Recent advances by Rose-Hulman students have provided an important step forward in automating and refining the manual breast examination process, bolstering research by two professors using reverse problem-solving techniques to aid early detection of breast cancer.
A specially designed electromechanical (robot) device slightly indents silicone-simulated breast tissue test samples (phantoms) into multiple locations in search of unusually hard areas. The results are reflected in a 3D map, which can reveal suspected tumors that require medical follow-up.
This new procedure correctly identified 14 tumor-free and 14 tumor-containing ghosts in early testing. It’s an important achievement that could help pave the way for a more comfortable examination than current mammography procedures.
“This is a huge step forward in university-level research that is taking incremental steps forward on campus over the years. We are closer to having technology that provides valuable data to further enhance our studies,” he said. mechanical engineering professor Laurie OlsonPh.D., who joined with Electrical and computer engineering professor Robert ThronePh.D., in studies that have been underway for nearly 20 years.
“Real engineering is a step-by-step process that is methodical and somewhat tedious but nonetheless noteworthy,” said Olson, recipient of the Rose-Hulman Board of Trustees Distinguished Scholar Award in 2013. (Throne received the award in 2021.) It has been a great learning experience for students interested in a potential career in research.”
This will include Andrew Hubbard, one of the seniors mechanical engineering He majored in secondary robotics, who redesigned the robotic arm mechanism and control system, and collected valuable data for future studies and research papers as part of Rose-Hulman’s Undergraduate Summer Research Fellowships Program (R-SURF).
“This was a great opportunity to work with Dr. Olson on an interesting project that happened to be a physical application of my professional interests in robotics. Hubbard, who is considering several graduate opportunities, said,”
Previous students have used their undergraduate research experience to open doors to master’s and doctoral degrees and to be included in Olson and Throne’s list as co-authors of papers and presentations published nationally at national conferences.
Early detection of breast cancer is essential to a patient’s survival. Mammograms and manual breast examination are the most widely used and effective methods for early detection, but these methods have both physical and psychological drawbacks. Also because a mammogram is an X-ray of the breast and exposes the patient to radiation, there are restrictions on specific age groups and risk groups. Also, mammograms are sensitive to tissue density – not hardness.
According to Olson, in most patients, cancerous tissue is denser than healthy tissue, but for the half of women over 40 with dense breasts, the differences are small and detecting them can be difficult. Cancerous tissue has an elastic sclerosis that may be up to ten times stronger than healthy tissue.
Meanwhile, manual breast exams rely on rigidity, but are limited in their ability to reliably detect tumors. They only provide information about the location where the force is applied. Nor does it provide any quantitative or recordable information for comparison with future tests.
Olson and Throne strive to automate and enhance the manual breast examination process with the device that has been refined by students undertaking first-year mechanical engineering graduate projects, independent research projects and specialist summer internships. These projects were supported by the National Science Foundation, Rose-Hulman Foundation’s Lawrence Giacolito Scholarship Fund (from 2018 to 21 when Throne was Giacolito Chair for Electrical and Computer Engineering), and the RSURF program.
Mechanical Engineering Associate Professor Dan Stoecklin, Ph.D., and students enhanced aspects of the project by applying machine learning principles to data similar to that collected by a robotic arm.
“Our students have made huge contributions to this project. It has been beneficial to them, it has enhanced their college and project experiences,” Olson said. “Now that we have determined that our process works with medium-sized breast models, we need to examine whether it can detect Easily hardened areas in large and small breasts, and whether any changes should be made to our regimen.”
Previous student researchers who helped with the project are Alison Crump, Matthew Billingsley, Matthew Conrad, Emily Cottingham, Caitlin Douglas, Robert Fleischl, Jonathan Gannon, Kaelyn Griffin, Tianhong Han, Matthew Hummel, Wanley He, Nikolai Yovanak, Thomas Jansen, Michael Jones, Zhicheng Kai, Tressa Lauer, Bowen Li, Xiaoyin Ling, Allen Liu, Emily Macak, Haley O’Neil, Gustavo Romo, Emily Rusnak, Geoffrey Tan, Ryan Tarr, Michael Samp, Brendan Smyth, Justin Speedy, Griffin Steffy, James Tumavich, And Jiaojiao Wang. Contributors to the capstone design teams were Alison Brumenschinkel, Logan Caldwell, Peter Garnash, Taylor Graham, Erin Minervy, Carson Stone and Dayong (Rae) Tong.
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