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Fougeron N, Oddes Z, Ashkenazi A, Solav D. Identification of constitutive materials of bi-layer soft tissues from multimodal indentations. J Mech Behav Biomed Mater 2024; 155:106572. [PMID: 38754153 DOI: 10.1016/j.jmbbm.2024.106572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Revised: 04/19/2024] [Accepted: 05/08/2024] [Indexed: 05/18/2024]
Abstract
The personalisation of finite element models is an important problem in the biomechanical fields where subject-specific analyses are fundamental, particularly in studying soft tissue mechanics. The personalisation includes the choice of the constitutive law of the model's material, as well as the choice of the material parameters. In vivo identification of the material properties of soft tissues is challenging considering the complex behaviour of soft tissues that are, among other things, non-linear hyperelastic and heterogeneous. Hybrid experimental-numerical methods combining in vivo indentations and inverse finite element analyses are common to identify these material parameters. Yet, the uniqueness and the uncertainty of the multi-material hyperelastic model have not been evaluated. This study presents a sensitivity analysis performed on synthetic indentation data to investigate the identification uncertainties of the material parameters in a bi-material thigh phantom. Synthetic numerical data, used to replace experimental measurements, considered several measurement modalities: indenter force and displacement, stereo-camera 3D digital image correlation of the indented surface, and ultrasound B-mode images. A finite element model of the indentation was designed with either Ogden-Moerman or Mooney-Rivlin constitutive laws for both materials. The parameters' identifiability (i.e. the possibility of converging to a unique parameter set within an acceptable margin of error) was assessed with various cost functions formulated using the different synthetic data sets. The results underline the need for multiple experimental modalities to reduce the uncertainty of the identified parameters. Additionally, the experimental error can impede the identification of a unique parameter set, and the cost function depends on the constitutive law. This study highlights the need for sensitivity analyses before the design of the experimental protocol. Such studies can also be used to define the acceptable range of errors in the experimental measurement. Eventually, the impact of the evaluated uncertainty of the identified parameters should be further investigated according to the purpose of the finite element modelling.
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Affiliation(s)
- Nolwenn Fougeron
- Faculty of Mechanical Engineering, Technion Institute of Technology, Haifa, Israel.
| | - Zohar Oddes
- Faculty of Mechanical Engineering, Technion Institute of Technology, Haifa, Israel
| | - Amit Ashkenazi
- Faculty of Mechanical Engineering, Technion Institute of Technology, Haifa, Israel
| | - Dana Solav
- Faculty of Mechanical Engineering, Technion Institute of Technology, Haifa, Israel
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Lee M, Woo J, Peak SH, Kim HG, Lim WS, Chung J, Lee JE, Kim JH, Park S, Kim JM, Lee JW. An exploratory clinical trial of preoperative non-invasive localization before breast-conserving surgery using augmented reality technology. Breast Cancer Res Treat 2024:10.1007/s10549-024-07272-3. [PMID: 38743175 DOI: 10.1007/s10549-024-07272-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 01/24/2024] [Indexed: 05/16/2024]
Abstract
PURPOSE This single-center, randomized, prospective, exploratory clinical trial was conducted to assess the clinical efficacy of an augmented reality (AR)-based breast cancer localization imaging solution for patients with breast cancer. METHODS This clinical trial enrolled 20 women who were diagnosed with invasive breast cancer between the ages of 19 and 80, had a single lesion with a diameter ≥ 5 mm but ≤ 30 mm, had no metastases to other organs, and had not received prior chemotherapy. All patients underwent mammography, ultrasound, computed tomography, and magnetic resonance imaging for preoperative assessment. Patients were randomly assigned to ultrasound-guided skin marking localization (USL) and AR-based localization (ARL) groups (n = 10 in each group). Statistical comparisons between USL and ARL groups were made based on demographics, radiologic features, pathological outcomes, and surgical outcomes using chi-square and Student t-tests. RESULTS Two surgeons performed breast-conserving surgery on 20 patients. Histopathologic evaluation of all patients confirmed negative margins. Two independent pathologists evaluated the marginal distances, and there were no intergroup differences in the readers' estimates (R1, 6.20 ± 4.37 vs. 5.04 ± 3.47, P = 0.519; R2, 5.10 ± 4.31 vs. 4.10 ± 2.38, P = 0.970) or the readers' average values (5.65 ± 4.19 vs. 4.57 ± 2.84, P = 0.509). In comparing the tumor plane area ratio, there was no statistically significant difference between the two groups in terms of either reader's mean values (R1, 15.90 ± 9.52 vs. 19.38 ± 14.05, P = 0.525; R2, 15.32 ± 9.48 vs. 20.83 ± 12.85, P = 0.290) or the overall mean values of two readers combined (15.56 ± 9.11 vs. 20.09 ± 13.38, P = 0.388). Convenience, safety, satisfaction, and reusability were all superior in the AR localization group (P < 0.001) based on the two surgeons' responses. CONCLUSION AR localization is an acceptable alternative to ultrasound-guided skin marking with no significant differences in surgical outcomes.
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Affiliation(s)
- Minah Lee
- Department of Radiology, School of Medicine, Ewha Womans University, Seoul, South Korea
| | - Joohyun Woo
- Division of Breast Surgery, Department of Surgery, School of Medicine, Ewha Womans University, Seoul, South Korea
| | - Se Hyun Peak
- Division of Breast Surgery, Department of Surgery, School of Medicine, Ewha Womans University, Seoul, South Korea
| | - Hyun Goo Kim
- Division of Breast Surgery, Department of Surgery, School of Medicine, Ewha Womans University, Seoul, South Korea
| | - Woo Sung Lim
- Division of Breast Surgery, Department of Surgery, School of Medicine, Ewha Womans University, Seoul, South Korea
| | - Jin Chung
- Department of Radiology, School of Medicine, Ewha Womans University, Seoul, South Korea
| | - Jee Eun Lee
- Department of Radiology, School of Medicine, Ewha Womans University, Seoul, South Korea
| | - Jeoung Hyun Kim
- Department of Radiology, School of Medicine, Ewha Womans University, Seoul, South Korea
| | - Sanghui Park
- Department of Pathology, School of Medicine, Ewha Womans University, Seoul, South Korea
| | - Ji Min Kim
- Department of Pathology, School of Medicine, Ewha Womans University, Seoul, South Korea
| | - Jun Woo Lee
- Division of Breast Surgery, Department of Surgery, School of Medicine, Ewha Womans University, Seoul, South Korea.
