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Pan F, Khoo K, Maso Talou GD, Song F, McGhee D, Doyle AJ, Nielsen PMF, Nash MP, Babarenda Gamage TP. Quantifying changes in shoulder orientation between the prone and supine positions from magnetic resonance imaging. Clin Biomech (Bristol, Avon) 2024; 111:106157. [PMID: 38103526 DOI: 10.1016/j.clinbiomech.2023.106157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 11/22/2023] [Accepted: 11/24/2023] [Indexed: 12/19/2023]
Abstract
BACKGROUND Predicting breast tissue motion using biomechanical models can provide navigational guidance during breast cancer treatment procedures. These models typically do not account for changes in posture between procedures. Difference in shoulder position can alter the shape of the pectoral muscles and breast. A greater understanding of the differences in the shoulder orientation between prone and supine could improve the accuracy of breast biomechanical models. METHODS 19 landmarks were placed on the sternum, clavicle, scapula, and humerus of the shoulder girdle in prone and supine breast MRIs (N = 10). These landmarks were used in an optimization framework to fit subject-specific skeletal models and compare joint angles of the shoulder girdle between these positions. FINDINGS The mean Euclidean distance between joint locations from the fitted skeletal model and the manually identified joint locations was 15.7 mm ± 2.7 mm. Significant differences were observed between prone and supine. Compared to supine position, the shoulder girdle in the prone position had the lateral end of the clavicle in more anterior translation (i.e., scapula more protracted) (P < 0.05), the scapula in more protraction (P < 0.01), the scapula in more upward rotation (associated with humerus elevation) (P < 0.05); and the humerus more elevated (P < 0.05) for both the left and right sides. INTERPRETATION Shoulder girdle orientation was found to be different between prone and supine. These differences would affect the shape of multiple pectoral muscles, which would affect breast shape and the accuracy of biomechanical models.
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Affiliation(s)
- Fangchao Pan
- Auckland Bioengineering Institute, University of Auckland, New Zealand
| | - Kejia Khoo
- Auckland Bioengineering Institute, University of Auckland, New Zealand
| | | | - Freda Song
- Faculty of Medical and Health Sciences, University of Auckland, New Zealand
| | - Deirdre McGhee
- Biomechanics Research Laboratory, University of Wollongong, NSW, Australia
| | - Anthony J Doyle
- Anatomy and Medical Imaging, Faculty of Medical and Health Sciences, University of Auckland, New Zealand; Te Whatu Ora, Health New Zealand, New Zealand
| | - Poul M F Nielsen
- Auckland Bioengineering Institute, University of Auckland, New Zealand; Department of Engineering Science and Biomedical Engineering, University of Auckland, New Zealand
| | - Martyn P Nash
- Auckland Bioengineering Institute, University of Auckland, New Zealand; Department of Engineering Science and Biomedical Engineering, University of Auckland, New Zealand
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2
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Mazier A, Bordas SPA. Breast simulation pipeline: From medical imaging to patient-specific simulations. Clin Biomech (Bristol, Avon) 2024; 111:106153. [PMID: 38061204 DOI: 10.1016/j.clinbiomech.2023.106153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 11/17/2023] [Accepted: 11/20/2023] [Indexed: 01/16/2024]
Abstract
BACKGROUND Breast-conserving surgery is the most acceptable operation for breast cancer removal from an invasive and psychological point of view. Before the surgical procedure, a preoperative MRI is performed in the prone configuration, while the surgery is achieved in the supine position. This leads to a considerable movement of the breast, including the tumor, between the two poses, complicating the surgeon's task. METHODS In this work, a simulation pipeline allowing the computation of patient-specific geometry and the prediction of personalized breast material properties was put forward. Through image segmentation, a finite element model including the subject-specific geometry is established. By first computing an undeformed state of the breast, the geometrico-material model is calibrated by surface acquisition in the intra-operative stance. FINDINGS Using an elastic corotational formulation, the patient-specific mechanical properties of the breast and skin were identified to obtain the best estimates of the supine configuration. The final results are a shape-fitting closest point residual of 4.00 mm for the mechanical parameters Ebreast=0.32 kPa and Eskin=22.72 kPa, congruent with the current state-of-the-art. The Covariance Matrix Adaptation Evolution Strategy optimizer converges on average between 5 to 30 min depending on the initial parameters, reaching a simulation speed of 20 s. To our knowledge, our model offers one of the best compromises between accuracy and speed. INTERPRETATION Satisfactory results were obtained for the estimation of breast deformation from preoperative to intra-operative configuration. Furthermore, we have demonstrated the clinical feasibility of such applications using a simulation framework that aims at the smallest disturbance of the actual surgical pipeline.
