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Toniolo I, Pirini P, Perretta S, Carniel EL, Berardo A. Endoscopic versus laparoscopic bariatric procedures: A computational biomechanical study through a patient-specific approach. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 243:107889. [PMID: 37944398 DOI: 10.1016/j.cmpb.2023.107889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 10/25/2023] [Accepted: 10/25/2023] [Indexed: 11/12/2023]
Abstract
BACKGROUND AND OBJECTIVES Within the framework of computational biomechanics, finite element models of the gastric district could be seen as a potential clinical tool not only to study the effects apported by bariatric surgery, but also to compare different surgical techniques such as the new emerging Endoscopic Sleeve Gastroplasty (ESG) with respect to well-established ones (such as the Laparoscopic Sleeve Gastrectomy, LSG). METHODS This work realized a fully computational comparison between the outcomes obtained from 10 patient-specific stomach models, which were used to simulate ESG, and the complementary results obtained from models representing the post-LSG of the same subjects. Specifically, once the ESG was simulated, a mechanical stimulus was applied by increasing an intragastric pressure up to a maximum of 5 kPa, in order to replicate the process of food intake, as well as for post-LSG models. RESULTS Results revealed non negligible differences between the techniques also within the same subject. In particular, not only LSG could lead to a greater reduction in the stomach volume (about 77 % at baseline, which is strictly linked to weight loss), but also influence the gastric distension (12 % less than pre-operative models). On the contrary, if ESG would be performed, a more similar pre-operative mechanical stimulation of the gastric walls may be seen (difference of about 1 %), thus preserving the mechanosensation, but the detriment of the volume reduction (about 56 % at baseline, and even decreases with increasing pressure). Moreover, since results suggested ESG may be more influenced by the pre-operative gastric cavity than LSG, a predictive model was proposed to support the surgical planning and the estimation of the volume reduction after ESG. CONCLUSIONS ESG and LSG have substantial differences in their protocols and post-surgical effects. This work pointed out that variations between the two procedures may be observed also from a computational point of view, especially when including patient-specific geometries. These insights support gastric modelling as a valuable tool to evaluate, design and critically compare emerging bariatric surgical procedures, not only from empirical aspects and clinical outcomes, but also from a mechanical point of view.
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Affiliation(s)
- Ilaria Toniolo
- Department of Civil, Environmental and Architectural Engineering, University of Padova, Italy; Centre for Mechanics of Biological Materials, University of Padova, Italy
| | - Paola Pirini
- Department of Civil, Environmental and Architectural Engineering, University of Padova, Italy
| | - Silvana Perretta
- IHU Strasbourg, Strasbourg, France; IRCAD France, Strasbourg, France; Department of Digestive and Endocrine Surgery, NHC, Strasbourg, France
| | - Emanuele Luigi Carniel
- Centre for Mechanics of Biological Materials, University of Padova, Italy; Department of Industrial Engineering, University of Padova, Italy.
| | - Alice Berardo
- Department of Civil, Environmental and Architectural Engineering, University of Padova, Italy; Centre for Mechanics of Biological Materials, University of Padova, Italy; Department of Biomedical Sciences, University of Padova, Italy.
