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Li S, Yang X, Lu T, Yuan L, Zhang Y, Zhao J, Deng J, Xue C, Sun Q, Liu X, Zhang W, Zhou J. Extracellular volume fraction can predict the treatment response and survival outcome of colorectal cancer liver metastases. Eur J Radiol 2024; 175:111444. [PMID: 38531223 DOI: 10.1016/j.ejrad.2024.111444] [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: 11/24/2023] [Revised: 03/09/2024] [Accepted: 03/21/2024] [Indexed: 03/28/2024]
Abstract
OBJECTIVE To assess the prognostic value of pre- and post-therapeutic changes in extracellular volume (ECV) fraction of liver metastases (LMs) for treatment response (TR) and survival outcomes in colorectal cancer liver metastases (CRLM). METHODS 186 LMs were confirmed by pathology or follow-up (Training: 130; Test: 56). We analyzed the changes in ECV fraction of LMs before and after 2 cycles of chemotherapy combined with bevacizumab. After 12 cycles, we evaluated the TR on LMs based on the RECIST v1.1. Relative changes in ECV fraction and Hounsfield Units (HU), defined as ΔECV and ΔHU, were associated with progression-free survival (PFS), overall survival (OS), and TR. We identified TR predictors with multivariate logistic regression and PFS, OS risk factors with COX analysis. RESULTS 186 LMs were classified as TR lesions (TR+: 84) and non-TR lesions (TR-:102). ΔECV, ΔHUA-E, and texture could distinguish the TR of LMs in training and test set (P < 0.05). ΔECV [Odds ratio (OR): 1.03; 95% Confidence interval (CI): 1.02-1.05, P < 0.01] was an independent predictor of TR-. Area under the curve (AUC), sensitivity and specificity of TR model in training and test set were 0.87, 0.84, 90.14%, 90.32%, 72.88%, 64.00%, respectively. High CRD_score indicates that patients have shorter PFS [Hazard ratio (HR): 2.01; 95%CI: 1.02-3.98, P = 0.045)] and OS (HR: 1.89, 95%CI: 1.04-3.42, P = 0.038). CONCLUSION ΔECV can be used as an independent predictor of TR of CRLM chemotherapy combined with bevacizumab.
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Affiliation(s)
- Shenglin Li
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China.
| | - Xinmei Yang
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Ting Lu
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Long Yuan
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Yuting Zhang
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
| | - Jun Zhao
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
| | - Juan Deng
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Caiqiang Xue
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Qiu Sun
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China
| | - Xianwang Liu
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Wenjuan Zhang
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China.
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García-Varela L, Codesido J, Perez-Pedrosa A, Muñoz-González M, Ramos-Docampo E, Rey-Bretal D, García-Otero X, Gómez-Lado N, Turrero A, Beiroa D, Rodríguez-Perez AI, Vidal A, Fernández-Ferreiro A, Pubul V, Aguiar P. Biodistribution and pharmacokinetics of [ 89Zr]-anti-VEGF mAbs using PET in glioblastoma rat models. Int J Pharm 2024; 652:123795. [PMID: 38224761 DOI: 10.1016/j.ijpharm.2024.123795] [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: 08/24/2023] [Revised: 01/04/2024] [Accepted: 01/05/2024] [Indexed: 01/17/2024]
Abstract
INTRODUCTION Glioblastomas present intensive angiogenesis, thus anti-Vascular Endothelial Growth Factor (VEGF) antibodies (mAbs) have been proposed as promising therapies. However, the results of clinical trials reported moderate toxicity and limited effectiveness. This study evaluates the in vivo pharmacokinetics and biodistribution of these mAbs in a growing model of glioblastoma in rats using Positron Emission Tomography (PET). MATERIAL &Methods: mAbs were radiolabeled with zirconium-89. Four days after the model induction, animals were injected with 2.33 ± 1.3 MBq of [89Zr]-DFO-bevacizumab (n = 8) or 2.35 ± 0.26 MBq of [89Zr]-DFO-aflibercept (n = 6). PETs were performed at 0H, 48H, 168H, 240H, and 336H post-injection. Tumor induction was confirmed using [18F]-Fluorodeoxyglucose-PET and immunohistochemistry. Radiotracer uptake was estimated in all pre-defined Volumes-of-Interest. RESULTS Anti-VEGF mAbs showed 100 % Radiochemical-Purity. [89Zr]-DFO-bevacizumab showed a significantly higher bioavailability in whole-blood. A significant increase in the tumor uptake was detectable at 168H PET with [89Zr]-DFO-bevacizumab meanwhile with [89Zr]-DFO-aflibercept it was only detectable at 336H. [89Zr]-DFO-bevacizumab tumor uptake was significantly higher than that of [89Zr]-DFO-aflibercept in all the scans. Tumor induction was confirmed in all animal models. CONCLUSION MAbs detect VEGF-expression in glioblastoma models. Tumors were earlier targeted by Bevacizumab. The lower blood availability of aflibercept resulted in a lower tumor uptake than bevacizumab in all the scans.