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Affiliation(s)
- Arnaud Mazier
- Institute of Computational Engineering, Department of Engineering, Université du Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Stéphane P A Bordas
- Institute of Computational Engineering, Department of Engineering, Université du Luxembourg, Esch-sur-Alzette, Luxembourg.
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Carreras J, Roncador G, Hamoudi R. Artificial Intelligence Predicted Overall Survival and Classified Mature B-Cell Neoplasms Based on Immuno-Oncology and Immune Checkpoint Panels. Cancers (Basel) 2022; 14:5318. [PMID: 36358737 PMCID: PMC9657332 DOI: 10.3390/cancers14215318] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 10/20/2022] [Accepted: 10/24/2022] [Indexed: 08/01/2023] Open
Abstract
Artificial intelligence (AI) can identify actionable oncology biomarkers. This research integrates our previous analyses of non-Hodgkin lymphoma. We used gene expression and immunohistochemical data, focusing on the immune checkpoint, and added a new analysis of macrophages, including 3D rendering. The AI comprised machine learning (C5, Bayesian network, C&R, CHAID, discriminant analysis, KNN, logistic regression, LSVM, Quest, random forest, random trees, SVM, tree-AS, and XGBoost linear and tree) and artificial neural networks (multilayer perceptron and radial basis function). The series included chronic lymphocytic leukemia, mantle cell lymphoma, follicular lymphoma, Burkitt, diffuse large B-cell lymphoma, marginal zone lymphoma, and multiple myeloma, as well as acute myeloid leukemia and pan-cancer series. AI classified lymphoma subtypes and predicted overall survival accurately. Oncogenes and tumor suppressor genes were highlighted (MYC, BCL2, and TP53), along with immune microenvironment markers of tumor-associated macrophages (M2-like TAMs), T-cells and regulatory T lymphocytes (Tregs) (CD68, CD163, MARCO, CSF1R, CSF1, PD-L1/CD274, SIRPA, CD85A/LILRB3, CD47, IL10, TNFRSF14/HVEM, TNFAIP8, IKAROS, STAT3, NFKB, MAPK, PD-1/PDCD1, BTLA, and FOXP3), apoptosis (BCL2, CASP3, CASP8, PARP, and pathway-related MDM2, E2F1, CDK6, MYB, and LMO2), and metabolism (ENO3, GGA3). In conclusion, AI with immuno-oncology markers is a powerful predictive tool. Additionally, a review of recent literature was made.
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Affiliation(s)
- Joaquim Carreras
- Department of Pathology, School of Medicine, Tokai University, 143 Shimokasuya, Isehara 259-1193, Kanagawa, Japan
| | - Giovanna Roncador
- Monoclonal Antibodies Unit, Spanish National Cancer Research Center (Centro Nacional de Investigaciones Oncologicas, CNIO), Melchor Fernandez Almagro 3, 28029 Madrid, Spain
| | - Rifat Hamoudi
- Department of Clinical Sciences, College of Medicine, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
- Division of Surgery and Interventional Science, University College London, Gower Street, London WC1E 6BT, UK
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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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Basurto-Hurtado JA, Cruz-Albarran IA, Toledano-Ayala M, Ibarra-Manzano MA, Morales-Hernandez LA, Perez-Ramirez CA. Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms. Cancers (Basel) 2022; 14:3442. [PMID: 35884503 PMCID: PMC9322973 DOI: 10.3390/cancers14143442] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 07/02/2022] [Accepted: 07/12/2022] [Indexed: 02/04/2023] Open
Abstract
Breast cancer is one the main death causes for women worldwide, as 16% of the diagnosed malignant lesions worldwide are its consequence. In this sense, it is of paramount importance to diagnose these lesions in the earliest stage possible, in order to have the highest chances of survival. While there are several works that present selected topics in this area, none of them present a complete panorama, that is, from the image generation to its interpretation. This work presents a comprehensive state-of-the-art review of the image generation and processing techniques to detect Breast Cancer, where potential candidates for the image generation and processing are presented and discussed. Novel methodologies should consider the adroit integration of artificial intelligence-concepts and the categorical data to generate modern alternatives that can have the accuracy, precision and reliability expected to mitigate the misclassifications.
