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Elton E, Strelez C, Ung N, Perez R, Ghaffarian K, Hixon D, Matasci N, Mumenthaler SM. A novel thin plate spline methodology to model tissue surfaces and quantify tumor cell invasion in organ-on-chip models. SLAS DISCOVERY : ADVANCING LIFE SCIENCES R & D 2024; 29:100163. [PMID: 38796111 PMCID: PMC11199902 DOI: 10.1016/j.slasd.2024.100163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 05/21/2024] [Accepted: 05/22/2024] [Indexed: 05/28/2024]
Abstract
Organ-on-chip (OOC) models can be useful tools for cancer drug discovery. Advances in OOC technology have led to the development of more complex assays, yet analysis of these systems does not always account for these advancements, resulting in technical challenges. A challenging task in the analysis of these two-channel microfluidic models is to define the boundary between the channels so objects moving within and between channels can be quantified. We propose a novel imaging-based application of a thin plate spline method - a generalized cubic spline that can be used to model coordinate transformations - to model a tissue boundary and define compartments for quantification of invaded objects, representing the early steps in cancer metastasis. To evaluate its performance, we applied our analytical approach to an adapted OOC developed by Emulate, Inc., utilizing a two-channel system with endothelial cells in the bottom channel and colorectal cancer (CRC) patient-derived organoids (PDOs) in the top channel. Initial application and visualization of this method revealed boundary variations due to microscope stage tilt and ridge and valley-like contours in the endothelial tissue surface. The method was functionalized into a reproducible analytical process and web tool - the Chip Invasion and Contour Analysis (ChICA) - to model the endothelial surface and quantify invading tumor cells across multiple chips. To illustrate applicability of the analytical method, we applied the tool to CRC organoid-chips seeded with two different endothelial cell types and measured distinct variations in endothelial surfaces and tumor cell invasion dynamics. Since ChICA utilizes only positional data output from imaging software, the method is applicable to and agnostic of the imaging tool and image analysis system used. The novel thin plate spline method developed in ChICA can account for variation introduced in OOC manufacturing or during the experimental workflow, can quickly and accurately measure tumor cell invasion, and can be used to explore biological mechanisms in drug discovery.
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Affiliation(s)
| | - Carly Strelez
- Ellison Institute of Technology, Los Angeles, CA, USA
| | - Nolan Ung
- Ellison Institute of Technology, Los Angeles, CA, USA
| | - Rachel Perez
- Ellison Institute of Technology, Los Angeles, CA, USA
| | | | | | - Naim Matasci
- Ellison Institute of Technology, Los Angeles, CA, USA
| | - Shannon M Mumenthaler
- Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA; Department of Medicine, Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Ellison Institute of Technology, Los Angeles, CA, USA.
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Elton E, Strelez C, Ung N, Perez R, Ghaffarian K, Matasci N, Mumenthaler SM. A novel thin plate spline methodology to model tissue surfaces and quantify tumor cell invasion in organ-on-chip models. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.20.567272. [PMID: 38045424 PMCID: PMC10690199 DOI: 10.1101/2023.11.20.567272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Organ-on-chip (OOC) models can be useful tools for cancer drug discovery. Advances in OOC technology have led to the development of more complex assays, yet analysis of these systems does not always account for these advancements, resulting in technical challenges. A challenging task in the analysis of these two-channel microfluidic models is to define the boundary between the channels so objects moving within and between channels can be quantified. We propose a novel imaging-based application of a thin plate spline method - a generalized cubic spline that can be used to model coordinate transformations - to model a tissue boundary and define compartments for quantification of invaded objects, representing the early steps in cancer metastasis. To evaluate its performance, we applied our analytical approach to an adapted OOC developed by Emulate, Inc., utilizing a two-channel system with endothelial cells in the bottom channel and colorectal cancer (CRC) patient-derived organoids (PDOs) in the top channel. Initial application and visualization of this method revealed boundary variations due to microscope stage tilt and ridge and valley-like contours in the endothelial tissue surface. The method was functionalized into a reproducible analytical process and web tool - the Chip Invasion and Contour Analysis (ChICA) - to model the endothelial surface and quantify invading tumor cells across multiple chips. To illustrate applicability of the analytical method, we applied the tool to CRC organoid-chips seeded with two different endothelial cell types and measured distinct variations in endothelial surfaces and tumor cell invasion dynamics. Since ChICA utilizes only positional data output from imaging software, the method is applicable to and agnostic of the imaging tool and image analysis system used. The novel thin plate spline method developed in ChICA can account for variation introduced in OOC manufacturing or during the experimental workflow, can quickly and accurately measure tumor cell invasion, and can be used to explore biological mechanisms in drug discovery.
