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Sperber J, Zachem TJ, Prakash R, Owolo E, Yamamoto K, Nguyen AD, Hockenberry H, Ross WA, Herndon JE, Codd PJ, Goodwin CR. A blinded study using laser induced endogenous fluorescence spectroscopy to differentiate ex vivo spine tumor, healthy muscle, and healthy bone. Sci Rep 2024; 14:1921. [PMID: 38253556 PMCID: PMC10803777 DOI: 10.1038/s41598-023-50995-4] [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: 07/11/2023] [Accepted: 12/28/2023] [Indexed: 01/24/2024] Open
Abstract
Ten patients undergoing surgical resection for spinal tumors were selected. Samples of tumor, muscle, and bone were resected, de-identified by the treating surgeon, and then scanned with the TumorID technology ex vivo. This study investigates whether TumorID technology is able to differentiate three different human clinical fresh tissue specimens: spine tumor, normal muscle, and normal bone. The TumorID technology utilizes a 405 nm excitation laser to target endogenous fluorophores, thereby allowing for the detection of tissue based on emission spectra. Metabolic profiles of tumor and healthy tissue vary, namely NADH (bound and free emission peak, respectively: 487 nm, 501 nm) and FAD (emission peak: 544) are endogenous fluorophores with distinct concentrations in tumor and healthy tissue. Emission spectra analyzed consisted of 74 scans of spine tumor, 150 scans of healthy normal bone, and 111 scans of healthy normal muscle. An excitation wavelength of 405 nm was used to obtain emission spectra from tissue as previously described. Emission spectra consisted of approximately 1400 wavelength intensity pairs between 450 and 750 nm. Kruskal-Wallis tests were conducted comparing AUC distributions for each treatment group, α = 0.05. Spectral signatures varied amongst the three different tissue types. All pairwise comparisons among tissues for Free NADH were statistically significant (Tumor vs. Muscle: p = 0.0006, Tumor vs. Bone: p < 0.0001, Bone vs. Muscle: p = 0.0357). The overall comparison of tissues for FAD (506.5-581.5 nm) was also statistically significant (p < 0.0001), with two pairwise comparisons being statistically significant (Tumor vs. Muscle: p < 0.0001, Tumor vs. Bone: p = 0.0045, Bone vs. Muscle: p = 0.249). These statistically significant differences were maintained when stratifying tumor into metastatic carcinoma (N = 57) and meningioma (N = 17). TumorID differentiates tumor tissue from normal bone and normal muscle providing further clinical evidence of its efficacy as a tissue identification tool. Future studies should evaluate TumorID's ability to serve as an adjunctive tool for intraoperative assessment of surgical margins and surgical decision-making.
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Affiliation(s)
- Jacob Sperber
- Department of Neurosurgery, Duke University School of Medicine, Durham, USA
| | - Tanner J Zachem
- Department of Neurosurgery, Duke University School of Medicine, Durham, USA
- Department of Mechanical Engineering and Materials Science, Duke University, Durham, USA
| | - Ravi Prakash
- Department of Mechanical Engineering and Materials Science, Duke University, Durham, USA
| | - Edwin Owolo
- Department of Neurosurgery, Duke University School of Medicine, Durham, USA
| | - Kent Yamamoto
- Department of Mechanical Engineering and Materials Science, Duke University, Durham, USA
| | - Annee D Nguyen
- Department of Neurosurgery, Duke University School of Medicine, Durham, USA
| | | | - Weston A Ross
- Department of Neurosurgery, Duke University School of Medicine, Durham, USA
| | - James E Herndon
- Department of Biostatistics & Bioinformatics, Duke University School of Medicine, Durham, USA
| | - Patrick J Codd
- Department of Neurosurgery, Duke University School of Medicine, Durham, USA
- Department of Mechanical Engineering and Materials Science, Duke University, Durham, USA
- Duke Cancer Institute, Duke University Medical Center, 200 Trent Drive DUMC 3807, Durham, NC, 27710, USA
| | - C Rory Goodwin
- Department of Neurosurgery, Duke University School of Medicine, Durham, USA.
- Duke Cancer Institute, Duke University Medical Center, 200 Trent Drive DUMC 3807, Durham, NC, 27710, USA.
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Fan Y, Liu S, Gao E, Guo R, Dong G, Li Y, Gao T, Tang X, Liao H. The LMIT: Light-mediated minimally-invasive theranostics in oncology. Theranostics 2024; 14:341-362. [PMID: 38164160 PMCID: PMC10750201 DOI: 10.7150/thno.87783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 10/18/2023] [Indexed: 01/03/2024] Open
Abstract
Minimally-invasive diagnosis and therapy have gradually become the trend and research hotspot of current medical applications. The integration of intraoperative diagnosis and treatment is a development important direction for real-time detection, minimally-invasive diagnosis and therapy to reduce mortality and improve the quality of life of patients, so called minimally-invasive theranostics (MIT). Light is an important theranostic tool for the treatment of cancerous tissues. Light-mediated minimally-invasive theranostics (LMIT) is a novel evolutionary technology that integrates diagnosis and therapeutics for the less invasive treatment of diseased tissues. Intelligent theranostics would promote precision surgery based on the optical characterization of cancerous tissues. Furthermore, MIT also requires the assistance of smart medical devices or robots. And, optical multimodality lay a solid foundation for intelligent MIT. In this review, we summarize the important state-of-the-arts of optical MIT or LMIT in oncology. Multimodal optical image-guided intelligent treatment is another focus. Intraoperative imaging and real-time analysis-guided optical treatment are also systemically discussed. Finally, the potential challenges and future perspectives of intelligent optical MIT are discussed.