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Żydowicz WM, Skokowski J, Marano L, Polom K. Current Trends and Beyond Conventional Approaches: Advancements in Breast Cancer Surgery through Three-Dimensional Imaging, Virtual Reality, Augmented Reality, and the Emerging Metaverse. J Clin Med 2024; 13:915. [PMID: 38337610 PMCID: PMC10856583 DOI: 10.3390/jcm13030915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 01/25/2024] [Accepted: 02/03/2024] [Indexed: 02/12/2024] Open
Abstract
Breast cancer stands as the most prevalent cancer globally, necessitating comprehensive care. A multidisciplinary approach proves crucial for precise diagnosis and treatment, ultimately leading to effective disease management. While surgical interventions continue to evolve and remain integral for curative treatment, imaging assumes a fundamental role in breast cancer detection. Advanced imaging techniques not only facilitate improved diagnosis but also contribute significantly to the overall enhancement of breast cancer management. This review article aims to provide an overview of innovative technologies such as virtual reality, augmented reality, and three-dimensional imaging, utilized in the medical field to elevate the diagnosis and treatment of breast cancer. Additionally, the article delves into an emerging technology known as the metaverse, still under development. Through the analysis of impactful research and comparison of their findings, this study offers valuable insights into the advantages of each innovative technique. The goal is to provide physicians, surgeons, and radiologists with information on how to enhance breast cancer management.
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Affiliation(s)
- Weronika Magdalena Żydowicz
- Department of General Surgery and Surgical Oncology, “Saint Wojciech” Hospital, “Nicolaus Copernicus” Health Center, Jana Pawła II 50, 80-462 Gdańsk, Poland; (W.M.Ż.); (J.S.)
| | - Jaroslaw Skokowski
- Department of General Surgery and Surgical Oncology, “Saint Wojciech” Hospital, “Nicolaus Copernicus” Health Center, Jana Pawła II 50, 80-462 Gdańsk, Poland; (W.M.Ż.); (J.S.)
- Department of Medicine, Academy of Applied Medical and Social Sciences, Akademia Medycznych I Spolecznych Nauk Stosowanych (AMiSNS), 2 Lotnicza Street, 82-300 Elbląg, Poland;
| | - Luigi Marano
- Department of General Surgery and Surgical Oncology, “Saint Wojciech” Hospital, “Nicolaus Copernicus” Health Center, Jana Pawła II 50, 80-462 Gdańsk, Poland; (W.M.Ż.); (J.S.)
- Department of Medicine, Academy of Applied Medical and Social Sciences, Akademia Medycznych I Spolecznych Nauk Stosowanych (AMiSNS), 2 Lotnicza Street, 82-300 Elbląg, Poland;
| | - Karol Polom
- Department of Medicine, Academy of Applied Medical and Social Sciences, Akademia Medycznych I Spolecznych Nauk Stosowanych (AMiSNS), 2 Lotnicza Street, 82-300 Elbląg, Poland;
- Department of Gastrointestinal Surgical Oncology, Greater Poland Cancer Centre, Garbary 15, 61-866 Poznan, Poland
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Dołęga-Kozierowski B, Kasprzak P, Lis M, Szynglarewicz B, Matkowski R, Sawicki M, Dymek M, Szumiejko A, Carmo G, Kwiatkowski A, Soliński DG, Ptak M. Numerical and physical modeling of breast cancer based on image fusion and artificial intelligence. Breast Cancer Res Treat 2023; 202:33-43. [PMID: 37490172 PMCID: PMC10504219 DOI: 10.1007/s10549-023-07056-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 07/14/2023] [Indexed: 07/26/2023]
Abstract
PURPOSE The key problem raised in the paper is the change in the position of the breast tumor due to magnetic resonance imaging examinations in the abdominal position relative to the supine position during the surgical procedure. Changing the position of the patient leads to significant deformation of the breast, which leads to the inability to indicate the location of the neoplastic lesion correctly. METHODS This study outlines a methodological process for treating cancer patients. Pre-qualification assessments are conducted for magnetic resonance imaging (MRI), and 3D scans are taken in three positions: supine with arms raised, supine surgical position (SS), and standing. MRI and standard ultrasonography (USG) imaging are performed, and breast and cancer tissue are segmented from the MRI images. Finite element analysis is used to simulate tissue behavior in different positions, and an artificial neural network is trained to predict tumor dislocation. Based on the model, a 3D-printed breast with a highlighted tumor is manufactured. This computer-aided analysis is used to create a detailed surgical plan, and lumpectomy surgery is performed in the SS. In addition, the geometry of the tumor is presented to the medical staff as a 3D-printed element. RESULTS By utilizing a comprehensive range of techniques, including pre-qualification assessment, 3D scanning, MRI and USG imaging, segmentation of breast and cancer tissue, model analysis, image fusion, finite element analysis, artificial neural network training, and additive manufacturing, a detailed surgical plan can be created for performing lumpectomy surgery in the supine surgical position. CONCLUSION The new approach developed for the pre-operative assessment and surgical planning of breast cancer patients has demonstrated significant potential for improving the accuracy and efficacy of surgical procedures. This procedure may also help the pathomorphological justification. Moreover, transparent 3D-printed breast models can benefit breast cancer operation assistance. The physical and computational models can help surgeons visualize the breast and the tumor more accurately and detailedly, allowing them to plan the surgery with greater precision and accuracy.