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Affiliation(s)
| | | | - Nolan Ung
- Ellison Institute of Technology, Los Angeles, CA
| | - Rachel Perez
- Ellison Institute of Technology, Los Angeles, CA
| | | | - Naim Matasci
- Ellison Institute of Technology, Los Angeles, CA
| | - Shannon M Mumenthaler
- Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA
- Department of Medicine, Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA
- Ellison Institute of Technology, Los Angeles, CA
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Agadi K, Dominari A, Tebha SS, Mohammadi A, Zahid S. Neurosurgical Management of Cerebrospinal Tumors in the Era of Artificial Intelligence : A Scoping Review. J Korean Neurosurg Soc 2023; 66:632-641. [PMID: 35831137 PMCID: PMC10641423 DOI: 10.3340/jkns.2021.0213] [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: 08/23/2021] [Revised: 10/06/2021] [Accepted: 03/14/2022] [Indexed: 11/27/2022] Open
Abstract
Central nervous system tumors are identified as tumors of the brain and spinal cord. The associated morbidity and mortality of cerebrospinal tumors are disproportionately high compared to other malignancies. While minimally invasive techniques have initiated a revolution in neurosurgery, artificial intelligence (AI) is expediting it. Our study aims to analyze AI's role in the neurosurgical management of cerebrospinal tumors. We conducted a scoping review using the Arksey and O'Malley framework. Upon screening, data extraction and analysis were focused on exploring all potential implications of AI, classification of these implications in the management of cerebrospinal tumors. AI has enhanced the precision of diagnosis of these tumors, enables surgeons to excise the tumor margins completely, thereby reducing the risk of recurrence, and helps to make a more accurate prediction of the patient's prognosis than the conventional methods. AI also offers real-time training to neurosurgeons using virtual and 3D simulation, thereby increasing their confidence and skills during procedures. In addition, robotics is integrated into neurosurgery and identified to increase patient outcomes by making surgery less invasive. AI, including machine learning, is rigorously considered for its applications in the neurosurgical management of cerebrospinal tumors. This field requires further research focused on areas clinically essential in improving the outcome that is also economically feasible for clinical use. The authors suggest that data analysts and neurosurgeons collaborate to explore the full potential of AI.
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Affiliation(s)
- Kuchalambal Agadi
- Division of Research and Academic Affairs, Larkin Health System, South Miami, FL, USA
| | - Asimina Dominari
- Division of Research and Academic Affairs, Larkin Health System, South Miami, FL, USA
- Aristotle University of Thessaloniki School of Medicine, Thessaloniki, Greece
| | - Sameer Saleem Tebha
- Division of Research and Academic Affairs, Larkin Health System, South Miami, FL, USA
- Department of Neurosurgery and Neurology, Jinnah Medical and Dental College, Karachi, Pakistan
| | - Asma Mohammadi
- Division of Research and Academic Affairs, Larkin Health System, South Miami, FL, USA
| | - Samina Zahid
- Division of Research and Academic Affairs, Larkin Health System, South Miami, FL, USA
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Wei R, Chen H, Cai Y, Chen J. Application of intraoperative ultrasound in the resection of high-grade gliomas. Front Neurol 2023; 14:1240150. [PMID: 37965171 PMCID: PMC10640994 DOI: 10.3389/fneur.2023.1240150] [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: 06/14/2023] [Accepted: 09/19/2023] [Indexed: 11/16/2023] Open
Abstract
The incidence of gliomas is approximately 3-5/100,000, with high-grade gliomas accounting for approximately 30-40% of these tumors. Surgery is a confirmed positive factor in prolonging the survival of these patients, and a larger resection range means a longer survival time. Therefore, surgery for high-grade glioma patients should aim to maximize the extent of resection while preserving neurological function to achieve a better quality of life. There is consensus regarding the need to lengthen progression-free survival (PFS) and overall survival (OS) times. In glioma surgery, methods such as intraoperative computed tomography (ICT), intraoperative magnetic resonance imaging (IMRI), navigation, 5-aminolevulinic acid (5-ALA), and intraoperative ultrasound (IOUS) are used to achieve an expanded resection during the surgical procedure. IOUS has been increasingly used in the surgery of high-grade gliomas and various tumors due to its convenient intraoperative use, its flexible repeatability, and the relatively low cost of operating room construction. With the continuous upgrading of ultrasound equipment, IOUS has been able to better assist surgeons in achieving an increased extent of resection. This review aims to summarize the application of ultrasound in the surgery of high-grade gliomas in the past decade, its improvement in patient prognosis, and its prospects.
