INTEGRATION OF BIOMEDICAL IMAGING AND TRANSLATIONAL APPROACHES FOR MANAGEMENT OF HEAD AND NECK CANCER.
Creators: name: Mohamed, Abdallah
Publisher: name: figshare
Year: 2022
Subjects: subject: 111208 Radiation Therapy
subject: FOS: Clinical medicine
subject: 111204 Cancer Therapy (excl. Chemotherapy and Radiation Therapy)
subject: 29903 Medical Physics
subject: FOS: Physical sciences
subject: 111299 Oncology and Carcinogenesis not elsewhere classified
subject: 110320 Radiology and Organ Imaging
subject: 110315 Otorhinolaryngology
subject: 80199 Artificial Intelligence and Image Processing not elsewhere classified
subject: FOS: Computer and information sciences
subject: Biomarkers
subject: 111201 Cancer Cell Biology
subject: 111205 Chemotherapy
subject: 110599 Dentistry not elsewhere classified
subject: 110313 Nuclear Medicine
subject: 110505 Oral Medicine and Pathology

description: <b>A Dissertation Presented to the Faculty of The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences</b><b>in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy </b><b>by Abdallah Sherif Radwan Mohamed, M.D., M.Sc.</b><br>Thesis Advisory Professor: Clifton D. Fuller, M.D., Ph.D.<br><br>Thesis Committee Members:Robert Bast, MDStephen Y. Lai, MD, PhD<br>Jihong Wang, PhD Jason Stafford, PhDPanayiotis Mavroidis, PhD<br><br>Presented in Public Defense &amp; Examination inHouston, Texas, and virtually via Teleconference, on <br>March 30th, 2022.<br><b>Thesis Abstract </b>The aim of the clinical component of this work was to determine whether the currently available clinical imaging tools can be integrated with radiotherapy (RT) platforms for monitoring and adaptation of radiation dose, prediction of tumor response and disease outcomes, and characterization of patterns of failure and normal tissue toxicity in head and neck cancer (HNC) patients with potentially curable tumors. In Aim 1, we showed that the currently available clinical imaging modalities can be successfully used to adapt RT dose based-on dynamic tumor response, predict oncologic disease outcomes, characterize RT-induced toxicity, and identify the patterns of disease failure. We used anatomical MRIs for the RT dose adaptation purpose. Our findings showed that after proper standardization of the immobilization and image acquisition techniques, we can achieve high geometric accuracy. These images can then be used to monitor the shrinkage of tumors during RT and optimize the clinical target volumes accordingly. Our results also showed that this MR-guided dose adaptation technique has a dosimetric advantage over the standard of care and was associated with a reduction in normal tissue doses that translated into a reduction of the odds of long-term RT-induced toxicity. In the second aim, we used quantitative MRIs to determine its benefit for prediction of oncologic outcomes and characterization of RT-induced normal tissue toxicity. Our findings showed that delta changes of apparent diffusion coefficient parameters derived from diffusion-weighted images at mid-RT can be used to predict local recurrence and recurrence free survival. We also showed that K trans and Ve vascular parameters derived from dynamic contrast-enhanced MRIs can characterize the mandibular areas of osteoradionecrosis. In the final clinical aim, we used CT images of recurrence and baseline CT planning images to develop a methodology and workflow that involves the application of deformable image registration software as a tool to standardize image co-registration in addition to granular combined geometric- and dosimetric-based failure characterization to correctly attribute sites and causes of locoregional failure. We then successfully applied this methodology to identify the patterns of failure following postoperative and definitive IMRT in HNC patients. Using this methodology, we showed that most recurrences occurred in the central high dose regions for patients treated with definitive IMRT compared with mainly non-central high dose recurrences after postoperative IMRT. We also correlated recurrences with pretreatment FDG-PET and identified that most of the central high dose recurrences originated in an area that would be covered by a 10-mm margin on the volume of 50% of the maximum FDG uptake. In the translational component of this work, we integrated radiomic features derived from pre-RT CT images with whole-genome measurements using TCGA and TCIA data. Our results demonstrated a statistically significant associations between radiomic features characterizing different tumor phenotypes and different genomic features. These findings represent a promising potential towards non-invasively tract genomic changes in the tumor during treatment and use this information to adapt treatment accordingly. In the final project of this dissertation, we developed a high-throughput approach to identify effective systemic agents against aggressive head and neck tumors with poor prognosis like anaplastic thyroid cancer. We successfully identified three candidate drugs and performed extensive in vitro and in vivo validation using orthotopic and PDX models. Among these drugs, HDAC inhibitor and LBH-589 showed the most effective tumor growth inhibition that can be used in future clinical trials.<br><br>This is a deposition of the Thesis version presented for Public Defense and Examination, and preliminarily approved by the Thesis Committee on 2022-03-30; a finalized version will deposited at the Texas Medical Center Digital Commons [https://digitalcommons.library.tmc.edu/utgsbs_dissertations/] upon formal Graduate School of Biomedical Sciences administrative approval, proofing/formatting, and doctoral degree conferral. <br>The presented Public Defense slide (2022-03-30) presentation is deposited as a PowerPoint and PDF version.<br>