Browsing by Subject "Breast-conserving surgery"

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    Computational Mechanobiology Model Evaluating Healing of Postoperative Cavities Following Breast-Conserving Surgery
    (bioRxiv, 2023-04-28) Harbin, Zachary; Sohutskay, David; Vanderlaan, Emma; Fontaine, Muira; Mendenhall, Carly; Fisher, Carla; Voytik-Harbin, Sherry; Buganza Tepole, Adrian; Surgery, School of Medicine
    Breast cancer is the most commonly diagnosed cancer type worldwide. Given high survivorship, increased focus has been placed on long-term treatment outcomes and patient quality of life. While breast-conserving surgery (BCS) is the preferred treatment strategy for early-stage breast cancer, anticipated healing and breast deformation (cosmetic) outcomes weigh heavily on surgeon and patient selection between BCS and more aggressive mastectomy procedures. Unfortunately, surgical outcomes following BCS are difficult to predict, owing to the complexity of the tissue repair process and significant patient-to-patient variability. To overcome this challenge, we developed a predictive computational mechanobiological model that simulates breast healing and deformation following BCS. The coupled biochemical-biomechanical model incorporates multi-scale cell and tissue mechanics, including collagen deposition and remodeling, collagen-dependent cell migration and contractility, and tissue plastic deformation. Available human clinical data evaluating cavity contraction and histopathological data from an experimental porcine lumpectomy study were used for model calibration. The computational model was successfully fit to data by optimizing biochemical and mechanobiological parameters through the Gaussian Process. The calibrated model was then applied to define key mechanobiological parameters and relationships influencing healing and breast deformation outcomes. Variability in patient characteristics including cavity-to-breast volume percentage and breast composition were further evaluated to determine effects on cavity contraction and breast cosmetic outcomes, with simulation outcomes aligning well with previously reported human studies. The proposed model has the potential to assist surgeons and their patients in developing and discussing individualized treatment plans that lead to more satisfying post-surgical outcomes and improved quality of life.
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    Computational Mechanobiology Model Evaluating Healing of Postoperative Cavities Following Breast-Conserving Surgery
    (Elsevier, 2023) Harbin, Zachary; Sohutskay, David; Vanderlaan, Emma; Fontaine, Muira; Mendenhall, Carly; Fisher, Carla; Voytik-Harbin, Sherry; Buganza Tepole, Adrian; Surgery, School of Medicine
    Breast cancer is the most commonly diagnosed cancer type worldwide. Given high survivorship, increased focus has been placed on long-term treatment outcomes and patient quality of life. While breast-conserving surgery (BCS) is the preferred treatment strategy for early-stage breast cancer, anticipated healing and breast deformation (cosmetic) outcomes weigh heavily on surgeon and patient selection between BCS and more aggressive mastectomy procedures. Unfortunately, surgical outcomes following BCS are difficult to predict, owing to the complexity of the tissue repair process and significant patient-to-patient variability. To overcome this challenge, we developed a predictive computational mechanobiological model that simulates breast healing and deformation following BCS. The coupled biochemical-biomechanical model incorporates multi-scale cell and tissue mechanics, including collagen deposition and remodeling, collagen-dependent cell migration and contractility, and tissue plastic deformation. Available human clinical data evaluating cavity contraction and histopathological data from an experimental porcine lumpectomy study were used for model calibration. The computational model was successfully fit to data by optimizing biochemical and mechanobiological parameters through Gaussian process surrogates. The calibrated model was then applied to define key mechanobiological parameters and relationships influencing healing and breast deformation outcomes. Variability in patient characteristics including cavity-to-breast volume percentage and breast composition were further evaluated to determine effects on cavity contraction and breast cosmetic outcomes, with simulation outcomes aligning well with previously reported human studies. The proposed model has the potential to assist surgeons and their patients in developing and discussing individualized treatment plans that lead to more satisfying post-surgical outcomes and improved quality of life.
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    A fiber optoacoustic guide with augmented reality for precision breast-conserving surgery
    (Springer Nature, 2018-05-18) Lan, Lu; Xia, Yan; Li, Rui; Liu, Kaiming; Mai, Jieying; Medley, Jennifer Anne; Obeng-Gyasi, Samilia; Han, Linda K.; Wang, Pu; Cheng, Ji-Xin; Radiology and Imaging Sciences, School of Medicine
    Lumpectomy, also called breast-conserving surgery, has become the standard surgical treatment for early-stage breast cancer. However, accurately locating the tumor during a lumpectomy, especially when the lesion is small and nonpalpable, is a challenge. Such difficulty can lead to either incomplete tumor removal or prolonged surgical time, which result in high re-operation rates (~25%) and increased surgical costs. Here, we report a fiber optoacoustic guide (FOG) with augmented reality (AR) for sub-millimeter tumor localization and intuitive surgical guidance with minimal interference. The FOG is preoperatively implanted in the tumor. Under external pulsed light excitation, the FOG omnidirectionally broadcasts acoustic waves through the optoacoustic effect by a specially designed nano-composite layer at its tip. By capturing the acoustic wave, three ultrasound sensors on the breast skin triangulate the FOG tip's position with 0.25-mm accuracy. An AR system with a tablet measures the coordinates of the ultrasound sensors and transforms the FOG tip's position into visual feedback with <1-mm accuracy, thus aiding surgeons in directly visualizing the tumor location and performing fast and accurate tumor removal. We further show the use of a head-mounted display to visualize the same information in the surgeons' first-person view and achieve hands-free guidance. Towards clinical application, a surgeon successfully deployed the FOG to excise a "pseudo tumor" in a female human cadaver. With the high-accuracy tumor localization by FOG and the intuitive surgical guidance by AR, the surgeon performed accurate and fast tumor removal, which will significantly reduce re-operation rates and shorten the surgery time.