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Affiliation(s)
- Lara García-Varela
- Molecular Imaging and Pharmacokinetic Modelling Group, Center for Research in Molecular Medicine and Chronic Diseases (CiMUS), University of Santiago de Compostela, Spain; Nuclear Medicine and Molecular Imaging Group, Health Research Institute of Santiago de Compostela (IDIS), University Hospital Santiago de Compostela, Spain
| | - Jessica Codesido
- Molecular Imaging and Pharmacokinetic Modelling Group, Center for Research in Molecular Medicine and Chronic Diseases (CiMUS), University of Santiago de Compostela, Spain; Pharmacy Dept & Pharmacology Group, Health Research Institute of Santiago de Compostela (IDIS), University Hospital Santiago de Compostela, Spain
| | | | - María Muñoz-González
- Molecular Imaging and Pharmacokinetic Modelling Group, Center for Research in Molecular Medicine and Chronic Diseases (CiMUS), University of Santiago de Compostela, Spain; Nuclear Medicine and Molecular Imaging Group, Health Research Institute of Santiago de Compostela (IDIS), University Hospital Santiago de Compostela, Spain
| | - Emma Ramos-Docampo
- Molecular Imaging and Pharmacokinetic Modelling Group, Center for Research in Molecular Medicine and Chronic Diseases (CiMUS), University of Santiago de Compostela, Spain; Nuclear Medicine and Molecular Imaging Group, Health Research Institute of Santiago de Compostela (IDIS), University Hospital Santiago de Compostela, Spain
| | - David Rey-Bretal
- Molecular Imaging and Pharmacokinetic Modelling Group, Center for Research in Molecular Medicine and Chronic Diseases (CiMUS), University of Santiago de Compostela, Spain; Nuclear Medicine and Molecular Imaging Group, Health Research Institute of Santiago de Compostela (IDIS), University Hospital Santiago de Compostela, Spain
| | - Xurxo García-Otero
- Molecular Imaging and Pharmacokinetic Modelling Group, Center for Research in Molecular Medicine and Chronic Diseases (CiMUS), University of Santiago de Compostela, Spain; Nuclear Medicine and Molecular Imaging Group, Health Research Institute of Santiago de Compostela (IDIS), University Hospital Santiago de Compostela, Spain
| | - Noemí Gómez-Lado
- Molecular Imaging and Pharmacokinetic Modelling Group, Center for Research in Molecular Medicine and Chronic Diseases (CiMUS), University of Santiago de Compostela, Spain; Nuclear Medicine and Molecular Imaging Group, Health Research Institute of Santiago de Compostela (IDIS), University Hospital Santiago de Compostela, Spain
| | - Angela Turrero
- Cell Cycle and Oncology Group (CiCLOn), Center for Research in Molecular Medicine and Chronic Diseases (CiMUS), Health Research Institute of Santiago de Compostela (IDIS), University of Santiago de Compostela, Spain
| | - Daniel Beiroa
- Centro de Biomedicina Experimental (CEBEGA), University of Santiago de Compostela, Spain
| | - Ana Isabel Rodríguez-Perez
- Cell and Molecular Neurobiology of Parkinson's disease, Center for Research in Molecular Medicine and Chronic Diseases (CiMUS), Health Research Institute of Santiago de Compostela (IDIS), University of Santiago de Compostela, Spain. Networking Research Center on Neurodegenerative Diseases (CIBERNED), Spain
| | - Anxo Vidal
- Cell Cycle and Oncology Group (CiCLOn), Center for Research in Molecular Medicine and Chronic Diseases (CiMUS), Health Research Institute of Santiago de Compostela (IDIS), University of Santiago de Compostela, Spain
| | - Anxo Fernández-Ferreiro
- Pharmacy Dept & Pharmacology Group, Health Research Institute of Santiago de Compostela (IDIS), University Hospital Santiago de Compostela, Spain
| | - Virginia Pubul
- Nuclear Medicine and Molecular Imaging Group, Health Research Institute of Santiago de Compostela (IDIS), University Hospital Santiago de Compostela, Spain
| | - Pablo Aguiar
- Molecular Imaging and Pharmacokinetic Modelling Group, Center for Research in Molecular Medicine and Chronic Diseases (CiMUS), University of Santiago de Compostela, Spain; Nuclear Medicine and Molecular Imaging Group, Health Research Institute of Santiago de Compostela (IDIS), University Hospital Santiago de Compostela, Spain
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Bagher-Ebadian H, Brown SL, Ghassemi MM, Nagaraja TN, Movsas B, Ewing JR, Chetty IJ. Radiomics characterization of tissues in an animal brain tumor model imaged using dynamic contrast enhanced (DCE) MRI. Sci Rep 2023; 13:10693. [PMID: 37394559 DOI: 10.1038/s41598-023-37723-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 06/27/2023] [Indexed: 07/04/2023] Open
Abstract
