Browsing by Author "Wright, J."

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    Control of pathogenic effector T-cell activities in situ by PD-L1 expression on respiratory inflammatory dendritic cells during respiratory syncytial virus infection
    (Nature Publishing Group, 2015-12) Yao, S.; Jiang, L.; Moser, E. K.; Jewett, L. B.; Wright, J.; Du, J.; Zhou, B.; Davis, S. D.; Krupp, N. L.; Braciale, T. J.; Sun, J.; Department of Pediatrics, IU School of Medicine
    Respiratory syncytial virus (RSV) infection is a leading cause of severe lower respiratory tract illness in young infants, the elderly and immunocompromised individuals. We demonstrate here that the co-inhibitory molecule programmed cell death 1 (PD-1) is selectively upregulated on T cells within the respiratory tract during both murine and human RSV infection. Importantly, the interaction of PD-1 with its ligand PD-L1 is vital to restrict the pro-inflammatory activities of lung effector T cells in situ, thereby inhibiting the development of excessive pulmonary inflammation and injury during RSV infection. We further identify that PD-L1 expression on lung inflammatory dendritic cells is critical to suppress inflammatory T-cell activities, and an interferon-STAT1-IRF1 axis is responsible for increased PD-L1 expression on lung inflammatory dendritic cells. Our findings suggest a potentially critical role of PD-L1 and PD-1 interactions in the lung for controlling host inflammatory responses and disease progression in clinical RSV infection.
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    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 Medicine
    BACKGROUND 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.