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Segar DJ, Bernstock JD, Arnaout O, Bi WL, Friedman GK, Langer R, Traverso G, Rampersad SM. Modeling of intracranial tumor treating fields for the treatment of complex high-grade gliomas. Sci Rep 2023; 13:1636. [PMID: 36717682 PMCID: PMC9886948 DOI: 10.1038/s41598-023-28769-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 01/24/2023] [Indexed: 02/01/2023] Open
Abstract
Increasing the intensity of tumor treating fields (TTF) within a tumor bed improves clinical efficacy, but reaching sufficiently high field intensities to achieve growth arrest remains challenging due in part to the insulating nature of the cranium. Using MRI-derived finite element models (FEMs) and simulations, we optimized an exhaustive set of intracranial electrode locations to obtain maximum TTF intensities in three clinically challenging high-grade glioma (HGG) cases (i.e., thalamic, left temporal, brainstem). Electric field strengths were converted into therapeutic enhancement ratios (TER) to evaluate the predicted impact of stimulation on tumor growth. Concurrently, conventional transcranial configurations were simulated/optimized for comparison. Optimized intracranial TTF were able to achieve field strengths that have previously been shown capable of inducing complete growth arrest, in 98-100% of the tumor volumes using only 0.54-0.64 A current. The reconceptualization of TTF as a targeted, intracranial therapy has the potential to provide a meaningful survival benefit to patients with HGG and other brain tumors, including those in surgically challenging, deep, or anatomically eloquent locations which may preclude surgical resection. Accordingly, such an approach may ultimately represent a paradigm shift in the use of TTFs for the treatment of brain cancer.
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Affiliation(s)
- David J Segar
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Joshua D Bernstock
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. .,David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Omar Arnaout
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Wenya Linda Bi
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Gregory K Friedman
- Department of Pediatrics, University of Alabama at Birmingham, Birmingham, USA
| | - Robert Langer
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Giovanni Traverso
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.,Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.,Division of Gastroenterology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Sumientra M Rampersad
- Department of Physics, University of Massachusetts, Boston, MA, USA.,Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA
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Narayan A, Liu Z, Bergquist JA, Charlebois C, Rampersad S, Rupp L, Brooks D, White D, Tate J, MacLeod RS. UncertainSCI: Uncertainty quantification for computational models in biomedicine and bioengineering. Comput Biol Med 2023; 152:106407. [PMID: 36521358 PMCID: PMC9812870 DOI: 10.1016/j.compbiomed.2022.106407] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 11/07/2022] [Accepted: 12/03/2022] [Indexed: 12/12/2022]
Abstract
BACKGROUND Computational biomedical simulations frequently contain parameters that model physical features, material coefficients, and physiological effects, whose values are typically assumed known a priori. Understanding the effect of variability in those assumed values is currently a topic of great interest. A general-purpose software tool that quantifies how variation in these parameters affects model outputs is not broadly available in biomedicine. For this reason, we developed the 'UncertainSCI' uncertainty quantification software suite to facilitate analysis of uncertainty due to parametric variability. METHODS We developed and distributed a new open-source Python-based software tool, UncertainSCI, which employs advanced parameter sampling techniques to build polynomial chaos (PC) emulators that can be used to predict model outputs for general parameter values. Uncertainty of model outputs is studied by modeling parameters as random variables, and model output statistics and sensitivities are then easily computed from the emulator. Our approaches utilize modern, near-optimal techniques for sampling and PC construction based on weighted Fekete points constructed by subsampling from a suitably randomized candidate set. RESULTS Concentrating on two test cases-modeling bioelectric potentials in the heart and electric stimulation in the brain-we illustrate the use of UncertainSCI to estimate variability, statistics, and sensitivities associated with multiple parameters in these models. CONCLUSION UncertainSCI is a powerful yet lightweight tool enabling sophisticated probing of parametric variability and uncertainty in biomedical simulations. Its non-intrusive pipeline allows users to leverage existing software libraries and suites to accurately ascertain parametric uncertainty in a variety of applications.
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Affiliation(s)
- Akil Narayan
- Scientific Computing and Imaging Institute, University of Utah, 72 Central Campus Dr, Salt Lake City, UT, 84112, United States; Department of Mathematics, University of Utah, 72 Central Campus Dr, Salt Lake City, UT, 84112, United States.
