Quantitative assessment of head growth supports early detection of neurological disease, yet current clinical practice relies on manual measurements that are variable, anatomically limited, and difficult to scale. Here we introduce a fully automated imaging-native framework (CranioTrace) that transforms routine neuroim...
P. Mattisson, A. Mandal, M. Gardner et al.· bioRxiv· 0 citations
Clinical brain MRIs from pediatric health systems represent a viable resource for modeling early neurodevelopmental trajectories and studying neurodevelopmental risk in real-world populations. However, a limitation to date has been the performance of existing segmentation tools for measuring various brain phenotypes in...
E. Levitis, H. Tregidgo, D. Zimmerman et al.· medRxiv· 0 citations
Accurate prediction of tumor recurrence in brain tumor patients following surgery is essential for optimizing adjuvant therapy, response assessment, and surveillance regimen. While MRI remains the gold standard for surveillance, integrating patient-specific clinical context may inform recurrence prediction. Traditional...
D. Tak, D. Sreedhar, H. Aerts et al.· medRxiv· 0 citations
PURPOSE
To develop and validate a pediatric diffuse midline glioma (DMG) auto-segmentation tool optimized for longitudinal treatment response assessment across the disease course.
MATERIALS AND METHODS
In this multi-institutional retrospective study, we included patients aged 1-30 years with DMG from an institutional...
John Zielke, Francesca Romana Mussa, A. Zapaishchykova et al.· AJNR. American journal of ne...· 0 citations
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