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Affiliation(s)
- Jesus A. Basurto-Hurtado
- C.A. Mecatrónica, Facultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro, Rio Moctezuma 249, San Cayetano, San Juan del Rio 76807, Mexico; (J.A.B.-H.); (I.A.C.-A.)
- Laboratorio de Dispositivos Médicos, Facultad de Ingeniería, Universidad Autónoma de Querétaro, Carretera a Chichimequillas S/N, Ejido Bolaños, Santiago de Querétaro 76140, Mexico
| | - Irving A. Cruz-Albarran
- C.A. Mecatrónica, Facultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro, Rio Moctezuma 249, San Cayetano, San Juan del Rio 76807, Mexico; (J.A.B.-H.); (I.A.C.-A.)
- Laboratorio de Dispositivos Médicos, Facultad de Ingeniería, Universidad Autónoma de Querétaro, Carretera a Chichimequillas S/N, Ejido Bolaños, Santiago de Querétaro 76140, Mexico
| | - Manuel Toledano-Ayala
- División de Investigación y Posgrado de la Facultad de Ingeniería (DIPFI), Universidad Autónoma de Querétaro, Cerro de las Campanas S/N Las Campanas, Santiago de Querétaro 76010, Mexico;
| | - Mario Alberto Ibarra-Manzano
- Laboratorio de Procesamiento Digital de Señales, Departamento de Ingeniería Electrónica, Division de Ingenierias Campus Irapuato-Salamanca (DICIS), Universidad de Guanajuato, Carretera Salamanca-Valle de Santiago KM. 3.5 + 1.8 Km., Salamanca 36885, Mexico;
| | - Luis A. Morales-Hernandez
- C.A. Mecatrónica, Facultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro, Rio Moctezuma 249, San Cayetano, San Juan del Rio 76807, Mexico; (J.A.B.-H.); (I.A.C.-A.)
| | - Carlos A. Perez-Ramirez
- Laboratorio de Dispositivos Médicos, Facultad de Ingeniería, Universidad Autónoma de Querétaro, Carretera a Chichimequillas S/N, Ejido Bolaños, Santiago de Querétaro 76140, Mexico
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Galstyan A, Bunker MJ, Lobo F, Sims R, Inziello J, Stubbs J, Mukhtar R, Kelil T. Applications of 3D printing in breast cancer management. 3D Print Med 2021; 7:6. [PMID: 33559793 PMCID: PMC7871648 DOI: 10.1186/s41205-021-00095-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 01/31/2021] [Indexed: 12/24/2022] Open
Abstract
Three-dimensional (3D) printing is a method by which two-dimensional (2D) virtual data is converted to 3D objects by depositing various raw materials into successive layers. Even though the technology was invented almost 40 years ago, a rapid expansion in medical applications of 3D printing has only been observed in the last few years. 3D printing has been applied in almost every subspecialty of medicine for pre-surgical planning, production of patient-specific surgical devices, simulation, and training. While there are multiple review articles describing utilization of 3D printing in various disciplines, there is paucity of literature addressing applications of 3D printing in breast cancer management. Herein, we review the current applications of 3D printing in breast cancer management and discuss the potential impact on future practices.
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Affiliation(s)
- Arpine Galstyan
- University of California, 1600 Divisadero St, C250, Box 1667, San Francisco, CA, 94115, USA.,Department of Radiology, Center for Advanced 3D Technologies, 1600 Divisadero St, C250, Box 1667, San Francisco, CA, 94115, USA
| | - Michael J Bunker
- University of California, 1600 Divisadero St, C250, Box 1667, San Francisco, CA, 94115, USA.,Department of Radiology, Center for Advanced 3D Technologies, 1600 Divisadero St, C250, Box 1667, San Francisco, CA, 94115, USA
| | - Fluvio Lobo
- University of Florida, 3100 Technology Pkwy, Orlando, FL, 32826, USA
| | - Robert Sims
- University of Florida, 3100 Technology Pkwy, Orlando, FL, 32826, USA
| | - James Inziello
- University of Florida, 3100 Technology Pkwy, Orlando, FL, 32826, USA
| | - Jack Stubbs
- University of Florida, 3100 Technology Pkwy, Orlando, FL, 32826, USA
| | - Rita Mukhtar
- University of California, 1600 Divisadero St, C250, Box 1667, San Francisco, CA, 94115, USA.,Department of Surgery, University of California, 1600 Divisadero St, C250, Box 1667, San Francisco, CA, 94115, USA
| | - Tatiana Kelil
- University of California, 1600 Divisadero St, C250, Box 1667, San Francisco, CA, 94115, USA. .,Department of Radiology, Center for Advanced 3D Technologies, 1600 Divisadero St, C250, Box 1667, San Francisco, CA, 94115, USA.