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Affiliation(s)
- Yingwei Fan
- School of Medical Technology, Beijing Institute of Technology, Beijing, China, 100081
| | - Shuai Liu
- School of Medical Technology, Beijing Institute of Technology, Beijing, China, 100081
| | - Enze Gao
- School of Medical Technology, Beijing Institute of Technology, Beijing, China, 100081
| | - Rui Guo
- School of Medical Technology, Beijing Institute of Technology, Beijing, China, 100081
| | - Guozhao Dong
- School of Medical Technology, Beijing Institute of Technology, Beijing, China, 100081
| | - Yangxi Li
- Dept. of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China, 100084
| | - Tianxin Gao
- School of Medical Technology, Beijing Institute of Technology, Beijing, China, 100081
| | - Xiaoying Tang
- School of Medical Technology, Beijing Institute of Technology, Beijing, China, 100081
| | - Hongen Liao
- Dept. of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China, 100084
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Prakash R, Yamamoto KK, Oca SR, Ross W, Codd PJ. Brain-Mimicking Phantom for Photoablation and Visualization. ... INTERNATIONAL SYMPOSIUM ON MEDICAL ROBOTICS. INTERNATIONAL SYMPOSIUM ON MEDICAL ROBOTICS 2023; 2023:10.1109/ismr57123.2023.10130243. [PMID: 37274088 PMCID: PMC10237535 DOI: 10.1109/ismr57123.2023.10130243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
While the use of tissue-mimicking (TM) phantoms has been ubiquitous in surgical robotics, the translation of technology from laboratory experiments to equivalent intraoperative tissue conditions has been a challenge. The increasing use of lasers for surgical tumor resection has introduced the need to develop a modular, low-cost, functionally relevant TM phantom to model the complex laser-tissue interaction. In this paper, a TM phantom with mechanically and thermally similar properties as human brain tissue suited for photoablation studies and subsequent visualization is developed. The proposed study demonstrates the tuned phantom response to laser ablation for fixed laser power, time, and angle. Additionally, the ablated crater profile is visualized using optical coherence tomography (OCT), enabling high-resolution surface profile generation.
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Affiliation(s)
- Ravi Prakash
- Department of Mechanical Engineering and Materials Science, Duke University
| | - Kent K. Yamamoto
- Department of Mechanical Engineering and Materials Science, Duke University
| | - Siobhan R. Oca
- Department of Mechanical Engineering and Materials Science, Duke University
| | - Weston Ross
- Department of Neurosurgery, Duke University School of Medicine
| | - Patrick J. Codd
- Department of Mechanical Engineering and Materials Science, Duke University
- Department of Neurosurgery, Duke University School of Medicine
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Ma G, Ross W, Codd PJ. N-mirror Robot System for Laser Surgery: A Simulation Study. ... INTERNATIONAL SYMPOSIUM ON MEDICAL ROBOTICS. INTERNATIONAL SYMPOSIUM ON MEDICAL ROBOTICS 2023; 2023:10.1109/ismr57123.2023.10130180. [PMID: 38031532 PMCID: PMC10686368 DOI: 10.1109/ismr57123.2023.10130180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/01/2023]
Abstract
Automated laser surgery with sensor fusion is an important problem in medical robotics since it requires precise control of mirrors used to steer the laser systems. The propagation of the laser beam should satisfy the geometric constraints of the surgical site but the relation between the number of mirrors and the design of the optical path remains an unsolved problem. Furthermore, different types of surgery (e.g. endoscopic vs open surgery) can require different optical designs with varying number of mirrors to successfully steer the laser beam to the tissue. A generalized method for controlling the laser beam in such systems remains an open research question. This paper proposes an analytical model for a laser-based surgical system with an arbitrary number of mirrors, which is referred as an "N -mirror" robotic system. This system consists of three laser inputs to transmit the laser beam to the tissue surface through N number of mirrors, which can achieve surface scanning, tissue resection and tissue classification separately. For sensor information alignment, the forward and inverse kinematics of the N -mirror robot system are derived and used to calculate the mirror angles for laser steering at the target surface. We propose a system calibration method to determine the laser input configuration that is required in the kinematic modelling. We conduct simulation experiments for a simulated 3-mirror system of an actual robotic laser platform and a 6-mirror simulated robot, both with 3-laser inputs. The simulation experiments for system calibration show results of maximum position offset smaller than 0.127 mm and maximum angle offset smaller than 0.05° for the optimal laser input predictions.
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Affiliation(s)
- Guangshen Ma
- Department of Mechanical Engineering and Materials Science, Duke University
| | - Weston Ross
- Department of Neurosurgery, Duke University Medical Center
| | - Patrick J Codd
- Department of Mechanical Engineering and Materials Science, Duke University
- Department of Neurosurgery, Duke University Medical Center
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