Obstetrics and Gynecology

Project Title: Sexually transmitted infections in an aging population

Faculty Mentor’s Name: Dr. Emily Weber LeBrun
Phone: 352-273-7677
Email: jmo44@ufl.edu

Research Project Description:

Background: The prevalence of sexually transmitted infections (STI) is increasing, especially in patients over the age of 60. Symptoms of STIs in these elderly patients can prompt a referral to Urogynecology since these women don’t typically continue seeing a Gynecologist regularly. We will be testing our new Urogynecology patients for STIs and relating this to their presenting complaints.
Hypothesis: We believe the prevalence of STIs in this population is relatively high and related to urinary complaints and social history.
Methods: There will be retrospective and prospective components to this study to establish prevalence, social background, risk factors, and patient symptoms.
Role of medical student: A student will be involved in all steps of the study. They will help to craft the IRB, gather data, perform mentored statistical analysis, and create an abstract for conference submission.
Because this is an understudied topic, there is the potential for multiple separate studies within this project across several fields including epidemiology, public health, and gynecology.

Entry Date: November 1, 2019

Project Title:Slow Freezing vs. Vitrification for Human Ovary Cryopreservation

Faculty Mentor’s Name:Dr. Gregory Christman
Phone:352-273-7676
Email:lgchristman@ufl.edu

Research Project Description:

This project has already secured IRB Approval. The goal of this project is to compare vitrification vs. slow freezing on human ovarian slices as to the relative ability of both methods to preserve ovarian slices in cancer patients for future transplantation to preserve their fertility. The project will involve securing ovaries removed in the OR for standard clinical indications and transporting them to the assisted reproductive technology lab. Samples will be prepared and frozen using the two different techniques and tissue sections will be sampled from the very same ovary to serve as an internal control. Samples will be studied before freezing for their ability to secrete estradiol in vitro and we will compare this value to the two frozen/thawed samples. Thawed frozen samples will be assessed for histology, apoptosis, steroid production ability and number of viable oocytes. This information will highlight the pros and cons of both methods as a valuable addition to this literature and serve as a framework for the development of a novel translational clinical program to add to the cancer program here at the University of Florida. This project is eligible for one student and will involve time in the clinics, OR, hospital and research laboratory. Students will be encouraged to participate in the preparation of data for abstract presentation and eventual publication.

Entry Date: December 4, 2019

Project Title: Cost Utility Analysis and Safety of Postoperative Hemoglobin Level Testing Following Robotic-Assisted Hysterectomy for Gynecologic Cancer

Faculty Mentor’s Name:Dr. Joel Cardenas
Phone: (352) 273-7562
Email: joelcardenas@ufl.edu

Research Project Description:

The standard practice for patients undergoing surgery for endometrial cancer is robotic-assisted surgery, which requires hospital admission for one or two days and routine daily blood work, including a complete blood count and a comprehensive metabolic panel. The purpose of this study is to assess the clinical relevance of post-operative blood testing following this kind of surgery, to evaluate its potential impact on patient discharge, and to conduct a cost-effectiveness analysis.

IRB-approved study.
Retrospective cohort study.
Data will be abstracted from electronic medical records.

Entry Date: 1/21/2020

Project Title: Artificial Intelligence And Gynecologic Cancer Surgery

Faculty Mentor’s Name:Dr. Joel Cardenas
Phone: (352) 273-7562
Email: joelcardenas@ufl.edu

Research Project Description:

Ovarian cancer is the most lethal gynecologic cancer. One the most important independent prognostic of survival is complete cytoreductive surgery (R0). Taking a patient with stage IIIV to IV to the operating room is a major decision with the hope to remove all tumor. Many factors influence the surgeon’s decision to take a patient to surgery up front versus neo-adjuvant chemotherapy. Previous studies that have been evaluated includes performance status, CA 125, tumor burden on CT scan. CT scan has limitation to identified peritoneal implants around the small-bowel mesentery, sub diaphragmatic space and porta hepatis. The use of laparoscopic model to predict tumor respectability was proposed with a 57.5% of success (R0). A randomized controlled trial using laparoscopy was able to reduce futile laparotomy from 39% to 10%. Base on the laparoscopic findings, 57% of patients who underwent primary cytoreductive surgery were able to obtain R0. The clinical question is how can we maximize the role of surgery upfront and minimize the risk of major surgery without survival benefit with the current tools or what tools are necessary to achieve the greatest prediction?
Artificial intelligence (AI) was introduced in 1950s, and refers to the ability of a machine to perform task commonly associated with intelligent human behavior. It can includes symbolic learning and machine learning. Symbolic learning involves image processing, robotics and computer vision. Machine learning (the ability to learn and improve from data without being explicitly programmed),-pattern recognition, relates to statistical learning, speech recognition, deep learning (a type of machine learning that uses layered neural networks with large amounts of raw data), neuronal network. AI is used for classification and prediction. Learning algorithms can includes Supervised learning (Learns patterns from pre-labeled output data. Training an algorithm with data and have answer), unsupervised learning (Learns pattern from unlabeled data. Training an algorithm with data and want the machine to figure it out the pattern), and reinforcement learning (give an algorithm a goal and expect the machine trial and error).
AI has been used in imaging, clinical outcomes prediction (cervical cancer), translational oncology. There is no publication regarding ovarian cancer and primary debulking surgery.
Bogani et al assess the utility of AI for predicting factor to achieve complete secondary cytoreductive surgery for recurrent ovarian cancer. The retrospective study included 194 patients with recurrent ovarian cancer. Using artificial neuronal network (ANN), different variable were analyzed to predict R0 and survival. Results showed that three main factors to achieve R0 were disease free survival, retroperitoneal recurrence, and residual disease. For overall survival, PFS was the most important variable. ANN analysis was used to weight of the variables to predict R0 and survival outcomes. A simple mathematical model associated with learning algorithms. ANN consist of 4-layer (1 input layer, 2 hidden layers, and 1 output layer) fee-forward analysis. To develop the ANN, cases were randomly assigned to the training group (80%) or to the testing group (20%) through a generator of random numbers.
Above concept may apply to endometrial, cervical, and vulvar cancer.

Hypothesis: can AI successful predict complete debulking surgery and be superior to current standards Can AI predict survival?
Methods: Retrospective cohort study in gynecologic cancer.
Role of medical student: A student will be train in the basics of AI. It also involves data collection, analysis, and create an abstract for local and National meeting.

Entry Date: 1/21/2020

Project Title: The Relative Efficacy and Applicability of Ovarian Tissue Transplantation as an Alternative to Oocyte Harvesting for Fertility Preservation in Female Cancer Patients

Faculty Mentor’s Name: Dr. Alice Rhoton-Vlasak
Phone: (352) 273-7676
Email: rhotona@ufl.edu

Research Project Description:

By providing a more immediate and efficient method of preserving fertility (Kim), and by simultaneously combatting against early-onset menopause in young women, ovarian tissue cryopreservation and transplantation may prove more effective than both embryo and oocyte cryopreservation- the current standard treatments for fertility preservation. The following project will be literature review current to ascertain the evidence supporting these assertions- and to determine the extent to which ovarian tissue cryopreservation can be implemented in pediatric cancer patients in need of gonadotoxic treatments. It will also compare the efficacy of the 2 main freezing methods, vitrification vs slow freezing

Entry Date: April 20, 2020