A multi-institutional research team, including Professor Fatima Merchant from the University of Houston, has received a $2.7 million grant from the National Institutes of Health (NIH) to develop patient-specific molds for breast reconstruction following mastectomy. The project aims to enhance the efficiency of breast reconstruction, improve psychosocial adjustment for cancer survivors, and reduce the overall time spent in care.
Addressing the Challenges of Breast Reconstruction
Breast cancer affects 1 in 8 women in the United States, and a significant portion of those undergoing surgery require additional breast reconstruction. Autologous reconstruction, which involves using tissue from another part of the patient's body, is a common and effective method. However, these procedures are complex and often necessitate revision surgeries to achieve the desired aesthetic outcome.
"Breast reconstruction can help women retain or regain quality of life by mitigating the impacts of body image disruption due to appearance changes arising from mastectomy," said Merchant.
The team's approach involves developing clinical decision-support algorithms to design patient-specific breast molds for tissue shaping. These molds are intended to reduce the need for multiple revision procedures, thereby lowering costs and minimizing patient discomfort and risk.
Innovation in Mold Design
Traditional molds often replicate the preoperative breast shape or mirror the contralateral breast in unilateral cases. However, many patients seek a different breast form post-mastectomy, rendering simple replication inadequate. The new algorithm-driven molds will consider the patient's desired breast form, representing a significant advancement in personalized breast reconstruction.
Clinical Trial and Algorithm Development
The research team will conduct a randomized controlled clinical trial to evaluate the impact of these patient-specific molds. The clinical decision-support algorithms are informed by expertise in image perception, machine learning, image processing, and shape modeling.
"Our approach in developing the clinical decision-support algorithms is informed by our experience in image perception, machine learning, image processing, and shape modeling, and conducting a thorough evaluation in a randomized controlled clinical trial," said Merchant.
The research team includes principal investigators Ashleigh M. Francis at The University of Texas MD Anderson Cancer Center and Mia K. Markey at The University of Texas at Austin, as well as investigators from UH. The team is part of the Multidisciplinary Breast Reconstruction Research Program.
