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Heidari M, Amouheidari A, Hemati S, Khanahmad H, Rahimmanesh I, Jafari P, Shokrani P. Prospective Prediction of Treatment Response in High-Grade Glioma Patients using Pre-Treatment Tumor ADC Value and miR-222 and miR-205 Expression Levels in Plasma. J Biomed Phys Eng 2024; 14:111-118. [PMID: 38628894 PMCID: PMC11016827 DOI: 10.31661/jbpe.v0i0.2108-1376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Accepted: 09/01/2021] [Indexed: 04/19/2024]
Abstract
Background Treatment response in High-grade Glioma (HGG) patients changes based on their genetic and biological characteristics. MiRNAs, as important regulators of drug and radiation resistance, and the Apparent Diffusion Coefficients (ADC) value of tumor can be used as a prognostic predictor for glioma. Objective This study aimed to identify some of the pre-treatment individual patient features for predicting the treatment response in HGG patients. Material and Methods In this prospective study, 18 HGG patients, who were candidated for chemo-radiation treatment, participated after informed consent of the patients. The investigated features were the expression level of miR-222 and miR-205 in plasma, the ADC value of tumor, Body Mass Index (BMI), and age. Treatment response was assessed, and Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to obtain a model to predict the treatment response. Mann-Whitney U test was also applied to select the variables with a significant relationship with patients' treatment response. Results The LASSO coefficients for miR-205, miR-222, tumor's mean ADC value, BMI, and age were 3.611, -1.683, 2.468, -0.184, and -0.024, respectively. Mann-Whitney U test results showed miR-205 and tumor's mean ADC significantly related to treatment response (P-value<0.05). Conclusion The miR-205 expression level of the patient in plasma and tumor's mean ADC value has the potential for prognostic predictors in HGG.
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Affiliation(s)
- Maryam Heidari
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | | | - Simin Hemati
- Department of Radiotherapy Oncology, Faculty of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Hossein Khanahmad
- Department of Genetics and Molecular Biology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Ilnaz Rahimmanesh
- Applied Physiology Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Peyman Jafari
- Department of Biostatistics, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Parvaneh Shokrani
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
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Mohammadi M, Banisharif S, Moradi F, Zamanian M, Tanzifi G, Ghaderi S. Brain diffusion MRI biomarkers after oncology treatments. Rep Pract Oncol Radiother 2024; 28:823-834. [PMID: 38515826 PMCID: PMC10954263 DOI: 10.5603/rpor.98728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 12/04/2023] [Indexed: 03/23/2024] Open
Abstract
In addition to providing a measurement of the tumor's size and dimensions, magnetic resonance imaging (MRI) provides excellent noninvasive radiographic detection of tumor location. The MRI technique is an important modality that has been shown to be useful in the prognosis, diagnosis, treatment planning, and evaluation of response and recurrence in solid cancers. Diffusion-weighted imaging (DWI) is an imaging technique that quantifies water mobility. This imaging approach is good for identifying sub-voxel microstructure of tissues, correlates with tumor cellularity, and has been proven to be valuable in the early assessment of cytotoxic treatment for a variety of malignancies. Diffusion tensor imaging (DTI) is an MRI method that assesses the preferred amount of water transport inside tissues. This enables precise measurements of water diffusion, which changes according to the direction of white matter fibers, their density, and myelination. This measurement corresponds to some related variables: fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), axial diffusivity (AD), and others. DTI biomarkers can detect subtle changes in white matter microstructure and integrity following radiation therapy (RT) or chemoradiotherapy, which may have implications for cognitive function and quality of life. In our study, these indices were evaluated after brain chemoradiotherapy.
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Affiliation(s)
- Mahdi Mohammadi
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Shabnam Banisharif
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Science, Isfahan, Iran
| | - Fatemeh Moradi
- Department of Energy Engineering & Physics, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran
| | - Maryam Zamanian
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Science, Isfahan, Iran
| | - Ghazal Tanzifi
- Department of Nuclear Engineering, Islamic Azad University, Central Tehran Branch, Tehran, Iran
| | - Sadegh Ghaderi
- Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
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Bao D, Zhao Y, Wu W, Zhong H, Yuan M, Li L, Lin M, Zhao X, Luo D. Added value of histogram analysis of ADC in predicting radiation-induced temporal lobe injury of patients with nasopharyngeal carcinoma treated by intensity-modulated radiotherapy. Insights Imaging 2022; 13:197. [PMID: 36528686 PMCID: PMC9759610 DOI: 10.1186/s13244-022-01338-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 11/20/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND This study evaluated the predictive potential of histogram analysis derived from apparent diffusion coefficient (ADC) maps in radiation-induced temporal lobe injury (RTLI) of nasopharyngeal carcinoma (NPC) after intensity-modulated radiotherapy (IMRT). RESULTS Pretreatment diffusion-weighted imaging (DWI) of the temporal lobes of 214 patients with NPC was retrospectively analyzed to obtain ADC histogram parameters. Of the 18 histogram parameters derived from ADC maps, 7 statistically significant variables in the univariate analysis were included in the multivariate logistic regression analysis. The final best prediction model selected by backward stepwise elimination with Akaike information criteria as the stopping rule included kurtosis, maximum energy, range, and total energy. A Rad-score was established by combining the four variables, and it provided areas under the curve (AUCs) of 0.95 (95% confidence interval [CI] 0.91-0.98) and 0.89 (95% CI 0.81-0.97) in the training and validation cohorts, respectively. The combined model, integrating the Rad-score with the T stage (p = 0.02), showed a favorable prediction performance in the training and validation cohorts (AUC = 0.96 and 0.87, respectively). The calibration curves showed a good agreement between the predicted and actual RTLI occurrences. CONCLUSIONS Pretreatment histogram analysis of ADC maps and their combination with the T stage showed a satisfactory ability to predict RTLI in NPC after IMRT.