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Affiliation(s)
- RenJie Wei
- Department of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Hao Chen
- Department of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - YuXiang Cai
- Department of Pathology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - JingCao Chen
- Department of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, China
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Bastos DCDA, Juvekar P, Tie Y, Jowkar N, Pieper S, Wells WM, Bi WL, Golby A, Frisken S, Kapur T. Challenges and Opportunities of Intraoperative 3D Ultrasound With Neuronavigation in Relation to Intraoperative MRI. Front Oncol 2021; 11:656519. [PMID: 34026631 PMCID: PMC8139191 DOI: 10.3389/fonc.2021.656519] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 04/09/2021] [Indexed: 11/15/2022] Open
Abstract
Introduction Neuronavigation greatly improves the surgeons ability to approach, assess and operate on brain tumors, but tends to lose its accuracy as the surgery progresses and substantial brain shift and deformation occurs. Intraoperative MRI (iMRI) can partially address this problem but is resource intensive and workflow disruptive. Intraoperative ultrasound (iUS) provides real-time information that can be used to update neuronavigation and provide real-time information regarding the resection progress. We describe the intraoperative use of 3D iUS in relation to iMRI, and discuss the challenges and opportunities in its use in neurosurgical practice. Methods We performed a retrospective evaluation of patients who underwent image-guided brain tumor resection in which both 3D iUS and iMRI were used. The study was conducted between June 2020 and December 2020 when an extension of a commercially available navigation software was introduced in our practice enabling 3D iUS volumes to be reconstructed from tracked 2D iUS images. For each patient, three or more 3D iUS images were acquired during the procedure, and one iMRI was acquired towards the end. The iUS images included an extradural ultrasound sweep acquired before dural incision (iUS-1), a post-dural opening iUS (iUS-2), and a third iUS acquired immediately before the iMRI acquisition (iUS-3). iUS-1 and preoperative MRI were compared to evaluate the ability of iUS to visualize tumor boundaries and critical anatomic landmarks; iUS-3 and iMRI were compared to evaluate the ability of iUS for predicting residual tumor. Results Twenty-three patients were included in this study. Fifteen patients had tumors located in eloquent or near eloquent brain regions, the majority of patients had low grade gliomas (11), gross total resection was achieved in 12 patients, postoperative temporary deficits were observed in five patients. In twenty-two iUS was able to define tumor location, tumor margins, and was able to indicate relevant landmarks for orientation and guidance. In sixteen cases, white matter fiber tracts computed from preoperative dMRI were overlaid on the iUS images. In nineteen patients, the EOR (GTR or STR) was predicted by iUS and confirmed by iMRI. The remaining four patients where iUS was not able to evaluate the presence or absence of residual tumor were recurrent cases with a previous surgical cavity that hindered good contact between the US probe and the brainsurface. Conclusion This recent experience at our institution illustrates the practical benefits, challenges, and opportunities of 3D iUS in relation to iMRI.
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Affiliation(s)
| | - Parikshit Juvekar
- Department of Neurosurgery, Brigham and Womens Hospital, Harvard Medical School, Boston, MA, United States
| | - Yanmei Tie
- Department of Neurosurgery, Brigham and Womens Hospital, Harvard Medical School, Boston, MA, United States
| | - Nick Jowkar
- Department of Neurosurgery, Brigham and Womens Hospital, Harvard Medical School, Boston, MA, United States
| | - Steve Pieper
- Department of Neurosurgery, Brigham and Womens Hospital, Harvard Medical School, Boston, MA, United States
| | - Willam M Wells
- Department of Neurosurgery, Brigham and Womens Hospital, Harvard Medical School, Boston, MA, United States
| | - Wenya Linda Bi
- Department of Neurosurgery, Brigham and Womens Hospital, Harvard Medical School, Boston, MA, United States
| | - Alexandra Golby
- Department of Neurosurgery, Brigham and Womens Hospital, Harvard Medical School, Boston, MA, United States
| | - Sarah Frisken
- Department of Neurosurgery, Brigham and Womens Hospital, Harvard Medical School, Boston, MA, United States
| | - Tina Kapur
- Department of Neurosurgery, Brigham and Womens Hospital, Harvard Medical School, Boston, MA, United States
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Gerard IJ, Kersten-Oertel M, Hall JA, Sirhan D, Collins DL. Brain Shift in Neuronavigation of Brain Tumors: An Updated Review of Intra-Operative Ultrasound Applications. Front Oncol 2021; 10:618837. [PMID: 33628733 PMCID: PMC7897668 DOI: 10.3389/fonc.2020.618837] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2020] [Accepted: 12/22/2020] [Indexed: 11/25/2022] Open
Abstract