Here, we investigate radiomics-based characterization of tumor vascular and microenvironmental properties in an orthotopic rat brain tumor model measured using dynamic-contrast-enhanced (DCE) MRI. Thirty-two immune compromised-RNU rats implanted with human U-251N cancer cells were imaged using DCE-MRI (7Tesla, Dual-Gradient-Echo). The aim was to perform pharmacokinetic analysis using a nested model (NM) selection technique to classify brain regions according to vasculature properties considered as the source of truth. A two-dimensional convolutional-based radiomics analysis was performed on the raw-DCE-MRI of the rat brains to generate dynamic radiomics maps. The raw-DCE-MRI and respective radiomics maps were used to build 28 unsupervised Kohonen self-organizing-maps (K-SOMs). A Silhouette-Coefficient (SC), k-fold Nested-Cross-Validation (k-fold-NCV), and feature engineering analyses were performed on the K-SOMs' feature spaces to quantify the distinction power of radiomics features compared to raw-DCE-MRI for classification of different Nested Models. Results showed that eight radiomics features outperformed respective raw-DCE-MRI in prediction of the three nested models. The average percent difference in SCs between radiomics features and raw-DCE-MRI was: 29.875% ± 12.922%, p < 0.001. This work establishes an important first step toward spatiotemporal characterization of brain regions using radiomics signatures, which is fundamental toward staging of tumors and evaluation of tumor response to different treatments.
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Affiliation(s)
- Hassan Bagher-Ebadian
- Department of Radiation Oncology, Henry Ford Health, Detroit, MI, 48202, USA.
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA.
- Department of Osteopathic Medicine, Michigan State University, East Lansing, MI, 48824, USA.
- Department of Physics, Oakland University, Rochester, MI, 48309, USA.
| | - Stephen L Brown
- Department of Radiation Oncology, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Radiation Oncology, Wayne State University, Detroit, MI, 48202, USA
| | - Mohammad M Ghassemi
- Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA
| | - Tavarekere N Nagaraja
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Neurosurgery, Henry Ford Health, Detroit, MI, 48202, USA
| | - Benjamin Movsas
- Department of Radiation Oncology, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Radiation Oncology, Wayne State University, Detroit, MI, 48202, USA
| | - James R Ewing
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Physics, Oakland University, Rochester, MI, 48309, USA
- Department of Neurosurgery, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Neurology, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Neurology, Wayne State University, Detroit, MI, 48202, USA
| | - Indrin J Chetty
- Department of Radiation Oncology, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Physics, Oakland University, Rochester, MI, 48309, USA
- Department of Radiation Oncology, Wayne State University, Detroit, MI, 48202, USA
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Bagher-Ebadian H, Brown SL, Ghassemi MM, Nagaraja TN, Valadie OG, Acharya PC, Cabral G, Divine G, Knight RA, Lee IY, Xu JH, Movsas B, Chetty IJ, Ewing JR. Dynamic contrast enhanced (DCE) MRI estimation of vascular parameters using knowledge-based adaptive models. Sci Rep 2023; 13:9672. [PMID: 37316579 PMCID: PMC10267191 DOI: 10.1038/s41598-023-36483-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Accepted: 06/05/2023] [Indexed: 06/16/2023] Open
Abstract
We introduce and validate four adaptive models (AMs) to perform a physiologically based Nested-Model-Selection (NMS) estimation of such microvascular parameters as forward volumetric transfer constant, Ktrans, plasma volume fraction, vp, and extravascular, extracellular space, ve, directly from Dynamic Contrast-Enhanced (DCE) MRI raw information without the need for an Arterial-Input Function (AIF). In sixty-six immune-compromised-RNU rats implanted with human U-251 cancer cells, DCE-MRI studies estimated pharmacokinetic (PK) parameters using a group-averaged radiological AIF and an extended Patlak-based NMS paradigm. One-hundred-ninety features extracted from raw DCE-MRI information were used to construct and validate (nested-cross-validation, NCV) four AMs for estimation of model-based regions and their three PK parameters. An NMS-based a priori knowledge was used to fine-tune the AMs to improve their performance. Compared to the conventional analysis, AMs produced stable maps of vascular parameters and nested-model regions less impacted by AIF-dispersion. The performance (Correlation coefficient and Adjusted R-squared for NCV test cohorts) of the AMs were: 0.914/0.834, 0.825/0.720, 0.938/0.880, and 0.890/0.792 for predictions of nested model regions, vp, Ktrans, and ve, respectively. This study demonstrates an application of AMs that quickens and improves DCE-MRI based quantification of microvasculature properties of tumors and normal tissues relative to conventional approaches.
