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Our findings suggest that pridopidine induces metabolic changes in brain regions implicated as important for mediating compensatory mechanisms in Huntington disease. In addition, the finding of a strong relationship between clinical severity and metabolic activity after treatment also suggests that pridopidine treatment targets a Huntington disease-related metabolic activity pattern.

Purpose To quantify the radiation dose distribution and lesion morphometry (shape) at baseline, prior to chemoradiation, and at the time of radiographic recurrence in patients with glioblastoma (GBM). Methods The IMRT dose distribution, location of the center of mass, sphericity, and solidity of the contrast enhancing tumor at baseline and the time of tumor recurrence was quantified in 48 IDH wild-type GBM who underwent postoperative IMRT (2 Gy daily for total of 60 Gy) with concomitant and adjuvant temozolomide. Results Average radiation dose within enhancing tumor at baseline and recurrence was ≥ 60 Gy. Centroid location of the enhancing tumor shifted an average of 11.3 mm at the time of recurrence with respect to pre-IMRT location. A positive correlation was observed between change in centroid location and PFS in MGMT methylated patients (P = 0.0007) and Cox multivariate regression confirmed centroid distance from baseline was associated with PFS when accounting for clinical factors (P = 0.0189). Lesion solidity was higher at recurrence compared to baseline (P = 0.0118). Tumors that progressed > 12 weeks after IMRT were significantly more spherical (P = 0.0094). Conclusion Most GBMs recur local within therapeutic IMRT doses; however, tumors with longer PFS occurred further from the original tumor location and were more solid and/or nodular.

Introduction: We propose a new method for quantifying the effect of endovascular therapy for acute ischemic stroke. Currently, an mTICI (modified treatment in cerebral ischemia) score is assigned manually to document the success of endovascular revascularization therapy. The mTICI score based on Digital Subtraction Angiography (DSA), due to visual assignment, has limitations in settings where standardization is pertinent. Methods: We hypothesize that mTICI scores can be classified successfully by deep learning and thus be used as a standardized imaging biomarker. We aim to develop a regression framework using classification models that can assign continuous score to patients depending on the success of therapy, resulting in a score that is more granular than the mTICI. We use deep learning and 3D Convolutional Neural Networks (CNN) to classify frontal post-intervention DSA 2D time series into the mTICI score categories of 0, 1, 2a, 2b, and 3. An mTICI score of 0 represents no perfusion and a score of 3 represents full perfusion. The DSA series serve as features where the time dimension is the third dimension for the CNN. For our preliminary research we have condensed our groupings into binary {0,1} (0 refers to mTICI of 0, 1, 2a while 1 refers to mTICI of 2b, 3) of frontal DSA to see if Deep Learning models can categorize between the different mTICI classes. Results: We reduced our original data size of 181 patients to 93 patients in binary group 0 and 88 patients in group 1. Using a train/test split of 0.2, we have achieved a test classification accuracy of 73%, and F1-Score of 72.2% on the binary dataset. This is a good statistical indication that neural networks are able to classify between DSA. Conclusion: Neural network models show promise as a method of distinguishing between DSA to be used as an automatic standardized scoring method for acute ischemic stroke procedures. We aim to expand this research to frontal and lateral DSA images to get more vascular information to improve model accuracies. We propose using the softmax score of the classifier as a new score which will be a standardized measurement for endovascular therapy success.