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DICOMAnon helps imaging teams anonymize, batch process, and automate DICOM workflows without writing custom scripts.
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Duke University
DICOMAnon helps imaging teams anonymize, batch process, and automate DICOM workflows without writing custom scripts.
Mechanistic tumor growth models provide a framework for linking observed tumor kinetics to underlying biological processes; however, their application to real preclinical datasets is limited by incomplete longitudinal measurements and uncertainty in model par...
Standardizing the assessment of the Tumor Microenvironment (TME) is critical for personalizing therapy in Head and Neck Squamous Cell Carcinoma (HNSCC). As part of a larger multiomics effort to discover multiscale biomarkers, we developed a mathematical frame...
To develop an in-silico tumor model that incorporates nutrient-driven growth and radiotherapy response to generate spatio-temporal proliferating (P), quiescent (Q), and necrotic (N) cell maps for radiomics-based heterogeneity analysis.
To develop a multi-parametric MRI (mp-MRI) radiomics framework for predicting post-resection glioblastoma (GBM) survival by integrating conventional MR modalities with a quantum mechanics–inspired imaging representation.
To develop an in silico clinical trial framework based on mathematical modeling of tumor response to chemoradiation therapy as a prognostic indication of patient outcomes.