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Cammarota G, Ianiro G, Ahern A, Carbone C, Temko A, Claesson MJ, Gasbarrini A, Tortora G. Gut microbiome, big data and machine learning to promote precision medicine for cancer. Nat Rev Gastroenterol Hepatol 2020; 17:635-648. [PMID: 32647386 DOI: 10.1038/s41575-020-0327-3] [Citation(s) in RCA: 137] [Impact Index Per Article: 34.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/02/2020] [Indexed: 12/13/2022]
Abstract
The gut microbiome has been implicated in cancer in several ways, as specific microbial signatures are known to promote cancer development and influence safety, tolerability and efficacy of therapies. The 'omics' technologies used for microbiome analysis continuously evolve and, although much of the research is still at an early stage, large-scale datasets of ever increasing size and complexity are being produced. However, there are varying levels of difficulty in realizing the full potential of these new tools, which limit our ability to critically analyse much of the available data. In this Perspective, we provide a brief overview on the role of gut microbiome in cancer and focus on the need, role and limitations of a machine learning-driven approach to analyse large amounts of complex health-care information in the era of big data. We also discuss the potential application of microbiome-based big data aimed at promoting precision medicine in cancer.
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Affiliation(s)
- Giovanni Cammarota
- Gastroenterology Department, Fondazione Policlinico Universitario Agostino Gemelli-IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy.
| | - Gianluca Ianiro
- Gastroenterology Department, Fondazione Policlinico Universitario Agostino Gemelli-IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Anna Ahern
- School of Microbiology and APC Microbiome Ireland, University College Cork, Cork, Ireland
| | - Carmine Carbone
- Oncology Department, Fondazione Policlinico Universitario Agostino Gemelli-IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Andriy Temko
- School of Engineering, University College Cork, Cork, Ireland.,Qualcomm ML R&D, Cork, Ireland
| | - Marcus J Claesson
- School of Microbiology and APC Microbiome Ireland, University College Cork, Cork, Ireland
| | - Antonio Gasbarrini
- Gastroenterology Department, Fondazione Policlinico Universitario Agostino Gemelli-IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Giampaolo Tortora
- Oncology Department, Fondazione Policlinico Universitario Agostino Gemelli-IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy
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Abstract
This theme issue of
Interface Focus
is the first of two sets of articles on the topic of bioengineering in women's health. Although there is a long history of collaboration between engineers and medical professionals in orthopaedics and cardiovascular medicine, there has been growing interest in the last decade for interdisciplinary collaborations in other areas of medical science. This growth is particularly true in the case of women's health, a traditionally underserved area of research in the scientific community where fundamental knowledge of female physiology is still needed. Women's health is a broad category encompassing reproduction, fertility, maternal health, normal and abnormal pregnancy and the sequelae associated with a difficult childbirth. Women's health also includes sex-associated pathology associated with cancer, pain, cardiac disease, osteoporosis and other diseases. This list is not exhaustive with new scientific frontiers developing based on the evolving discourse of medicine for all. This first issue in the series focuses on bioengineering advances in the study of the non-pregnant woman, and the articles highlight important developments in pelvic floor disorders, biomedical devices, fertility, breast implant failure and breast cancer. The second issue in the series focuses on pregnancy.
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Affiliation(s)
- Kristin S. Miller
- Biomedical Engineering, Tulane University, 500 Lindy Boggs Center, New Orleans, LA 70118, USA
| | - Kristin Myers
- Mechanical Engineering, Columbia University, New York, NY 10025, USA
| | - Michelle Oyen
- Department of Engineering, East Carolina University, Greenville, NC, USA
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