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Affiliation(s)
- Hassan Bagher-Ebadian
- Department of Radiation Oncology, Henry Ford Health, Detroit, MI, 48202, USA.
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA.
- Department of Osteopathic Medicine, Michigan State University, East Lansing, MI, 48824, USA.
- Department of Physics, Oakland University, Rochester, MI, 48309, USA.
| | - Stephen L Brown
- Department of Radiation Oncology, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Radiation Oncology, Wayne State University, Detroit, MI, 48202, USA
| | - Mohammad M Ghassemi
- Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA
| | - Tavarekere N Nagaraja
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Neurosurgery, Henry Ford Health, Detroit, MI, 48202, USA
| | - Olivia Grahm Valadie
- Department of Radiation Oncology, Wayne State University, Detroit, MI, 48202, USA
| | - Prabhu C Acharya
- Department of Physics, Oakland University, Rochester, MI, 48309, USA
| | - Glauber Cabral
- Department of Neurology, Henry Ford Health, Detroit, MI, 48202, USA
| | - George Divine
- Department of Public Health Sciences, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Epidemiology and Biostatistics, Michigan State University, E. Lansing, MI, 48824, USA
| | - Robert A Knight
- Department of Neurology, Henry Ford Health, Detroit, MI, 48202, USA
| | - Ian Y Lee
- Department of Neurosurgery, Henry Ford Health, Detroit, MI, 48202, USA
| | - Jun H Xu
- Department of Neurosurgery, Henry Ford Health, Detroit, MI, 48202, USA
| | - Benjamin Movsas
- Department of Radiation Oncology, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Radiation Oncology, Wayne State University, Detroit, MI, 48202, USA
| | - Indrin J Chetty
- Department of Radiation Oncology, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Physics, Oakland University, Rochester, MI, 48309, USA
- Department of Radiation Oncology, Wayne State University, Detroit, MI, 48202, USA
| | - James R Ewing
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Physics, Oakland University, Rochester, MI, 48309, USA
- Department of Neurosurgery, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Neurology, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Neurology, Wayne State University, Detroit, MI, 48202, USA
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Shen W, Zhang J, Pan Y, Jin Y. LncRNA MIR4435-2HG functions as a ceRNA against miR-125a-5p and promotes neuroglioma development by upregulating TAZ. J Clin Lab Anal 2021; 35:e24066. [PMID: 34714963 PMCID: PMC8649364 DOI: 10.1002/jcla.24066] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 09/23/2021] [Accepted: 09/25/2021] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Expression of the TAZ gene is closely related to the prognosis of glioma patients. We hoped to find long noncoding RNAs (lncRNAs) related to TAZ and a new target for glioma treatment. METHODS TAZ-related genes were found by dual-luciferase reporter gene assay, and the correlation of each gene was analyzed by the Pearson method. Human glioma cell lines U87 MG and U251 and glioma rats were used for cytology assays, and the related genes were transfected. We conducted immunohistochemistry, RT-qPCR, Western blotting, CCK8 test, flow cytometry, transwell assays, clone formation analysis, and tumor weight measurements to verify the above relationship. RESULTS We found that miR-125a-5p was closely related to the TAZ gene, and the lncRNA MIR4435-2HG was closely related to miR-125a-5p. Both MIR4435-2HG-OE and TAZ increased the expression of the TAZ gene, activated the Wnt signaling pathway, inhibited apoptosis, and promoted migration and proliferation in glioma cells. Besides, it also increased the tumor volume of gliomas in a rat model subcutaneously inoculated with glioma cells. We also found miR-125a-5p could block the effect of MIR4435-2HG-OE and TAZ. CONCLUSIONS LncRNA MIR4435-2HG obstructs the functions of miR-125a-5p and promotes neuroglioma development by upregulating the TAZ gene.
