, we propose two different neural network models to predict the recurrence patterns in glioblastoma that utilizes high-dimensional radiomic profiles based on perfusion MRI: area under the curve (AUC) (95% confidence interval), 0.969 (0.903–1.000) for local recurrence; 0.864 (0.726–0.976) for distant recurrence for each patient in the validation set. This creates an opportunity to provide personalized medicine in contrast to studies investigating only group differences. Moreover, interpretable deep learning identified that salient radiomic features for each recurrence pattern are related to perfusional intratumoral heterogeneity. We also demonstrated that the combined salient radiomic features, or “radiomic risk score”, increased risk of recurrence/progression (hazard ratio, 1.61; p = 0.03) in multivariate Cox regression on progression-free survival.

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Ta C x N y films were grown by a plasma-enhanced atomic layer deposition using Ta(N-t-C5H11)[N(CH3)2]3 as the precursor and H2 or Ar∕H2 plasma as the reducing agent. The Ar∕H2 plasma appeared to efficiently break the Ta–N bonds in the Ta precursor and formed more TaCx, which significantly decreased the resistivity of the films (∼255μΩcm) compared with the case of the H2 plasma (∼1570μΩcm). The Ar∕H2 plasma also made the films denser and efficiently eliminated the oxygen from the films. This improved the resistance against the elemental diffusion as well as the aging characteristics of the films after exposure to air.

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This study examined the interfacial reaction of plasma-enhanced atomic layer deposited TaCxNy films with underlying SiO2 and HfO2 layers, as well as their effective work functions (EWFs). The adoption of Ar∕H2 plasma as a reducing agent suppressed the interfacial reactions resulting in a lower electrical thickness. However, it increased the interface state density due to the massive Ar+ plasma damage on the dielectric films. The interfacial reactions were suppressed in TaCxNy on HfO2 compared with that on SiO2. The EWF of TaCxNy with the H2 plasma and Ar∕H2 plasma on HfO2 was ∼4.9–5.2 and ∼4.6eV, respectively.