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Home > DEPARTMENTS > RADIATION_ONCOLOGY > RADIATION_ONCOLOGY_POSTERS

Posters

 
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  • The Prognostic Significance of Glucose-6-Phosphate Dehydrogenase as a Biomarker in Head and Neck Squamous Cell Carcinomas Treated with Conventional Chemoradiation by Kenneth H. Barker, Barbara L. Pruetz, Jessica D. Arden, Thomas J. Quinn, and George D. Wilson

    The Prognostic Significance of Glucose-6-Phosphate Dehydrogenase as a Biomarker in Head and Neck Squamous Cell Carcinomas Treated with Conventional Chemoradiation

    Kenneth H. Barker, Barbara L. Pruetz, Jessica D. Arden, Thomas J. Quinn, and George D. Wilson

    Publication Date: 5-2-2022

    INTRODUCTION
    The prognosis of patients with head and neck squamous cell carcinoma (HNSCC) treated with chemoradiation can be predicted using p16 as a surrogate biomarker of Human Papilloma Virus (HPV) status, but a subset of patients continues to do poorly despite a positive or negative p16 status. This project attempted to identify another biomarker, glucose-6-phosphate dehydrogenase (G6PD) as a marker for prognosis in HNSCC patients. Radiation induces oxidative damage to destroy tumor cells, and G6PD is a key enzyme involved in protecting cells from oxidative damage. The goal of this project was to identify a prognostic biomarker that would aid in recognizing patients who would not respond well to chemoradiation and potentially respond better to other therapeutic measures.

  • Functional Lung Segmentation from Computed Tomography Images using Deep Learning by Duyen M. Quach, Evan Porter, and Thomas M. Guerrero

    Functional Lung Segmentation from Computed Tomography Images using Deep Learning

    Duyen M. Quach, Evan Porter, and Thomas M. Guerrero

    Publication Date: 5-2-2022

    INTRODUCTION
    Functional avoidance treatment planning allows radiation oncologists to intentionally minimize radiation dose to higher-functioning lung areas while favoring radiation dose towards lower-functioning regions. This treatment planning method has been shown to reduce pulmonary toxicity for patients receiving radiation therapy. Functional lung information, traditionally ventilation or perfusion SPECT scans, is required to plan for functional avoidance.

  • Evaluating the Impact of Software Distortion Correction on Target Doses in Cranial Stereotactic Radiosurgery by Sharjil Shamim and Zachary Seymour

    Evaluating the Impact of Software Distortion Correction on Target Doses in Cranial Stereotactic Radiosurgery

    Sharjil Shamim and Zachary Seymour

    Publication Date: 5-2-2022

    INTRODUCTION
    Brainlab’s Elements suite proposed a novel MR distortion correction method based on CT imaging. This involves registration of sub-volumes from MR imaging to generate single continuous deformation field maps, creating a corrected MR image set. No literature currently exists studying the impact of software correction on target dose for cranial stereotactic radiosurgery. In this study, we aim to evaluate the impact of Brainlab distortion correction on radiation target dose compared to original treatment plans without distortion correction.

  • Fluoroscopic Demonstration of Thoracic Tumor Immobilization with High Frequency Percussive Ventilation by Cristian Solano, Ina M. Sala, Beverly Maurer, Ronald Levin, and Thomas M. Guerrero

    Fluoroscopic Demonstration of Thoracic Tumor Immobilization with High Frequency Percussive Ventilation

    Cristian Solano, Ina M. Sala, Beverly Maurer, Ronald Levin, and Thomas M. Guerrero

    Publication Date: 5-2-2022

    INTRODUCTION
    High frequency percussive ventilation (HFPV) is a novel immobilization technique that utilizes high frequency low tidal volume ventilation to produce endotracheal percussion. In a previous departmental study of chest wall motion immobilization, it was found that volunteers were able to tolerate HFPV for varying lengths of time – from a few to tens of minutes. By investigating a novel process to immobilize the chest wall, and thus thoracic tumors, it can allow for more localized radiation delivery and reduction of healthy tissue irradiation.

  • Development of A Deep Neural Network for Synthesis of Non-Contrast Cranial T1-Weighted Magnetic Resonance Imaging by Agueda M. Taylor, Evan Porter, and Thomas Guerrero

    Development of A Deep Neural Network for Synthesis of Non-Contrast Cranial T1-Weighted Magnetic Resonance Imaging

    Agueda M. Taylor, Evan Porter, and Thomas Guerrero

    Publication Date: 5-2-2022

    INTRODUCTION
    Although 122 out of 1000 people in the US have MRI’s done each year, there are over 4 million with contraindications that subsequently forgo the diagnostic benefits. Studies in recent years have implemented artificial intelligence (AI) algorithms such as deep neural networks (DNN) for production of synthetic medical imaging. The goals of this project are to develop a DNN, specifically a Generative Adversarial Network (GAN) that will predict synthetic Cranial T1 Weighted MRI from non-contrast CT, and to evaluate the model quality.

 
 
 

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