Oncology Commons^™

Quantifying The Magnitude Of Total Dose Deviation Caused By Various Sources Of Error Among Iroc Phantom Irradiation Results, Sharbacha S. Edward

Dissertations & Theses (Open Access)

The Imaging and Radiation Oncology Core (IROC) phantoms are used as an end-to-end test of an institution’s radiotherapy processes, and for clinical trial credentialing. Phantoms are treated like patients, and evaluation of the doses received by the thermoluminescent dosimeters (TLDs) inside the phantom, reflects the accuracy with which an institution can image, plan and irradiate a phantom or patient. Recent phantom results show that among the hundreds of various IROC phantoms irradiated annually, 8-17% of institutions fail this test. The purpose of this work was to investigate the various types of errors that may occur during the treatment process and …

Accelerating Multiparametric Mri For Adaptive Radiotherapy, Shraddha Pandey

USF Tampa Graduate Theses and Dissertations

MR guided Radiotherapy (MRgRT) marks an important paradigm shift in the field of radiotherapy. Superior tissue contrast of MRI offers better visualization of the abnormal lesions, as a result precise radiation dose delivery is possible. In case of online treatment planning, MRgRT offers better control of intratumoral motion and quick adaptation to changes in the gross tumor volume. Nonetheless, the MRgRT process flow does suffer from some challenges that limit its clinical usability. The primary aspects of MRgRT workflow are MRI acquisition, tumor delineation, dose map prediction and administering treatment. It is estimated that the acquisition of MRI takes around …

Absolute Quantification Of Tc-99m Activity Distributions Using A Planar Molecular Breast Imaging Commercial System, Benjamin P. Lopez

Dissertations & Theses (Open Access)

Molecular breast imaging (MBI) uses two dedicated-breast semiconductor detectors to visualize the preferential uptake of technetium-99m-sestamibi (^99mTc-sestamibi) by breast cancer cells relative to surrounding benign breast tissues. Clinically, MBI is used primarily as a supplementary tool to standard-of-care mammography because of its improved detection of breast cancers, especially in women with mammographically-dense breasts. Because of a lack of image corrections, MBI applications are currently limited to qualitative evaluations of relative pixel intensities between image regions with suspected lesions and normal tissue.

The objective of this dissertation was to use Monte Carlo simulations to better characterize the MBI imaging …

Weakly-Supervised Tumor Purity Prediction From Frozen H&E Stained Slides, Matthew Brendel, Vanesa Getseva, Majd Al Assaad, Michael Sigouros, Alexandros Sigaras, Troy Kane, Pegah Khosravi, Juan Miguel Mosquera, Olivier Elemento, Iman Hajirasouliha

Publications and Research

Background

Estimating tumor purity is especially important in the age of precision medicine. Purity estimates have been shown to be critical for correction of tumor sequencing results, and higher purity samples allow for more accurate interpretations from next-generation sequencing results. Molecular-based purity estimates using computational approaches require sequencing of tumors, which is both time-consuming and expensive.

Methods

Here we propose an approach, weakly-supervised purity (wsPurity), which can accurately quantify tumor purity within a digitally captured hematoxylin and eosin (H&E) stained histological slide, using several types of cancer from The Cancer Genome Atlas (TCGA) as a proof-of-concept.

Findings

Our model predicts …

Hepatocellular Carcinoma Image-Guided Intervention: Quantitative Characterization Of Reagents For Thermochemical Ablation, Emily A. Thompson

Dissertations & Theses (Open Access)

Thermochemical ablation (TCA) is a minimally invasive therapy under development for hepatocellular carcinoma, a leading cause of cancer death worldwide. TCA utilizes acid-base chemistry delivered simultaneously to induce local ablation when administered. When delivered via a mixing catheter placed directly into the tumor, acid (e.g., AcOH) and base (e.g., NaOH) react to completion at the catheter tip, producing the acetate salt, water, and releasing heat (Δ>50°C) in sufficient quantities to induce lethal osmotic and thermal stress in tumor cells. However, these two reagents are not distinguishable from tissues with noninvasive imaging modalities, which makes monitoring the delivery of TCA …

Modeling Of Cns Cancer With A Focus On The Immune Component, Daniel Zamler

Dissertations & Theses (Open Access)

The knowledge surrounding cancers of the central nervous system remains poorly developed, in particular with regard to the immune component. The works contained in this thesis look at craniopharyngioma, glioblastoma, and several forms of brain metastasis. While some attention is given to the tumor cells themselves, as well as the patient setting which these studies model, the immune component of disease progression and treatment plays a strong role in each and is the primary focus of the works contained.

