# Clinical decision-support algorithms for interactive design of patient-specific breast molds > **NIH NIH R01** · UNIVERSITY OF HOUSTON · 2024 · $665,331 ## Abstract Breast reconstruction can help feminine-presenting individuals retain or regain quality of life by mitigating the impacts of body image disruption due to appearance changes arising from mastectomy. Autologous reconstruction is widely recognized as effective, with long term advantages over other techniques. However, autologous reconstruction procedures are complex, lengthy operations requiring substantial skill and experience. Moreover, a revision procedure is typically required to adequately restore the patient’s bodily form; in some cases, multiple revisions are needed. Prior work investigated simple molds that merely copied the preoperative shape and size of the patient's breasts, or a mirrored version of the contralateral breast in the case of unilateral breast reconstruction. But many patients desire or require a different breast form after mastectomy and so simply copying the preoperative ‘native’ breast form is inadequate. The proposed study would shift current clinical practice paradigms by enabling patient-specific molds for patients whose reconstructive goals are not merely to reproduce their preoperative breasts. Moreover, while a few proof-of-concept studies have demonstrated the feasibility of using patient-specific molds to shape tissue into a breast form, a critical barrier to progress in the field is that no one has rigorously evaluated their impact. In contrast, the proposed study includes a randomized controlled clinical trial for evaluation. The goal of this study is to develop clinical decision-support algorithms for designing patient-specific breast molds for tissue shaping. We hypothesize that autologous reconstruction will be more efficient when performed with patient-specific molds designed using our clinical decision-support algorithms. The proposed project is significant because it has the potential to improve the efficiency of autologous breast reconstruction, an important component of breast cancer rehabilitation. The investigators are a multi-disciplinary team bringing complementary expertise in biomedical informatics, biostatistics, engineering technology, and reconstructive procedures. This innovative project seeks to overcome the limitations of prior studies by developing clinical decision-support algorithms to enable design of patient- specific molds that are suitable for patients whose reconstructive goals are more complex than reproducing their preoperative appearance. Our approach entails developing clinical decision-support algorithms 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. Our team will benefit from the extensive resources available in the Multidisciplinary Breast Reconstruction Research Program. After successful completion of this study, we will pursue a path to clinical translation analogous to existing products and services, such as current systems for designing pat... ## Key facts - **NIH application ID:** 10982126 - **Project number:** 1R01CA286648-01A1 - **Recipient organization:** UNIVERSITY OF HOUSTON - **Principal Investigator:** Ashleigh Michelle Francis - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $665,331 - **Award type:** 1 - **Project period:** 2024-08-01 → 2028-07-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10982126 ## Citation > US National Institutes of Health, RePORTER application 10982126, Clinical decision-support algorithms for interactive design of patient-specific breast molds (1R01CA286648-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10982126. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*