A radio-pathomic machine learning (ML) model has been developed to estimate tumor cell density, cytoplasm density (Cyt) and extracellular fluid density (ECF) from multimodal MR images and autopsy pathology. In this multicenter study, we implemented this model to test its ability to predict survival in patients with recurrent glioblastoma (rGBM) treated with chemotherapy. Pre- and post-contrast T-weighted, FLAIR and ADC images were used to generate radio-pathomic maps for 51 patients with longitudinal pre- and post-treatment scans.
View Article and Find Full Text PDFPurpose: Pharmacologic therapies for neurofibromatosis type 1-associated plexiform neurofibromas (NF1-PNs) are limited; currently, none are US Food and Drug Administration-approved for adults.
Methods: ReNeu is an open-label, multicenter, pivotal, phase IIb trial of mirdametinib in 58 adults (≥18 years of age) and 56 children (2 to 17 years of age) with NF1-PN causing significant morbidities. Patients received mirdametinib capsules or tablets for oral suspension (2 mg/m twice daily, maximum 4 mg twice daily), regardless of food intake, in 3 weeks on/1 week off 28-day cycles.
Typical longitudinal radiographic assessment of brain tumors relies on side-by-side qualitative visualization of serial magnetic resonance images (MRIs) aided by quantitative measurements of tumor size. However, when assessing slowly growing tumors and/or complex tumors, side-by-side visualization and quantification may be difficult or unreliable. Whole-brain, patient-specific "digital flipbooks" of longitudinal scans are a potential method to augment radiographic side-by-side reads in clinical settings by enhancing the visual perception of changes in tumor size, mass effect, and infiltration across multiple slices over time.
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