Craniopharyngioma is a relatively rare tumor in adults. Although histologically benign, it can be locally aggressive and may require additional therapeutic …

Foundations Of Plasmas For Medical Applications, T. Von Woedtke, Mounir Laroussi, M. Gherardi

Electrical & Computer Engineering Faculty Publications

Plasma medicine refers to the application of nonequilibrium plasmas at approximately body temperature, for therapeutic purposes. Nonequilibrium plasmas are weakly ionized gases which contain charged and neutral species and electric fields, and emit radiation, particularly in the visible and ultraviolet range. Medically-relevant cold atmospheric pressure plasma (CAP) sources and devices are usually dielectric barrier discharges and nonequilibrium atmospheric pressure plasma jets. Plasma diagnostic methods and modelling approaches are used to characterize the densities and fluxes of active plasma species and their interaction with surrounding matter. In addition to the direct application of plasma onto living tissue, the treatment of liquids …

The Effects Of Metronomic And Maximum-Tolerated Dose Chemotherapy In Colorectal Cancer Angiogenesis: A Combined Approach Using Endoscopic Diffuse Reflectance Spectroscopy And Mrna Expression, Ariel Isaac Mundo Ortiz

Graduate Theses and Dissertations

Colorectal cancer (CRC) continues to be one of the most incident and deadliest types of cancer worldwide. Chemotherapy has proven effective to reduce tumor burden for CRC patients, but there are several disadvantages associated with the use of mainstay maximtolerated dose (MTD) chemotherapeutic strategies. Metronomic chemotherapy (MET) has been developed as an alternative that addresses the shortcomings of maximum-tolerated dose chemotherapy but so far its effectiveness as a neoadjuvant strategy for CRC has not been explored.

This dissertation uses a combined optics and molecular biology approach (using diffuse reflectance spectroscopy and mRNA expression) to study the changes in angiogenesis and …

Machine Learning Based Medical Image Deepfake Detection: A Comparative Study, Siddharth Solaiyappan, Yuxin Wen

Engineering Faculty Articles and Research

Deep generative networks in recent years have reinforced the need for caution while consuming various modalities of digital information. One avenue of deepfake creation is aligned with injection and removal of tumors from medical scans. Failure to detect medical deepfakes can lead to large setbacks on hospital resources or even loss of life. This paper attempts to address the detection of such attacks with a structured case study. Specifically, we evaluate eight different machine learning algorithms, which include three conventional machine learning methods (Support Vector Machine, Random Forest, Decision Tree) and five deep learning models (DenseNet121, DenseNet201, ResNet50, ResNet101, VGG19) …

Adverse Events Reporting Of Clinical Trials In Exercise Oncology Research (Advance): Protocol For A Scoping Review, Hao Luo, Oliver Schumacher, Daniel A. Galvão, Robert U. Newton, Dennis R. Taaffe

Research outputs 2022 to 2026

Introduction: Adequate, transparent, and consistent reporting of adverse events (AEs) in exercise oncology trials is critical to assess the safety of exercise interventions for people following a cancer diagnosis. However, there is little understanding of how AEs are reported in exercise oncology trials. Thus, we propose to conduct a scoping review to summarise and evaluate current practice of reporting of AEs in published exercise oncology trials with further exploration of factors associated with inadequate reporting of AEs. The study findings will serve to inform the need for future research on standardisation of the definition, collection, and reporting of AEs for …

Upregulation Of Cd36, A Fatty Acid Translocase, Promotes Colorectal Cancer Metastasis By Increasing Mmp28 And Decreasing E-Cadherin Expression, James Drury, Piotr G. Rychahou, Courtney O. Kelson, Mariah E. Geisen, Yuanyuan Wu, Daheng He, Chi Wang, Eun Y. Lee, B. Mark Evers, Yekaterina Y. Zaytseva

Surgery Faculty Publications

Altered fatty acid metabolism continues to be an attractive target for therapeutic intervention in cancer. We previously found that colorectal cancer (CRC) cells with a higher metastatic potential express a higher level of fatty acid translocase (CD36). However, the role of CD36 in CRC metastasis has not been studied. Here, we demonstrate that high expression of CD36 promotes invasion of CRC cells. Consistently, CD36 promoted lung metastasis in the tail vein model and GI metastasis in the cecum injection model. RNA-Seq analysis of CRC cells with altered expression of CD36 revealed an association between high expression of CD36 and upregulation …