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Affiliation(s)
- Bartosz Dołęga-Kozierowski
- Breast Unit, Department of Breast Imaging, Lower Silesian Oncology, Pulmonology and Hematology Center, Wroclaw, Poland
| | - Piotr Kasprzak
- Breast Unit, Department of Breast Imaging, Lower Silesian Oncology, Pulmonology and Hematology Center, Wroclaw, Poland
| | - Michał Lis
- Burn and Plastic Surgery Department, Ludwik Rydygier Memorial Specialized Hospital in Krakow, Krakow, Poland
| | - Bartłomiej Szynglarewicz
- Breast Unit, Department of Breast Imaging, Lower Silesian Oncology, Pulmonology and Hematology Center, Wroclaw, Poland
- Department of Oncology, Faculty of Medicine, Wroclaw Medical University, Wroclaw, Poland
| | - Rafał Matkowski
- Breast Unit, Department of Breast Imaging, Lower Silesian Oncology, Pulmonology and Hematology Center, Wroclaw, Poland
- Department of Oncology, Faculty of Medicine, Wroclaw Medical University, Wroclaw, Poland
| | - Marek Sawicki
- Faculty of Mechanical Engineering, Wroclaw University of Science and Technology, Lukasiewicza 7/9, 50-371 Wroclaw, Poland
| | - Mateusz Dymek
- Faculty of Mechanical Engineering, Wroclaw University of Science and Technology, Lukasiewicza 7/9, 50-371 Wroclaw, Poland
| | - Adrianna Szumiejko
- Faculty of Mechanical Engineering, Wroclaw University of Science and Technology, Lukasiewicza 7/9, 50-371 Wroclaw, Poland
| | - Gustavo Carmo
- Department of Mechanical Engineering, Centre for Mechanical Technology and Automation (TEMA), Campus Universitário de Santiago, University of Aveiro, 3810-193 Aveiro, Portugal
| | - Artur Kwiatkowski
- Department of Neurosurgery, Provincial Specialist Hospital in Legnica, Iwaszkiewicza 5, 59-220, Legnica, Poland
| | | | - Mariusz Ptak
- Faculty of Mechanical Engineering, Wroclaw University of Science and Technology, Lukasiewicza 7/9, 50-371 Wroclaw, Poland
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Harbin Z, Sohutskay D, Vanderlaan E, Fontaine M, Mendenhall C, Fisher C, Voytik-Harbin S, Tepole AB. Computational mechanobiology model evaluating healing of postoperative cavities following breast-conserving surgery. Comput Biol Med 2023; 165:107342. [PMID: 37647782 PMCID: PMC10581740 DOI: 10.1016/j.compbiomed.2023.107342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 07/07/2023] [Accepted: 08/07/2023] [Indexed: 09/01/2023]
Abstract
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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Affiliation(s)
- Zachary Harbin
- School of Mechanical Engineering Purdue University, West Lafayette, IN, USA
| | - David Sohutskay
- Weldon School of Biomedical Engineering Purdue University, West Lafayette, IN, USA; Indiana University School of Medicine, Indianapolis, IN, USA
| | - Emma Vanderlaan
- Weldon School of Biomedical Engineering Purdue University, West Lafayette, IN, USA; Indiana University School of Medicine, Indianapolis, IN, USA
| | - Muira Fontaine
- Weldon School of Biomedical Engineering Purdue University, West Lafayette, IN, USA
| | - Carly Mendenhall
- School of Mechanical Engineering Purdue University, West Lafayette, IN, USA
| | - Carla Fisher
- Indiana University School of Medicine, Indianapolis, IN, USA
| | - Sherry Voytik-Harbin
- Weldon School of Biomedical Engineering Purdue University, West Lafayette, IN, USA; Department of Basic Medical Sciences Purdue University, West Lafayette, IN, USA
| | - Adrian Buganza Tepole
- School of Mechanical Engineering Purdue University, West Lafayette, IN, USA; Weldon School of Biomedical Engineering Purdue University, West Lafayette, IN, USA.
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6
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Harbin Z, Sohutskay D, Vanderlaan E, Fontaine M, Mendenhall C, Fisher C, Voytik-Harbin S, Tepolea AB. Computational Mechanobiology Model Evaluating Healing of Postoperative Cavities Following Breast-Conserving Surgery. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.26.538467. [PMID: 37162899 PMCID: PMC10168325 DOI: 10.1101/2023.04.26.538467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
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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Affiliation(s)
- Zachary Harbin
- School of Mechanical Engineering Purdue University, West Lafayette, IN, USA
| | - David Sohutskay
- Weldon School of Biomedical Engineering Purdue University, West Lafayette, IN, USA
- Indiana University School of Medicine, Indianapolis, IN, USA
| | - Emma Vanderlaan
- Weldon School of Biomedical Engineering Purdue University, West Lafayette, IN, USA
- Indiana University School of Medicine, Indianapolis, IN, USA
| | - Muira Fontaine
- Weldon School of Biomedical Engineering Purdue University, West Lafayette, IN, USA
| | - Carly Mendenhall
- School of Mechanical Engineering Purdue University, West Lafayette, IN, USA
| | - Carla Fisher
- Indiana University School of Medicine, Indianapolis, IN, USA
| | - Sherry Voytik-Harbin
- Weldon School of Biomedical Engineering Purdue University, West Lafayette, IN, USA
- Department of Basic Medical Sciences Purdue University, West Lafayette, IN, USA
| | - Adrian Buganza Tepolea
- School of Mechanical Engineering Purdue University, West Lafayette, IN, USA
- Weldon School of Biomedical Engineering Purdue University, West Lafayette, IN, USA
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7
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Briot N, Chagnon G, Connesson N, Payan Y. In vivo measurement of breast tissues stiffness using a light aspiration device. Clin Biomech (Bristol, Avon) 2022; 99:105743. [PMID: 36099706 DOI: 10.1016/j.clinbiomech.2022.105743] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 08/12/2022] [Indexed: 02/07/2023]
Abstract
BACKGROUND This paper addresses the question of the in vivo measurement of breast tissue stiffness, which has been poorly adressed until now, except for elastography imaging which has shown promising results but which is still difficult for clinicians to use on a day-to-day basis. Estimating subject-specific tissue stiffness is indeed a critical area of research due to the development of a large number of Finite Element (FE) breast models for various medical applications. METHODS This paper proposes to use an original aspiration device, put into contact with breast surface, and to estimate tissue stiffness using an inverse analysis of the aspiration experiment. The method assumes that breast tissue is composed of a bilayered structure made of fatty and fribroglandular tissues (lower layer) superimposed with the skin (upper layer). Young moduli of both layers are therefore estimated based on repeating low intensity suction tests (<40 mbar) of breast tissues using cups of 7 different diameters. FINDINGS Seven volunteers were involved in this pilot study with average Young moduli of 56.3 kPa ± 16.4 and 3.04 kPa ± 1.17 respectively for the skin and the fatty and fibroglandular tissue. The measurements were carried out in a reasonable time scale (<60 min in total) without any discomfort perceived by the participants. These encouraging results should be confirmed in a clinical study that will include a much larger number of volunteers and patients.
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Affiliation(s)
- N Briot
- Univ. Grenoble Alpes, CNRS, UMR 5525, VetAgro Sup, Grenoble INP, TIMC, 38000 Grenoble, France.
