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Item Chronic Pancreatitis: What the Clinician Wants to Know from MR Imaging(Elsevier, 2018-08) Tirkes, Temel; Radiology and Imaging Sciences, School of MedicineDiagnosis of chronic pancreatitis requires a complete medical history and clinical investigations, including imaging technologies and function tests. MR imaging/magnetic resonance cholangiopancreatography is the preferred diagnostic tool for detection of ductal and parenchymal changes in patients with chronic pancreatitis. Ductal changes may not be present in the initial phase of chronic pancreatitis. Therefore, early diagnosis remains challenging.Item MR imaging findings in a neonate with COVID -19 associated encephalitis(Elsevier, 2021) Martin, Paul J.; Felker, Marcia; Radhakrishnan, Rupa; Radiology and Imaging Sciences, School of MedicineItem Radiomics Can Distinguish Pediatric Supratentorial Embryonal Tumors, High-Grade Gliomas, and Ependymomas(AJNR, 2022-04) Zhang, M.; Tam, L.; Wright, J.; Mohammadzadeh, M.; Han, M.; Chen, E.; Wagner, M.; Nemalka, J.; Lai, H.; Eghbal, A.; Ho, C. Y.; Lober, R. M.; Cheshier, S. H.; Vitanza, N. A.; Grant, G. A.; Prolo, L. M; Yeom, K. W.; Jaju, A.; Radiology and Imaging Sciences, School of MedicineBACKGROUND AND PURPOSE: Pediatric supratentorial tumors such as embryonal tumors, high-grade gliomas, and ependymomas are difficult to distinguish by histopathology and imaging because of overlapping features. We applied machine learning to uncover MR imaging–based radiomics phenotypes that can differentiate these tumor types. MATERIALS AND METHODS: Our retrospective cohort of 231 patients from 7 participating institutions had 50 embryonal tumors, 127 high-grade gliomas, and 54 ependymomas. For each tumor volume, we extracted 900 Image Biomarker Standardization Initiative–based PyRadiomics features from T2-weighted and gadolinium-enhanced T1-weighted images. A reduced feature set was obtained by sparse regression analysis and was used as input for 6 candidate classifier models. Training and test sets were randomly allocated from the total cohort in a 75:25 ratio. RESULTS: The final classifier model for embryonal tumor-versus-high-grade gliomas identified 23 features with an area under the curve of 0.98; the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 0.85, 0.91, 0.79, 0.94, and 0.89, respectively. The classifier for embryonal tumor-versus-ependymomas identified 4 features with an area under the curve of 0.82; the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 0.93, 0.69, 0.76, 0.90, and 0.81, respectively. The classifier for high-grade gliomas-versus-ependymomas identified 35 features with an area under the curve of 0.96; the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 0.82, 0.94, 0.82, 0.94, and 0.91, respectively. CONCLUSIONS: In this multi-institutional study, we identified distinct radiomic phenotypes that distinguish pediatric supratentorial tumors, high-grade gliomas, and ependymomas with high accuracy. Incorporation of this technique in diagnostic algorithms can improve diagnosis, risk stratification, and treatment planning.