Neuronavigation using pre-operative imaging data for neurosurgical guidance is a ubiquitous tool for the planning and resection of oncologic brain disease. These systems are rendered unreliable when brain shift invalidates the patient-image registration. Our previous review in 2015, Brain shift in neuronavigation of brain tumours: A review offered a new taxonomy, classification system, and a historical perspective on the causes, measurement, and pre- and intra-operative compensation of this phenomenon. Here we present an updated review using the same taxonomy and framework, focused on the developments of intra-operative ultrasound-based brain shift research from 2015 to the present (2020). The review was performed using PubMed to identify articles since 2015 with the specific words and phrases: “Brain shift” AND “Ultrasound”. Since 2015, the rate of publication of intra-operative ultrasound based articles in the context of brain shift has increased from 2–3 per year to 8–10 per year. This efficient and low-cost technology and increasing comfort among clinicians and researchers have allowed unique avenues of development. Since 2015, there has been a trend towards more mathematical advancements in the field which is often validated on publicly available datasets from early intra-operative ultrasound research, and may not give a just representation to the intra-operative imaging landscape in modern image-guided neurosurgery. Focus on vessel-based registration and virtual and augmented reality paradigms have seen traction, offering new perspectives to overcome some of the different pitfalls of ultrasound based technologies. Unfortunately, clinical adaptation and evaluation has not seen as significant of a publication boost. Brain shift continues to be a highly prevalent pitfall in maintaining accuracy throughout oncologic neurosurgical intervention and continues to be an area of active research. Intra-operative ultrasound continues to show promise as an effective, efficient, and low-cost solution for intra-operative accuracy management. A major drawback of the current research landscape is that mathematical tool validation based on retrospective data outpaces prospective clinical evaluations decreasing the strength of the evidence. The need for newer and more publicly available clinical datasets will be instrumental in more reliable validation of these methods that reflect the modern intra-operative imaging in these procedures.
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Affiliation(s)
- Ian J Gerard
- Department of Radiation Oncology, McGill University Health Centre, Montreal, QC, Canada
| | | | - Jeffery A Hall
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Denis Sirhan
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - D Louis Collins
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
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Abstract
This article discusses intraoperative imaging techniques used during high-grade glioma surgery. Gliomas can be difficult to differentiate from surrounding tissue during surgery. Intraoperative imaging helps to alleviate problems encountered during glioma surgery, such as brain shift and residual tumor. There are a variety of modalities available all of which aim to give the surgeon more information, address brain shift, identify residual tumor, and increase the extent of surgical resection. The article starts with a brief introduction followed by a review of with the latest advances in intraoperative ultrasound, intraoperative MRI, and intraoperative computed tomography.
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Affiliation(s)
- Thomas Noh
- Department of Neurosurgery, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA; Hawaii Pacific Health, John A Burns School of Medicine, Honolulu, Hawaii, USA
| | - Martina Mustroph
- Department of Neurosurgery, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA; Harvard Medical School, Boston, Massachusetts, USA
| | - Alexandra J Golby
- Department of Neurosurgery, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA; Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA.
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Luo M, Larson PS, Martin AJ, Miga MI. Accounting for Deformation in Deep Brain Stimulation Surgery With Models: Comparison to Interventional Magnetic Resonance Imaging. IEEE Trans Biomed Eng 2020; 67:2934-2944. [PMID: 32078527 DOI: 10.1109/tbme.2020.2974102] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The efficacy of deep brain stimulation (DBS) depends on electrode placement accuracy, which can be jeopardized by brain shift due to burr hole and dura opening during surgery. Brain shift violates assumed rigid alignment between preoperative image and intraoperative anatomy, negatively impacting therapy. OBJECTIVE This study presents a deformation-atlas biomechanical model-based approach to address shift. METHODS Six patients, who underwent interventional magnetic resonance (iMR) image-guided DBS burr hole surgery, were studied. A patient-specific model was employed under varying surgical conditions, generating a collection of possible intraoperative shift estimations or a 'deformation atlas.' An inverse problem was driven by sparse measurements derived from iMR to determine an optimal fit of solutions of the atlas. This fit was then used to obtain a volumetric deformation field, which was utilized to update preoperative MR and estimate shift at surgical target region localized on iMR. Model performance was examined by quantitatively comparing intraoperative subsurface measurements to their model-predicted counterparts, and qualitatively comparing iMR, preoperative MR, and model updated MR. A nonrigid image registration was introduced as a comparator. RESULTS Model-based approach reduced general parenchyma shift from 8.2 ± 2.2 to 2.7 ± 1.1 mm (∼66.8% correction), and produced updated MR with better agreement to iMR than that of preoperative MR. The average model estimated shift at target region was 1.2 mm. CONCLUSIONS This study demonstrates the feasibility of a model-based shift correction strategy in DBS surgery with only sparse data. SIGNIFICANCE The developed strategy has the potential to complement and/or enhance current clinical approaches in addressing shift.
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