| | - G Chagnon
- Univ. Grenoble Alpes, CNRS, UMR 5525, VetAgro Sup, Grenoble INP, TIMC, 38000 Grenoble, France
| | - N Connesson
- Univ. Grenoble Alpes, CNRS, UMR 5525, VetAgro Sup, Grenoble INP, TIMC, 38000 Grenoble, France
| | - Y Payan
- Univ. Grenoble Alpes, CNRS, UMR 5525, VetAgro Sup, Grenoble INP, TIMC, 38000 Grenoble, France
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Satisfaction survey on a preoperative explanation method using three-dimensional breast imaging for breast cancer patients considering breast-conserving surgery. Surg Today 2022; 53:476-482. [PMID: 36129539 DOI: 10.1007/s00595-022-02592-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 08/14/2022] [Indexed: 10/14/2022]
Abstract
PURPOSE Although one of the essential factors in surgical shared decision-making is the body image, the breast morphology after breast-conserving surgery is particularly difficult to explain in a uniform manner due to large individual differences. METHODS Patients with breast cancer eligible for breast-conserving surgery (BCS) were recruited between June 2020 and October 2021. We surveyed the patients' satisfaction with our method of explaining the likely breast morphology after BCS using three-dimensional (3D) breast imaging in the form of a questionnaire. RESULTS A total of 162 patients were enrolled, and 137 (84.6%) answered the questionnaire. One hundred and sixteen patients (84.6%) answered that they were very satisfied or satisfied with our explanation method, and 100 (73.0%) patients were very satisfied or satisfied with the 3D breast imaging. Some patients answered that 3D breast imaging helped them prepare for BCS, or on the contrary, made them choose mastectomy with breast reconstruction because the deformation likely with BCS was considered unacceptable. Only a few patients who underwent BCS felt that their postoperative morphology was more deformed than the preoperatively imagined one. CONCLUSION Our results suggest that our preoperative explanation method using 3D breast imaging was useful for shared decision-making.
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Viguerie A, Grave M, Barros GF, Lorenzo G, Reali A, Coutinho A. Data-Driven Simulation of Fisher-Kolmogorov Tumor Growth Models Using Dynamic Mode Decomposition. J Biomech Eng 2022; 144:1141945. [PMID: 35771166 DOI: 10.1115/1.4054925] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Indexed: 11/08/2022]
Abstract
The computer simulation of organ-scale biomechanistic models of cancer personalized via routinely collected clinical and imaging data enables to obtain patient-specific predictions of tumor growth and treatment response over the anatomy of the patient's affected organ. However, the simulation of the underlying spatiotemporal models can entail a prohibitive computational cost, which constitutes a barrier to the successful development of clinically-actionable computational technologies for personalized tumor forecasting. Here we propose to utilize Dynamic-Mode Decomposition (DMD), an unsupervised machine learning method, to construct a low-dimensional representation of cancer models and accelerate their simulation. We show that DMD may be applied to Fisher-Kolmogorov models, which constitute an established formulation to represent untreated solid tumor growth that can further accommodate other relevant cancer phenomena. Our results show that a DMD implementation of this model over a clinically-relevant parameter space can yield impressive predictions, with short to medium-term errors remaining under 1% and long-term errors remaining under 20%, despite very short training periods. In particular, we have found that, for moderate to high tumor cell diffusivity and low to moderate tumor cell proliferation rate, DMD reconstructions provide accurate, bounded-error reconstructions for all tested training periods. We posit that this data-driven approach has the potential to greatly reduce the computational overhead of personalized simulations of cancer models, thereby facilitating tumor forecasting, parameter identification, uncertainty quantification, and treatment optimization.
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Affiliation(s)
- Alex Viguerie
- Department of Mathematics, Gran Sasso Science Institute, Viale Francesco Crispi 7, L'Aquila, AQ 67100, Italy
| | - Malú Grave
- Dept. of Civil Engineering, COPPE/Federal University of Rio de Janeiro, P.O. Box 68506, RJ 21945-970, Rio de Janeiro, Brazil; Fundação Oswaldo Cruz - Fiocruz, Rua Waldemar Falcão 121, BA 40296-710, Salvador, Brazil
| | - Gabriel F Barros
- Dept. of Civil Engineering, COPPE/Federal University of Rio de Janeiro, P.O. Box 68506, RJ 21945-970, Rio de Janeiro, Brazil
| | - Guillermo Lorenzo
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 E. 24th Street, Austin, TX, 78712-1229, USA; Dipartimento di Ingegneria Civile ed Architettura, Università di Pavia, Via Ferrata 3, Pavia, PV 27100, Italy
| | - Alessandro Reali
- Dipartimento di Ingegneria Civile ed Architettura, Università di Pavia, Via Ferrata 3, Pavia, PV 27100, Italy
| | - Alvaro Coutinho
- Dept. of Civil Engineering, COPPE/Federal University of Rio de Janeiro, P.O. Box 68506, RJ 21945-970, Rio de Janeiro, Brazil
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Wu C, Lorenzo G, Hormuth DA, Lima EABF, Slavkova KP, DiCarlo JC, Virostko J, Phillips CM, Patt D, Chung C, Yankeelov TE. Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology. BIOPHYSICS REVIEWS 2022; 3:021304. [PMID: 35602761 PMCID: PMC9119003 DOI: 10.1063/5.0086789] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 04/29/2022] [Indexed: 12/11/2022]
Abstract
Digital twins employ mathematical and computational models to virtually represent a physical object (e.g., planes and human organs), predict the behavior of the object, and enable decision-making to optimize the future behavior of the object. While digital twins have been widely used in engineering for decades, their applications to oncology are only just emerging. Due to advances in experimental techniques quantitatively characterizing cancer, as well as advances in the mathematical and computational sciences, the notion of building and applying digital twins to understand tumor dynamics and personalize the care of cancer patients has been increasingly appreciated. In this review, we present the opportunities and challenges of applying digital twins in clinical oncology, with a particular focus on integrating medical imaging with mechanism-based, tissue-scale mathematical modeling. Specifically, we first introduce the general digital twin framework and then illustrate existing applications of image-guided digital twins in healthcare. Next, we detail both the imaging and modeling techniques that provide practical opportunities to build patient-specific digital twins for oncology. We then describe the current challenges and limitations in developing image-guided, mechanism-based digital twins for oncology along with potential solutions. We conclude by outlining five fundamental questions that can serve as a roadmap when designing and building a practical digital twin for oncology and attempt to provide answers for a specific application to brain cancer. We hope that this contribution provides motivation for the imaging science, oncology, and computational communities to develop practical digital twin technologies to improve the care of patients battling cancer.