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Affiliation(s)
- Wangzhen Shen
- Department of NeurosurgeryTongxiang First People's HospitalTongxiangChina
| | - Jiawei Zhang
- Department of NeurosurgeryTongxiang First People's HospitalTongxiangChina
| | - Yunfeng Pan
- Department of NeurosurgeryTongxiang First People's HospitalTongxiangChina
| | - Yun Jin
- Department of NeurosurgeryTongxiang First People's HospitalTongxiangChina
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Adaptation of laser interstitial thermal therapy for tumor ablation under MRI monitoring in a rat orthotopic model of glioblastoma. Acta Neurochir (Wien) 2021; 163:3455-3463. [PMID: 34554269 DOI: 10.1007/s00701-021-05002-y] [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: 07/02/2021] [Accepted: 09/01/2021] [Indexed: 10/20/2022]
Abstract
BACKGROUND Laser interstitial thermal therapy (LITT) under magnetic resonance imaging (MRI) monitoring is being increasingly used in cytoreductive surgery of recurrent brain tumors and tumors located in eloquent brain areas. The objective of this study was to adapt this technique to an animal glioma model. METHODS A rat model of U251 glioblastoma (GBM) was employed. Tumor location and extent were determined by MRI and dynamic contrast-enhanced (DCE) MRI. A day after assessing tumor appearance, tumors were ablated during diffusion-weighted imaging (DWI)-MRI using a Visualase LITT system (n = 5). Brain images were obtained immediately after ablation and again at 24 h post-ablation to confirm the efficacy of tumor cytoablation. Untreated tumors served as controls (n = 3). Rats were injected with fluorescent isothiocyanate (FITC) dextran and Evans blue that circulated for 10 min after post-LITT MRI. The brains were then removed for fluorescence microscopy and histopathology evaluations using hematoxylin and eosin (H&E) and major histocompatibility complex (MHC) staining. RESULTS All rats showed a space-occupying tumor with T2 and T1 contrast-enhancement at pre-LITT imaging. The rats that underwent the LITT procedure showed a well-demarcated ablation zone with near-complete ablation of tumor tissue and with peri-ablation contrast enhancement at 24 h post-ablation. Tumor cytoreduction by ablation as seen on MRI was confirmed by H&E and MHC staining. CONCLUSIONS Data showed that tumor cytoablation using MRI-monitored LITT was possible in preclinical glioma models. Real-time MRI monitoring facilitated visualizing and controlling the area of ablation as it is otherwise performed in clinical applications.
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Rey JA, Ewing JR, Sarntinoranont M. A computational model of glioma reveals opposing, stiffness-sensitive effects of leaky vasculature and tumor growth on tissue mechanical stress and porosity. Biomech Model Mechanobiol 2021; 20:1981-2000. [PMID: 34363553 DOI: 10.1007/s10237-021-01488-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Accepted: 06/29/2021] [Indexed: 11/29/2022]
Abstract
A biphasic computational model of a growing, vascularized glioma within brain tissue was developed to account for unique features of gliomas, including soft surrounding brain tissue, their low stiffness relative to brain tissue, and a lack of draining lymphatics. This model is the first to couple nonlinear tissue deformation with porosity and tissue hydraulic conductivity to study the mechanical interaction of leaky vasculature and solid growth in an embedded glioma. The present model showed that leaky vasculature and elevated interstitial fluid pressure produce tensile stress within the tumor in opposition to the compressive stress produced by tumor growth. This tensile effect was more pronounced in softer tissue and resulted in a compressive stress concentration at the tumor rim that increased when tumor was softer than host. Aside from generating solid stress, fluid pressure-driven tissue deformation decreased the effective stiffness of the tumor while growth increased it, potentially leading to elevated stiffness in the tumor rim. A novel prediction of reduced porosity at the tumor rim was corroborated by direct comparison with estimates from our in vivo imaging studies. Antiangiogenic and radiation therapy were simulated by varying vascular leakiness and tissue hydraulic conductivity. These led to greater solid compression and interstitial pressure in the tumor, respectively, the former of which may promote tumor infiltration of the host. Our findings suggest that vascular leakiness has an important influence on in vivo solid stress, stiffness, and porosity fields in gliomas given their unique mechanical microenvironment.
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Affiliation(s)
- Julian A Rey
- Department of Mechanical and Aerospace Engineering, University of Florida, PO BOX 116250, Gainesville, FL, 32611, USA
| | - James R Ewing
- Department of Neurology, Henry Ford Hospital, Detroit, MI, USA
- Department of Physics, Oakland University, Rochester, MI, USA
- Department of Neurology, Wayne State University, Detroit, MI, USA
| | - Malisa Sarntinoranont
- Department of Mechanical and Aerospace Engineering, University of Florida, PO BOX 116250, Gainesville, FL, 32611, USA.
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