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Gill, Ritu

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Gill

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Ritu

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Gill, Ritu
Author's Publications: search.filters.isAuthorOfPublication.38a5a246-4d42-44f0-9057-2c85fb7c1e7d

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    Green Herring Syndrome: Bacterial Infection in Patients With Mucormycosis Cavitary Lung Disease
    (Oxford University Press, 2014) Peixoto, Driele; Hammond, Sarah; Issa, Nicolas; Madan, Rachna; Gill, Ritu; Milner, Danny; Colson, Yolonda; Koo, Sophia; Baden, Lindsey; Marty, Francisco
    Mucormycosis is a life-threatening fungal disease in patients with hematological malignancies. The diagnosis of pulmonary mucormycosis is particularly challenging. We describe 3 mucormycosis cases with an uncommon presentation in patients whose cavitary lung disease was attributed to well documented bacterial infection, although evolution and reassessment established mucormycosis as the underlying disease.
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    Quantitative Clinical Staging for Patients With Malignant Pleural Mesothelioma
    (Oxford University Press, 2017) Gill, Ritu; Yeap, Beow; Bueno, Raphael; Richards, William
    Abstract Background: Analysis of the International Association for the Study of Lung Cancer (IASLC) Malignant Pleural Mesothelioma (MPM) database revealed that clinical (cTNM) staging minimally stratified survival and was discrepant with pathological (pTNM) staging. To improve prognostic classification of MPM, alternative staging models based on quantitative parameters were explored. Methods: An institutional review board–approved MPM registry was queried to identify patients with available pathological and preoperative imaging data. Qualifying patients were randomly assigned to training and test sets in a 1:2 ratio. Computed cTNM and pTNM staging (AJCC Cancer Staging Manual, 7th ed.) were compared. Quantitative image analysis included tumor volume assessed from three-dimensional reconstruction of computed tomography scans (VolCT) and maximal fissural thickness (Fmax). Survival was estimated using the Kaplan-Meier method, and the relationship with VolCT was examined by Cox regression analysis to identify optimized cut-points. Performance of cTNM and quantitative models derived was compared in the test set using Harrell’s C index. Results: A total of 472 patients met inclusion criteria. TNM staging was concordant with pathological TNM staging in 171 of 472 (36.2%), understaged in 209 (44.2%), and overstaged in 92 (19.4%) patients. The most concordant feature was involvement of interlobar fissures. A quantitative clinical staging model comprising VolCT and Fmax (c-index = 0.638, 95% confidence interval [CI] = 0.603 to 0.673) performed statistically significantly better as a prognostic classifier when compared in the test set with cTNM (c-index = 0.562, 95% CI = 0.525 to 0.599, P = .001). Conclusions: Improved prognostic performance may be achievable by quantitative clinical staging combining VolCT and Fmax, providing a cost-effective and clinically relevant surrogate for clinical TNM stage.
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    Fine needle aspirate flow cytometric phenotyping characterizes immunosuppressive nature of the mesothelioma microenvironment
    (Nature Publishing Group, 2016) Lizotte, Patrick H.; Jones, Robert E.; Keogh, Lauren; Ivanova, Elena; Liu, Hongye; Awad, Mark; Hammerman, Peter S.; Gill, Ritu; Richards, William; Barbie, David; Bass, Adam; Bueno, Raphael; English, Jessie M.; Bittinger, Mark; Wong, Kwok-Kin
    With the emergence of checkpoint blockade and other immunotherapeutic drugs, and the growing adoption of smaller, more flexible adaptive clinical trial designs, there is an unmet need to develop diagnostics that can rapidly immunophenotype patient tumors. The ability to longitudinally profile the tumor immune infiltrate in response to immunotherapy also presents a window of opportunity to illuminate mechanisms of resistance. We have developed a fine needle aspirate biopsy (FNA) platform to perform immune profiling on thoracic malignancies. Matching peripheral blood, bulk resected tumor, and FNA were analyzed from 13 mesothelioma patients. FNA samples yielded greater numbers of viable cells when compared to core needle biopsies. Cell numbers were adequate to perform flow cytometric analyses on T cell lineage, T cell activation and inhibitory receptor expression, and myeloid immunosuppressive checkpoint markers. FNA samples were representative of the tumor as a whole as assessed by head-to-head comparison to single cell suspensions of dissociated whole tumor. Parallel analysis of matched patient blood enabled us to establish quality assurance criteria to determine the accuracy of FNA procedures to sample tumor tissue. FNA biopsies provide a diagnostic to rapidly phenotype the tumor immune microenvironment that may be of great relevance to clinical trials.