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Affiliation(s)
- Chengyue Wu
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas 78712, USA
| | | | | | | | - Kalina P. Slavkova
- Department of Physics, The University of Texas at Austin, Austin, Texas 78712, USA
| | | | | | - Caleb M. Phillips
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas 78712, USA
| | - Debra Patt
- Texas Oncology, Austin, Texas 78731, USA
| | - Caroline Chung
- Department of Radiation Oncology, MD Anderson Cancer Center, University of Texas, Houston, Texas 77030, USA
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A new visual design language for biological structures in a cell. Structure 2022; 30:485-497.e3. [DOI: 10.1016/j.str.2022.01.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 09/28/2021] [Accepted: 01/04/2022] [Indexed: 01/16/2023]
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12
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Singh GD, Singh M. Virtual Surgical Planning: Modeling from the Present to the Future. J Clin Med 2021; 10:jcm10235655. [PMID: 34884359 PMCID: PMC8658225 DOI: 10.3390/jcm10235655] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 10/19/2021] [Accepted: 11/23/2021] [Indexed: 11/16/2022] Open
Abstract
Virtual surgery planning is a non-invasive procedure, which uses digital clinical data for diagnostic, procedure selection and treatment planning purposes, including the forecast of potential outcomes. The technique begins with 3D data acquisition, using various methods, which may or may not utilize ionizing radiation, such as 3D stereophotogrammetry, 3D cone-beam CT scans, etc. Regardless of the imaging technique selected, landmark selection, whether it is manual or automated, is the key to transforming clinical data into objects that can be interrogated in virtual space. As a prerequisite, the data require alignment and correspondence such that pre- and post-operative configurations can be compared in real and statistical shape space. In addition, these data permit predictive modeling, using either model-based, data-based or hybrid modeling. These approaches provide perspectives for the development of customized surgical procedures and medical devices with accuracy, precision and intelligence. Therefore, this review briefly summarizes the current state of virtual surgery planning.
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Affiliation(s)
- G. Dave Singh
- Virtual Craniofacial Laboratory, Stanford University, Stanford, CA 94301, USA
- Correspondence: ; Tel.: +1-720-924-9929
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13
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Godden AR, Micha A, Wolf LM, Pitches C, Barry PA, Khan AA, Krupa KDC, Kirby AM, Rusby JE. Three-dimensional simulation of aesthetic outcome from breast-conserving surgery compared with viewing photographs or standard care: randomized clinical trial. Br J Surg 2021; 108:1181-1188. [PMID: 34370833 PMCID: PMC10364871 DOI: 10.1093/bjs/znab217] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 05/06/2021] [Indexed: 11/13/2022]
Abstract
INTRODUCTION Over half of women with surgically managed breast cancer in the UK undergo breast-conserving treatment (BCT). While photographs are shown prior to reconstructive surgery or complex oncoplastic procedures, standard practice prior to breast conservation is to simply describe the likely aesthetic changes. Patients have expressed the desire for more personalized information about likely appearance after surgery. The hypothesis was that viewing a three-dimensional (3D) simulation improves patients' confidence in knowing their likely aesthetic outcome after surgery. METHODS A randomized, controlled trial of 117 women planning unilateral BCT was undertaken. The randomization was three-way: standard of care (verbal description alone, control group), viewing two-dimensional (2D) photographs, or viewing a 3D simulation before surgery. The primary endpoint was the comparison between groups' median answer on a visual analogue scale (VAS) for the question administered before surgery: 'How confident are you that you know how your breasts are likely to look after treatment?' RESULTS The median VAS in the control group was 5.2 (i.q.r. 2.6-7.8); 8.0 (i.q.r. 5.7-8.7) for 2D photography, and 8.9 (i.q.r. 8.2-9.5) for 3D simulation. There was a significant difference between groups (P < 0.010) with post-hoc pairwise comparisons demonstrating a statistically significant difference between 3D simulation and both standard care and viewing 2D photographs (P < 0.010 and P = 0.012, respectively). CONCLUSION This RCT has demonstrated that women who viewed an individualized 3D simulation of likely aesthetic outcome for BCT were more confident going into surgery than those who received standard care or who were shown 2D photographs of other women. The impact on longer-term satisfaction with outcome remains to be determined.Registration number: NCT03250260 (http://www.clinicaltrials.gov).
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Affiliation(s)
- A R Godden
- Department of Breast Surgery, Royal Marsden NHS Foundation Trust, Sutton, Surrey, UK
- Independent patient co-designer, Institute of Cancer Research, Sutton, Surrey, UK
| | - A Micha
- Department of Breast Surgery, Royal Marsden NHS Foundation Trust, Sutton, Surrey, UK
| | - L M Wolf
- Department of Breast Surgery, Royal Marsden NHS Foundation Trust, Sutton, Surrey, UK
| | - C Pitches
- Independent patient co-designer, Institute of Cancer Research, Sutton, Surrey, UK
| | - P A Barry
- Department of Breast Surgery, Royal Marsden NHS Foundation Trust, Sutton, Surrey, UK
| | - A A Khan
- Department of Plastic Surgery, The Royal Marsden NHS Foundation Trust, London, UK
| | - K D C Krupa
- Department of Breast Surgery, Royal Marsden NHS Foundation Trust, Sutton, Surrey, UK
| | - A M Kirby
- Department of Breast Surgery, Royal Marsden NHS Foundation Trust, Sutton, Surrey, UK
- Independent patient co-designer, Institute of Cancer Research, Sutton, Surrey, UK
| | - J E Rusby
- Department of Breast Surgery, Royal Marsden NHS Foundation Trust, Sutton, Surrey, UK
- Independent patient co-designer, Institute of Cancer Research, Sutton, Surrey, UK
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14
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Sen A, Fowlkes NW, Kingsley CV, Kulp AM, Huynh T, Willis BJ, Brewer Savannah KJ, Bordes MCA, Hwang KP, McCulloch MM, Stafford RJ, Contreras A, Reece G, Brock KK. Technical Note: Histological validation of anatomical imaging for breast modeling using a novel cryo-microtome. Med Phys 2021; 48:7323-7332. [PMID: 34559413 DOI: 10.1002/mp.15245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Revised: 08/27/2021] [Accepted: 09/14/2021] [Indexed: 11/05/2022] Open
Abstract
PURPOSE Precise correlation between three-dimensional (3D) imaging and histology can aid biomechanical modeling of the breast. We develop a framework to register ex vivo images to histology using a novel cryo-fluorescence tomography (CFT) device. METHODS A formalin-fixed cadaveric breast specimen, including chest wall, was subjected to high-resolution magnetic resonance (MR) imaging. The specimen was then frozen and embedded in an optimal cutting temperature (OCT) compound. The OCT block was placed in a CFT device with an overhead camera and 50 μm thick slices were successively shaved off the block. After each shaving, the block-face was photographed. At select locations including connective/adipose tissue, muscle, skin, and fibroglandular tissue, 20 μm sections were transferred onto cryogenic tape for manual hematoxylin and eosin staining, histological assessment, and image capture. A 3D white-light image was automatically reconstructed from the photographs by aligning fiducial markers embedded in the OCT block. The 3D MR image, 3D white-light image, and photomicrographs were rigidly registered. Target registration errors (TREs) were computed based on 10 pairs of points marked at fibroglandular intersections. The overall MR-histology registration was used to compare the MR intensities at tissue extraction sites with a one-way analysis of variance. RESULTS The MR image to CFT-captured white-light image registration achieved a mean TRE of 0.73 ± 0.25 mm (less than the 1 mm MR slice resolution). The block-face white-light image and block-face photomicrograph registration showed visually indistinguishable alignment of anatomical structures and tissue boundaries. The MR intensities at the four tissue sites identified from histology differed significantly (p < 0.01). Each tissue pair, except the skin-connective/adipose tissue pair, also had significantly different MR intensities (p < 0.01). CONCLUSIONS Fine sectioning in a highly controlled imaging/sectioning environment enables accurate registration between the MR image and histology. Statistically significant differences in MR signal intensities between histological tissues are indicators for the specificity of correlation between MRI and histology.
