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Lad Y, Jangam A, Carlton H, Abu-Ayyad M, Hadjipanayis C, Ivkov R, Zacharia BE, Attaluri A. Development of a Treatment Planning Framework for Laser Interstitial Thermal Therapy (LITT). Cancers (Basel) 2023; 15:4554. [PMID: 37760524 PMCID: PMC10526178 DOI: 10.3390/cancers15184554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 08/29/2023] [Accepted: 09/05/2023] [Indexed: 09/29/2023] Open
Abstract
PURPOSE Develop a treatment planning framework for neurosurgeons treating high-grade gliomas with LITT to minimize the learning curve and improve tumor thermal dose coverage. METHODS Deidentified patient images were segmented using the image segmentation software Materialize MIMICS©. Segmented images were imported into the commercial finite element analysis (FEA) software COMSOL Multiphysics© to perform bioheat transfer simulations. The laser probe was modeled as a cylindrical object with radius 0.7 mm and length 100 mm, with a constant beam diameter. A modeled laser probe was placed in the tumor in accordance with patient specific patient magnetic resonance temperature imaging (MRTi) data. The laser energy was modeled as a deposited beam heat source in the FEA software. Penne's bioheat equation was used to model heat transfer in brain tissue. The cerebrospinal fluid (CSF) was modeled as a solid with convectively enhanced conductivity to capture heat sink effects. In this study, thermal damage-dependent blood perfusion was assessed. Pulsed laser heating was modeled based on patient treatment logs. The stationary heat source and pullback heat source techniques were modeled to compare the calculated tissue damage. The developed bioheat transfer model was compared to MRTi data obtained from a laser log during LITT procedures. The application builder module in COMSOL Multiphysics© was utilized to create a Graphical User Interface (GUI) for the treatment planning framework. RESULTS Simulations predicted increased thermal damage (10-15%) in the tumor for the pullback heat source approach compared with the stationary heat source. The model-predicted temperature profiles followed trends similar to those of the MRTi data. Simulations predicted partial tissue ablation in tumors proximal to the CSF ventricle. CONCLUSION A mobile platform-based GUI for bioheat transfer simulation was developed to aid neurosurgeons in conveniently varying the simulation parameters according to a patient-specific treatment plan. The convective effects of the CSF should be modeled with heat sink effects for accurate LITT treatment planning.
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Affiliation(s)
- Yash Lad
- Department of Mechanical Engineering, School of Science, Engineering, and Technology, The Pennsylvania State University Harrisburg, Harrisburg, PA 17057, USA
| | - Avesh Jangam
- Department of Mechanical Engineering, School of Science, Engineering, and Technology, The Pennsylvania State University Harrisburg, Harrisburg, PA 17057, USA
| | - Hayden Carlton
- Department of Radiation Oncology and Molecular Radiation Sciences, The Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Ma’Moun Abu-Ayyad
- Department of Mechanical Engineering, School of Science, Engineering, and Technology, The Pennsylvania State University Harrisburg, Harrisburg, PA 17057, USA
| | - Constantinos Hadjipanayis
- Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Robert Ivkov
- Department of Radiation Oncology and Molecular Radiation Sciences, The Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
- Department of Mechanical Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Materials Science and Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Brad E. Zacharia
- Department of Neurosurgery, Pennsylvania State Health, Hershey, PA 17033, USA
| | - Anilchandra Attaluri
- Department of Mechanical Engineering, School of Science, Engineering, and Technology, The Pennsylvania State University Harrisburg, Harrisburg, PA 17057, USA
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Sharma A, Jangam A, Shen JLY, Ahmad A, Arepally N, Rodriguez B, Borrello J, Bouras A, Kleinberg L, Ding K, Hadjipanayis C, Kraitchman DL, Ivkov R, Attaluri A. Validation of a Temperature-Feedback Controlled Automated Magnetic Hyperthermia Therapy Device. Cancers (Basel) 2023; 15:327. [PMID: 36672278 PMCID: PMC9856953 DOI: 10.3390/cancers15020327] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 12/22/2022] [Accepted: 12/29/2022] [Indexed: 01/05/2023] Open
Abstract
We present in vivo validation of an automated magnetic hyperthermia therapy (MHT) device that uses real-time temperature input measured at the target to control tissue heating. MHT is a thermal therapy that uses heat generated by magnetic materials exposed to an alternating magnetic field. For temperature monitoring, we integrated a commercial fiber optic temperature probe containing four gallium arsenide (GaAs) temperature sensors. The controller device used temperature from the sensors as input to manage power to the magnetic field applicator. We developed a robust, multi-objective, proportional-integral-derivative (PID) algorithm to control the target thermal dose by modulating power delivered to the magnetic field applicator. The magnetic field applicator was a 20 cm diameter Maxwell-type induction coil powered by a 120 kW induction heating power supply operating at 160 kHz. Finite element (FE) simulations were performed to determine values of the PID gain factors prior to verification and validation trials. Ex vivo verification and validation were conducted in gel phantoms and sectioned bovine liver, respectively. In vivo validation of the controller was achieved in a canine research subject following infusion of magnetic nanoparticles (MNPs) into the brain. In all cases, performance matched controller design criteria, while also achieving a thermal dose measured as cumulative equivalent minutes at 43 °C (CEM43) 60 ± 5 min within 30 min.
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Affiliation(s)
- Anirudh Sharma
- Department of Radiation Oncology and Molecular Radiation Sciences, The Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Avesh Jangam
- Department of Mechanical Engineering, School of Science, Engineering, and Technology, The Pennsylvania State University—Harrisburg, Harrisburg, PA 17057, USA
| | - Julian Low Yung Shen
- Department of Mechanical Engineering, School of Science, Engineering, and Technology, The Pennsylvania State University—Harrisburg, Harrisburg, PA 17057, USA
| | - Aiman Ahmad
- Department of Mechanical Engineering, School of Science, Engineering, and Technology, The Pennsylvania State University—Harrisburg, Harrisburg, PA 17057, USA
| | - Nageshwar Arepally
- Department of Mechanical Engineering, School of Science, Engineering, and Technology, The Pennsylvania State University—Harrisburg, Harrisburg, PA 17057, USA
| | - Benjamin Rodriguez
- Sinai BioDesign, Mount Sinai Hospital, New York, NY 10029, USA
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Joseph Borrello
- Sinai BioDesign, Mount Sinai Hospital, New York, NY 10029, USA
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Alexandros Bouras
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Lawrence Kleinberg
- Department of Radiation Oncology and Molecular Radiation Sciences, The Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Kai Ding
- Department of Radiation Oncology and Molecular Radiation Sciences, The Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Constantinos Hadjipanayis
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Dara L. Kraitchman
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Robert Ivkov
- Department of Radiation Oncology and Molecular Radiation Sciences, The Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
- Department of Mechanical Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Materials Science and Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Anilchandra Attaluri
- Department of Mechanical Engineering, School of Science, Engineering, and Technology, The Pennsylvania State University—Harrisburg, Harrisburg, PA 17057, USA
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