Cerebrovascular Disease Progression in Patients With ACTA2 Arg179 Pathogenic Variants.

OBJECTIVE: To establish progression of imaging biomarkers of stroke, arterial steno-occlusive disease, and white matter injury in patients with smooth muscle dysfunction syndrome caused by mutations in the ACTA2 gene, we analyzed 113 cerebral MRI scans from a retrospective cohort of 27 patients with ACTA2 Arg179 pathogenic variants. METHODS: Systematic quantifications of arterial ischemic strokes and white matter lesions were performed on baseline and follow-up scans using planimetric methods. Critical stenosis and arterial vessel diameters were quantified applying manual and semiautomated methods to cerebral magnetic resonance angiograms. We then assessed correlations between arterial abnormalities and parenchymal injury. RESULTS: We found characteristic patterns of acute white matter ischemic injury and progressive internal carotid artery stenosis during infancy. Longitudinal analysis of patients older than 1.2 years showed stable white matter hyperintensities but increased number of cystic-like lesions over time. Progressive narrowing of the terminal internal carotid artery occurred in 80% of patients and correlated with the number of critical stenoses in cerebral arteries and arterial ischemic infarctions. Arterial ischemic strokes occurred in same territories affected by critical stenosis. CONCLUSIONS: We found characteristic, early, and progressive cerebrovascular abnormalities in patients with ACTA2 Arg179 pathogenic variants. Our longitudinal data suggest that while steno-occlusive disease progresses over time and is associated with arterial ischemic infarctions and cystic-like white matter lesions, white matter hyperintensities can remain stable over long periods. The evaluated metrics will enable diagnosis in early infancy and be used to monitor disease progression, guide timing of stroke preventive interventions, and assess response to current and future therapies.

Autosegmentation for thoracic radiation treatment planning: A grand challenge at AAPM 2017.

PURPOSE: This report presents the methods and results of the Thoracic Auto-Segmentation Challenge organized at the 2017 Annual Meeting of American Association of Physicists in Medicine. The purpose of the challenge was to provide a benchmark dataset and platform for evaluating performance of autosegmentation methods of organs at risk (OARs) in thoracic CT images. METHODS: Sixty thoracic CT scans provided by three different institutions were separated into 36 training, 12 offline testing, and 12 online testing scans. Eleven participants completed the offline challenge, and seven completed the online challenge. The OARs were left and right lungs, heart, esophagus, and spinal cord. Clinical contours used for treatment planning were quality checked and edited to adhere to the RTOG 1106 contouring guidelines. Algorithms were evaluated using the Dice coefficient, Hausdorff distance, and mean surface distance. A consolidated score was computed by normalizing the metrics against interrater variability and averaging over all patients and structures. RESULTS: The interrater study revealed highest variability in Dice for the esophagus and spinal cord, and in surface distances for lungs and heart. Five out of seven algorithms that participated in the online challenge employed deep-learning methods. Although the top three participants using deep learning produced the best segmentation for all structures, there was no significant difference in the performance among them. The fourth place participant used a multi-atlas-based approach. The highest Dice scores were produced for lungs, with averages ranging from 0.95 to 0.98, while the lowest Dice scores were produced for esophagus, with a range of 0.55-0.72. CONCLUSION: The results of the challenge showed that the lungs and heart can be segmented fairly accurately by various algorithms, while deep-learning methods performed better on the esophagus. Our dataset together with the manual contours for all training cases continues to be available publicly as an ongoing benchmarking resource.