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Affiliation(s)
- Anando Sen
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Natalie W Fowlkes
- Department of Veterinary Medicine & Surgery, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Charles V Kingsley
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Adam M Kulp
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Thomas Huynh
- Department of Veterinary Medicine & Surgery, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Brandy J Willis
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Kari J Brewer Savannah
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Mary Catherine A Bordes
- Department of Plastic Surgery, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Ken-Pin Hwang
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Molly M McCulloch
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Roger Jason Stafford
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Alejandro Contreras
- Department of Pathology, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Gregory Reece
- Department of Plastic Surgery, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Kristy K Brock
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA.,Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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15
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Dempsey SCH, O'Hagan JJ, Samani A. Measurement of the hyperelastic properties of 72 normal homogeneous and heterogeneous ex vivo breast tissue samples. J Mech Behav Biomed Mater 2021; 124:104794. [PMID: 34496308 DOI: 10.1016/j.jmbbm.2021.104794] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Revised: 06/10/2021] [Accepted: 08/21/2021] [Indexed: 12/24/2022]
Abstract
The mechanical properties of normal soft tissues, including breast tissue, have been of interest to the biomedical research community as there are many clinical and industrial applications that can benefit from quantitative information characterizing such properties. For instance, computer assisted surgery planning, elastography for breast cancer diagnosis, and bra design can all involve biomechanical modeling of the breast to predict its deformation or stress distribution. It is known that most biological soft tissues, including breast tissue, exhibit nonlinear mechanical response over large strains. As such, it is necessary to model such tissues as hyperelastic. In this work, we used indentation testing to estimate the hyperelastic parameters of 4 models (3rd order Ogden, 5-term polynomial, Veronda-Westman and Yeoh) estimated from 72 healthy ex vivo breast tissue samples covering adipose, fibroglandular, and mixed tissue. All estimated parameter sets were confirmed to represent stable material using Drucker's stability criterion. We observed that all three tissue types were statistically similar solidifying the use of homogenous breast modelling over large strain simulation.
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Affiliation(s)
- Sergio C H Dempsey
- School of Biomedical Engineering, University of Western Ontario, London, ON, Canada
| | - Joseph J O'Hagan
- School of Biomedical Engineering, University of Western Ontario, London, ON, Canada
| | - Abbas Samani
- Department of Electrical and Computer Engineering, University of Western Ontario, London, ON, Canada; School of Biomedical Engineering, University of Western Ontario, London, ON, Canada; Department of Medical Biophysics, University of Western Ontario, London, ON, Canada; Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada.
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16
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Gouveia PF, Costa J, Morgado P, Kates R, Pinto D, Mavioso C, Anacleto J, Martinho M, Lopes DS, Ferreira AR, Vavourakis V, Hadjicharalambous M, Silva MA, Papanikolaou N, Alves C, Cardoso F, Cardoso MJ. Breast cancer surgery with augmented reality. Breast 2021; 56:14-17. [PMID: 33548617 PMCID: PMC7890000 DOI: 10.1016/j.breast.2021.01.004] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 01/18/2021] [Accepted: 01/20/2021] [Indexed: 11/30/2022] Open
Abstract
Introduction: Innovations in 3D spatial technology and augmented reality imaging driven by digital high-tech industrial science have accelerated experimental advances in breast cancer imaging and the development of medical procedures aimed to reduce invasiveness. Presentation of case: A 57-year-old post-menopausal woman presented with screen-detected left-sided breast cancer. After undergoing all staging and pre-operative studies the patient was proposed for conservative breast surgery with tumor localization. During surgery, an experimental digital and non-invasive intra-operative localization method with augmented reality was compared with the standard pre-operative localization with carbon tattooing (institutional protocol). The breast surgeon wearing an augmented reality headset (Hololens) was able to visualize the tumor location projection inside the patient’s left breast in the usual supine position. Discussion: This work describes, to our knowledge, the first experimental test with a digital non-invasive method for intra-operative breast cancer localization using augmented reality to guide breast conservative surgery. In this case, a successful overlap of the previous standard pre-operative marks with carbon tattooing and tumor visualization inside the patient’s breast with augmented reality was obtained. Conclusion: Breast cancer conservative guided surgery with augmented reality can pave the way for a digital non-invasive method for intra-operative tumor localization.
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Affiliation(s)
- Pedro F Gouveia
- Breast Unit, Champalimaud Clinical Centre/Champalimaud Foundation,Avenida Brasilia, 1400-038, Lisboa, Portugal; Faculty of Medicine, Lisbon University,Avenida Professor Egas Moniz, 1649-028, Lisboa, Portugal.
| | - Joana Costa
- Breast Unit, Champalimaud Clinical Centre/Champalimaud Foundation,Avenida Brasilia, 1400-038, Lisboa, Portugal.
| | - Pedro Morgado
- AI4medimaging,Rua do Parque Poente, Lote 35, 4705-002, Braga, Portugal.
| | - Ronald Kates
- Breast Unit, Champalimaud Clinical Centre/Champalimaud Foundation,Avenida Brasilia, 1400-038, Lisboa, Portugal.
| | - David Pinto
- Breast Unit, Champalimaud Clinical Centre/Champalimaud Foundation,Avenida Brasilia, 1400-038, Lisboa, Portugal.
| | - Carlos Mavioso
- Breast Unit, Champalimaud Clinical Centre/Champalimaud Foundation,Avenida Brasilia, 1400-038, Lisboa, Portugal.
| | - João Anacleto
- Breast Unit, Champalimaud Clinical Centre/Champalimaud Foundation,Avenida Brasilia, 1400-038, Lisboa, Portugal.
| | - Marta Martinho
- Breast Unit, Champalimaud Clinical Centre/Champalimaud Foundation,Avenida Brasilia, 1400-038, Lisboa, Portugal.
| | - Daniel Simões Lopes
- INESC ID, Instituto Superior Técnico, Lisbon University,Rua Alves Redol 9, 1000-029, Lisboa, Portugal.
| | - Arlindo R Ferreira
- Breast Unit, Champalimaud Clinical Centre/Champalimaud Foundation,Avenida Brasilia, 1400-038, Lisboa, Portugal; Faculty of Medicine, Lisbon University,Avenida Professor Egas Moniz, 1649-028, Lisboa, Portugal.