| | - Zexin Liu
- Scientific Computing and Imaging Institute, University of Utah, 72 Central Campus Dr, Salt Lake City, UT, 84112, United States; Department of Mathematics, University of Utah, 72 Central Campus Dr, Salt Lake City, UT, 84112, United States
| | - Jake A Bergquist
- Scientific Computing and Imaging Institute, University of Utah, 72 Central Campus Dr, Salt Lake City, UT, 84112, United States; Department of Biomedical Engineering, University of Utah, 72 Central Campus Dr, Salt Lake City, UT, 84112, United States
| | - Chantel Charlebois
- Scientific Computing and Imaging Institute, University of Utah, 72 Central Campus Dr, Salt Lake City, UT, 84112, United States; Department of Biomedical Engineering, University of Utah, 72 Central Campus Dr, Salt Lake City, UT, 84112, United States
| | - Sumientra Rampersad
- Department of Physics, University of Massachusetts Boston, Boston, MA, USA; Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA
| | - Lindsay Rupp
- Scientific Computing and Imaging Institute, University of Utah, 72 Central Campus Dr, Salt Lake City, UT, 84112, United States; Department of Biomedical Engineering, University of Utah, 72 Central Campus Dr, Salt Lake City, UT, 84112, United States
| | - Dana Brooks
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA
| | - Dan White
- Scientific Computing and Imaging Institute, University of Utah, 72 Central Campus Dr, Salt Lake City, UT, 84112, United States; Department of Biomedical Engineering, University of Utah, 72 Central Campus Dr, Salt Lake City, UT, 84112, United States
| | - Jess Tate
- Scientific Computing and Imaging Institute, University of Utah, 72 Central Campus Dr, Salt Lake City, UT, 84112, United States; Department of Biomedical Engineering, University of Utah, 72 Central Campus Dr, Salt Lake City, UT, 84112, United States
| | - Rob S MacLeod
- Scientific Computing and Imaging Institute, University of Utah, 72 Central Campus Dr, Salt Lake City, UT, 84112, United States; Department of Biomedical Engineering, University of Utah, 72 Central Campus Dr, Salt Lake City, UT, 84112, United States
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Anderson DN, Charlebois CM, Smith EH, Arain AM, Davis TS, Rolston JD. Probabilistic comparison of gray and white matter coverage between depth and surface intracranial electrodes in epilepsy. Sci Rep 2021; 11:24155. [PMID: 34921176 PMCID: PMC8683494 DOI: 10.1038/s41598-021-03414-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 11/23/2021] [Indexed: 11/20/2022] Open
Abstract
In this study, we quantified the coverage of gray and white matter during intracranial electroencephalography in a cohort of epilepsy patients with surface and depth electrodes. We included 65 patients with strip electrodes (n = 12), strip and grid electrodes (n = 24), strip, grid, and depth electrodes (n = 7), or depth electrodes only (n = 22). Patient-specific imaging was used to generate probabilistic gray and white matter maps and atlas segmentations. Gray and white matter coverage was quantified using spherical volumes centered on electrode centroids, with radii ranging from 1 to 15 mm, along with detailed finite element models of local electric fields. Gray matter coverage was highly dependent on the chosen radius of influence (RoI). Using a 2.5 mm RoI, depth electrodes covered more gray matter than surface electrodes; however, surface electrodes covered more gray matter at RoI larger than 4 mm. White matter coverage and amygdala and hippocampal coverage was greatest for depth electrodes at all RoIs. This study provides the first probabilistic analysis to quantify coverage for different intracranial recording configurations. Depth electrodes offer increased coverage of gray matter over other recording strategies if the desired signals are local, while subdural grids and strips sample more gray matter if the desired signals are diffuse.
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Affiliation(s)
- Daria Nesterovich Anderson
- Department of Neurosurgery, University of Utah, Salt Lake City, UT, USA. .,Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT, USA.
| | - Chantel M Charlebois
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
| | - Elliot H Smith
- Department of Neurosurgery, University of Utah, Salt Lake City, UT, USA
| | - Amir M Arain
- Department of Neurology, University of Utah, Salt Lake City, UT, USA
| | - Tyler S Davis
- Department of Neurosurgery, University of Utah, Salt Lake City, UT, USA
| | - John D Rolston
- Department of Neurosurgery, University of Utah, Salt Lake City, UT, USA. .,Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA.
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