Construction Of A Repeatable Framework For Prostate Cancer Lesion Binary Semantic Segmentation Using Convolutional Neural Networks, Ian Vincent O. Mirasol, Patricia Angela R. Abu, Rosula Sj Reyes

Department of Information Systems & Computer Science Faculty Publications

Prostate cancer is the 3rd most diagnosed cancer overall. Current screening methods such as the prostate-specific antigen test could result in overdiagonosis and overtreatment while other methods such as a transrectal ultrasonography are invasive. Recent medical advancements have allowed the use of multiparametric MRI — a noninvasive and reliable screening process for prostate cancer. However, assessment would still vary from different professionals introducing subjectivity. While con-volutional neural network has been used in multiple studies to ob-jectively segment prostate lesions, due to the sensitivity of datasets and varying ground-truth established used in these studies, it is not possible to reproduce and …

Multi-Modality Automatic Lung Tumor Segmentation Method Using Deep Learning And Radiomics, Siqiu Wang

Theses and Dissertations

Delineation of the tumor volume is the initial and fundamental step in the radiotherapy planning process. The current clinical practice of manual delineation is time-consuming and suffers from observer variability. This work seeks to develop an effective automatic framework to produce clinically usable lung tumor segmentations. First, to facilitate the development and validation of our methodology, an expansive database of planning CTs, diagnostic PETs, and manual tumor segmentations was curated, and an image registration and preprocessing pipeline was established. Then a deep learning neural network was constructed and optimized to utilize dual-modality PET and CT images for lung tumor segmentation. …

Qu-Brats: Miccai Brats 2020 Challenge On Quantifying Uncertainty In Brain Tumor Segmentation - Analysis Of Ranking Scores And Benchmarking Results, Raghav Mehta, Angelos Filos, Ujjwal Baid, Chiharu Sako, Richard Mckinley, Michael Rebsamen, Katrin Dätwyler, Raphael Meier, Piotr Radojewski, Gowtham Krishnan Murugesan, Sahil Nalawade, Chandan Ganesh, Ben Wagner, Fang F. Yu, Baowei Fei, Ananth J. Madhuranthakam, Joseph A. Maldjian, Laura Daza, Catalina Gómez, Pablo Arbeláez, Chengliang Dai, Shuo Wang, Hadrien Reynaud, Yuan-Han Mo, Elsa Angelini, Yike Guo, Wenjia Bai, Subhashis Banerjee, Lin-Min Pei, Murat Ak, Sarahi Rosas-González, Ilyess Zemmoura, Clovis Tauber, Minh H. Vu, Tufve Nyholm, Tommy Löfstedt, Laura Mora Ballestar, Veronica Vilaplana, Hugh Mchugh, Gonzalo Maso Talou, Alan Wang, Jay Patel, Ken Chang, Katharina Hoebel, Mishka Gidwani, Nishanth Arun, Sharut Gupta, Mehak Aggarwal, Praveer Singh, Elizabeth R. Gerstner, Jayashree Kalpathy-Cramer, Nicholas Boutry, Alexis Huard, Lasitha Vidyaratne, Md. Monibor Rahman, Khan M. Iftekharuddin, Joseph Chazalon, Elodie Puybareau, Guillaume Tochon, Jun Ma, Mariano Cabezas, Xavier Llado, Arnau Oliver, Liliana Valencia, Sergi Valverde, Mehdi Amian, Mohammadreza Soltaninejad, Andriy Myronenko, Ali Hatamizadeh, Xue Feng, Quan Dou, Nicholas Tustison, Craig Meyer, Nisarg A. Shah, Sanjay Talbar, Marc-André Weber, Abhishek Mahajan, Andras Jakab, Roland Wiest, Hassan M. Fathallah-Shaykh, Arash Nazeri, Mikhail Milchenko1, Daniel Marcus, Aikaterini Kotrotsou, Rivka Colen, John Freymann, Justin Kirby, Christos Davatzikos, Bjoern Menze, Spyridon Bakas, Yarin Gal, Tal Arbel

Electrical & Computer Engineering Faculty Publications

Deep learning (DL) models have provided the state-of-the-art performance in a wide variety of medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder the translation of DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties, could enable clinical review of the most uncertain regions, thereby building trust and paving the way towards clinical translation. Recently, a number of uncertainty estimation methods have been introduced for DL medical image segmentation tasks. …