| | - Vasileios Vavourakis
- Department of Mechanical & Manufacturing Engineering, University of Cyprus,Dept. of Mechanical & Manufacturing Engineering University of Cyprus, Cyprus; Department of Medical Physics & Biomedical Engineering, University College London,Malet Place Engineering Building, University College London, Gower Street, London, WC1E 6BT, United Kingdom.
| | - Myrianthi Hadjicharalambous
- Department of Mechanical & Manufacturing Engineering, University of Cyprus,Dept. of Mechanical & Manufacturing Engineering University of Cyprus, Cyprus.
| | - Marco A Silva
- Microsoft Corporation (Portugal),Rua do Fogo de Santelmo, Lote 2.07.02, Lisboa, Portugal.
| | - Nickolas Papanikolaou
- Breast Unit, Champalimaud Clinical Centre/Champalimaud Foundation,Avenida Brasilia, 1400-038, Lisboa, Portugal.
| | - Celeste Alves
- Breast Unit, Champalimaud Clinical Centre/Champalimaud Foundation,Avenida Brasilia, 1400-038, Lisboa, Portugal.
| | - Fatima Cardoso
- Breast Unit, Champalimaud Clinical Centre/Champalimaud Foundation,Avenida Brasilia, 1400-038, Lisboa, Portugal.
| | - Maria João Cardoso
- Breast Unit, Champalimaud Clinical Centre/Champalimaud Foundation,Avenida Brasilia, 1400-038, Lisboa, Portugal; NOVA Medical School, Campo dos Mártires da Pátria 130, 1169-056, Lisboa, Portugal.
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17
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Hajhashemkhani M, Hematiyan MR. The identification of the unloaded configuration of breast tissue with unknown non-homogenous stiffness parameters using surface measured data in deformed configuration. Comput Biol Med 2020; 128:104107. [PMID: 33220593 DOI: 10.1016/j.compbiomed.2020.104107] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2020] [Revised: 10/16/2020] [Accepted: 11/04/2020] [Indexed: 01/09/2023]
Abstract
Large deformation analysis of the breast is known as a useful approach for locating the tumor and treatment strategies of breast cancer, for which knowing the breast stiffness parameters and unloaded configuration is crucial to obtain reliable results. In this study, an iterative inverse finite element algorithm is developed to identify the unloaded configuration of the breast while its stiffness constants are unknown and its internal structure is assumed to be non-homogeneous. The position vector of surface points in the deformed configuration of the breast is employed to obtain the unknowns of the inverse problem. An objective function based on the difference between the position vector of the calculated and measured deformed configurations is defined. Thereafter, the objective function is minimized using a gradient-based method. The sensitivity analysis for material parameters is performed using an analytic direct differentiation approach. Through several numerical examples, the effectiveness of the proposed inverse method for identifying the unloaded configuration of a uniform, a computational breast phantom with a single inclusion as well as a computational breast phantom with randomly distributed stiffness, is demonstrated. The effects of the number of load cases, measurement error, and initial guesses on the results of the inverse problem are investigated, as well. It is observed that the unloaded configuration of the computational breast phantom with a single inclusion or heterogeneous breast tissues can be accurately found by considering an equivalent homogenous material for the tissue.
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Affiliation(s)
- M Hajhashemkhani
- Department of Mechanical Engineering, Shiraz University, Shiraz, 71936, Iran
| | - M R Hematiyan
- Department of Mechanical Engineering, Shiraz University, Shiraz, 71936, Iran.
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18
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Diab M, Kumaraswamy N, Reece GP, Hanson SE, Fingeret MC, Markey MK, Ravi-Chandar K. Characterization of human female breast and abdominal skin elasticity using a bulge test. J Mech Behav Biomed Mater 2020; 103:103604. [PMID: 32090931 DOI: 10.1016/j.jmbbm.2019.103604] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Revised: 11/15/2019] [Accepted: 12/20/2019] [Indexed: 11/19/2022]
Abstract
Characterization of material properties of human skin is required to develop a physics-based biomechanical model that can predict deformation of female breast after cosmetic and reconstructive surgery. In this paper, we have adopted an experimental approach to characterize the biaxial response of human skin using bulge tests. Skin specimens were harvested from breast and abdominal skin of female subjects who underwent mastectomy and/or reconstruction at The University of Texas MD Anderson Cancer Center and who provided informed consent. The specimens were tested within 2 h of harvest, and after freezing for different time periods but not exceeding 6 months. Our experimental results show that storage in a freezer at -20 °C for up to about 40 days does not lead to changes in the mechanical response of the skin beyond statistical variation. Moreover, displacement at the apex of the bulged specimen versus applied pressure varies significantly between different specimens from the same subject and from different subjects. The bulge test results were used in an inverse optimization procedure in order to calibrate two different constitutive material models - the angular integration model proposed by Lanir (1983) and the generalized structure tensor formulation of Gasser et al. (2006). The material parameters were estimated through a cost function that penalized deviations of the displacement and principal curvatures at the apex. Generally, acceptable fits were obtained with both models, although the angular integration model was able to fit the curvatures slightly better than the Gasser et al. model. The range of the model parameters has been extracted for use in physics-based biomechanical models of the breast.
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Affiliation(s)
- Mazen Diab
- Department of Aerospace Engineering & Engineering Mechanics, The University of Texas at Austin, Austin, TX, USA; Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA.
| | - Nishamathi Kumaraswamy
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Gregory P Reece
- Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Summer E Hanson
- Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Michelle C Fingeret
- Department of Behavioral Science, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Mia K Markey
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Krishnaswamy Ravi-Chandar
- Department of Aerospace Engineering & Engineering Mechanics, The University of Texas at Austin, Austin, TX, USA
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19
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Esslinger D, Bacher N, Rapp P, Preibsch H, Tarin C, Sawodny O, Brucker SY, Hahn M. Finite Element Breast Simulation for Sonography Image Registration. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:7100-7106. [PMID: 31947473 DOI: 10.1109/embc.2019.8857282] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
In case of female breast cancer, a breast conserving excision is often necessary. For this purpose, information from multiple medical imaging techniques have to be combined. Sonography imaging is essential for dense breast tissue and the only medical imaging technique available during surgery. During sonography of the outer breast quadrants the woman is usually in contralateral posterior oblique position, being in supine orientation while holding her ipsilateral arm over the head. Thus, these images cannot be directly registered with MRI or mammography images because these imaging technologies are performed in other patient positions with hands on the side of the body. Thus, we present a novel Finite Element approach how to enable a sonography image registration by showing the first time how to transfer the supine position with the arm straight on side into a supine position with the ipsilateral arm over the head which can be used to include information from MRI or mammography images. This approach is shown and validated with 3D scanner breast surface data as proof of concept. When comparing the simulation result with a 3D surface scan in supine orientation with the arm over the head, a mean surface distance error of 1.57 mm is achieved.
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20
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Bessa S, Gouveia PF, Carvalho PH, Rodrigues C, Silva NL, Cardoso F, Cardoso JS, Oliveira HP, Cardoso MJ. 3D digital breast cancer models with multimodal fusion algorithms. Breast 2020; 49:281-290. [PMID: 31986378 PMCID: PMC7375583 DOI: 10.1016/j.breast.2019.12.016] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 12/23/2019] [Accepted: 12/27/2019] [Indexed: 11/17/2022] Open
Abstract
Breast cancer image fusion consists of registering and visualizing different sets of a patient synchronized torso and radiological images into a 3D model. Breast spatial interpretation and visualization by the treating physician can be augmented with a patient-specific digital breast model that integrates radiological images. But the absence of a ground truth for a good correlation between surface and radiological information has impaired the development of potential clinical applications. A new image acquisition protocol was designed to acquire breast Magnetic Resonance Imaging (MRI) and 3D surface scan data with surface markers on the patient’s breasts and torso. A patient-specific digital breast model integrating the real breast torso and the tumor location was created and validated with a MRI/3D surface scan fusion algorithm in 16 breast cancer patients. This protocol was used to quantify breast shape differences between different modalities, and to measure the target registration error of several variants of the MRI/3D scan fusion algorithm. The fusion of single breasts without the biomechanical model of pose transformation had acceptable registration errors and accurate tumor locations. The performance of the fusion algorithm was not affected by breast volume. Further research and virtual clinical interfaces could lead to fast integration of this fusion technology into clinical practice. MRI/3D surface scan fusion algorithm to create 3D breast cancer models. A replicable clinical validation protocol for MRI/3D surface scan fusion algorithms. Anthropometric study that quantifies breast deformations by area in MRI and 3D scans.
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Affiliation(s)
- Sílvia Bessa
- INESC TEC, Portugal; University of Porto, Portugal.
| | - Pedro F Gouveia
- Champalimaud Foundation, Portugal; Medical School, Lisbon University, Portugal
| | | | | | - Nuno L Silva
- Champalimaud Foundation, Portugal; Nova Medical School, Portugal
| | | | | | | | - Maria João Cardoso
- INESC TEC, Portugal; Champalimaud Foundation, Portugal; Nova Medical School, Portugal
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21
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A novel finite element model-based navigation system-supported workflow for breast tumor excision. Med Biol Eng Comput 2019; 57:1537-1552. [PMID: 30980230 DOI: 10.1007/s11517-019-01977-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Accepted: 03/27/2019] [Indexed: 10/27/2022]
Abstract
In the case of female breast cancer, a breast-conserving excision is often desirable. This surgery is based on preoperatively gathered MRI, mammography, and sonography images. These images are recorded in multiple patient positions, e. g., 2D mammography images in standing position with a compressed breast and 3D MRI images in prone position. In contrast, the surgery happens in supine or beach chair position. Due to these different perspectives and the flexible, thus challenging, breast tissue, the excision puts high demands on the physician. Therefore, this publication presents a novel eight-step excision support workflow that can be used to include information captured preoperatively through medical imaging based on a finite element (FE) model. In addition, an indoor positioning system is integrated in the workflow in order to track surgical devices and the sonography transducer during surgery. The preoperative part of the navigation system-supported workflow is outlined exemplarily based on first experimental results including 3D scans of a patient in different patient positions and her MRI images. Graphical Abstract Finite Element model based navigation system supported workflow for breast tumor excision is based on eight steps and allows inclusion of information from medical images recorded in multiple patient positions.
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22
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Torii R, Velliou RI, Hodgson D, Mudera V. Modelling multi-scale cell-tissue interaction of tissue-engineered muscle constructs. J Tissue Eng 2018; 9:2041731418787141. [PMID: 30128109 PMCID: PMC6090492 DOI: 10.1177/2041731418787141] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2018] [Accepted: 06/12/2018] [Indexed: 01/21/2023] Open
Abstract
Expectation on engineered tissue substitute continues to grow, and for an effective development of a functional tissue and to control its quality, cellular mechanoresponse plays a key role. Although the mechanoresponse – in terms of cell–tissue interaction across scales – has been understood better in recent years, there are still technical limitations to quantitatively monitor the processes involved in the development of both native and engineered tissues. Computational (in silico) studies have been utilised to complement the experimental limitations and successfully applied to the prediction of tissue growth. We here review recent activities in the area of combined experimental and computational analyses of tissue growth, especially in the tissue engineering context, and highlight the advantages of such an approach for the future of the tissue engineering, using our own case study of predicting musculoskeletal tissue engineering construct development.
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Affiliation(s)
- Ryo Torii
- Department of Mechanical Engineering, University College London, London, UK
| | | | - David Hodgson
- Centre for Computation, Mathematics and Physics in the Life Sciences and Experimental Biology (COMPLEX), University College London, London, UK.,Clinical Operational Research Unit, Department of Mathematics, University College London, London, UK
| | - Vivek Mudera
- Division of Surgery and Interventional Science, University College London, London, UK
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23
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A Regression Model for Predicting Shape Deformation after Breast Conserving Surgery. SENSORS 2018; 18:s18010167. [PMID: 29315279 PMCID: PMC5795402 DOI: 10.3390/s18010167] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Revised: 01/03/2018] [Accepted: 01/05/2018] [Indexed: 01/12/2023]
Abstract
Breast cancer treatments can have a negative impact on breast aesthetics, in case when surgery is intended to intersect tumor. For many years mastectomy was the only surgical option, but more recently breast conserving surgery (BCS) has been promoted as a liable alternative to treat cancer while preserving most part of the breast. However, there is still a significant number of BCS intervened patients who are unpleasant with the result of the treatment, which leads to self-image issues and emotional overloads. Surgeons recognize the value of a tool to predict the breast shape after BCS to facilitate surgeon/patient communication and allow more educated decisions; however, no such tool is available that is suited for clinical usage. These tools could serve as a way of visually sensing the aesthetic consequences of the treatment. In this research, it is intended to propose a methodology for predict the deformation after BCS by using machine learning techniques. Nonetheless, there is no appropriate dataset containing breast data before and after surgery in order to train a learning model. Therefore, an in-house semi-synthetic dataset is proposed to fulfill the requirement of this research. Using the proposed dataset, several learning methodologies were investigated, and promising outcomes are obtained.
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