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Affiliation(s)
- Dan Bao
- grid.506261.60000 0001 0706 7839Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021 China
| | - Yanfeng Zhao
- grid.506261.60000 0001 0706 7839Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021 China
| | - Wenli Wu
- Medical Imaging Center, Liaocheng Tumor Hospital, Shandong, 252000 China
| | - Hongxia Zhong
- grid.506261.60000 0001 0706 7839Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021 China
| | - Meng Yuan
- grid.506261.60000 0001 0706 7839Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021 China
| | - Lin Li
- grid.506261.60000 0001 0706 7839Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021 China
| | - Meng Lin
- grid.506261.60000 0001 0706 7839Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021 China
| | - Xinming Zhao
- grid.506261.60000 0001 0706 7839Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021 China
| | - Dehong Luo
- grid.506261.60000 0001 0706 7839Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021 China ,grid.506261.60000 0001 0706 7839Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, 518116 China
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Jarrett AM, Hormuth DA, Barnes SL, Feng X, Huang W, Yankeelov TE. Incorporating drug delivery into an imaging-driven, mechanics-coupled reaction diffusion model for predicting the response of breast cancer to neoadjuvant chemotherapy: theory and preliminary clinical results. Phys Med Biol 2018; 63:105015. [PMID: 29697054 PMCID: PMC5985823 DOI: 10.1088/1361-6560/aac040] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Clinical methods for assessing tumor response to therapy are largely rudimentary, monitoring only temporal changes in tumor size. Our goal is to predict the response of breast tumors to therapy using a mathematical model that utilizes magnetic resonance imaging (MRI) data obtained non-invasively from individual patients. We extended a previously established, mechanically coupled, reaction-diffusion model for predicting tumor response initialized with patient-specific diffusion weighted MRI (DW-MRI) data by including the effects of chemotherapy drug delivery, which is estimated using dynamic contrast-enhanced (DCE-) MRI data. The extended, drug incorporated, model is initialized using patient-specific DW-MRI and DCE-MRI data. Data sets from five breast cancer patients were used-obtained before, after one cycle, and at mid-point of neoadjuvant chemotherapy. The DCE-MRI data was used to estimate spatiotemporal variations in tumor perfusion with the extended Kety-Tofts model. The physiological parameters derived from DCE-MRI were used to model changes in delivery of therapy drugs within the tumor for incorporation in the extended model. We simulated the original model and the extended model in both 2D and 3D and compare the results for this five-patient cohort. Preliminary results show reductions in the error of model predicted tumor cellularity and size compared to the experimentally-measured results for the third MRI scan when therapy was incorporated. Comparing the two models for agreement between the predicted total cellularity and the calculated total cellularity (from the DW-MRI data) reveals an increased concordance correlation coefficient from 0.81 to 0.98 for the 2D analysis and 0.85 to 0.99 for the 3D analysis (p < 0.01 for each) when the extended model was used in place of the original model. This study demonstrates the plausibility of using DCE-MRI data as a means to estimate drug delivery on a patient-specific basis in predictive models and represents a step toward the goal of achieving individualized prediction of tumor response to therapy.
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Affiliation(s)
- Angela M. Jarrett
- Institute for Computational Engineering and Sciences, The University of Texas at Austin Austin, Texas USA
| | - David A. Hormuth
- Institute for Computational Engineering and Sciences, The University of Texas at Austin Austin, Texas USA
| | - Stephane L. Barnes
- Institute for Computational Engineering and Sciences, The University of Texas at Austin Austin, Texas USA
| | - Xinzeng Feng
- Institute for Computational Engineering and Sciences, The University of Texas at Austin Austin, Texas USA
| | - Wei Huang
- Advanced Imaging Research Center Oregon Health and Science University Portland, Oregon USA
| | - Thomas E. Yankeelov
- Institute for Computational Engineering and Sciences, The University of Texas at Austin Austin, Texas USA
- Livestrong Cancer Institutes, The University of Texas at Austin Austin, Texas USA
- Department of Biomedical Engineering, The University of Texas at Austin Austin, Texas USA
- Department of Oncology, The University of Texas at Austin Austin, Texas USA
- Department of Diagnostic Medicine, The University of Texas at Austin Austin, Texas USA
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Jafar MM, Parsai A, Miquel ME. Diffusion-weighted magnetic resonance imaging in cancer: Reported apparent diffusion coefficients, in-vitro and in-vivo reproducibility. World J Radiol 2016; 8:21-49. [PMID: 26834942 PMCID: PMC4731347 DOI: 10.4329/wjr.v8.i1.21] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/21/2015] [Revised: 11/10/2015] [Accepted: 12/07/2015] [Indexed: 02/06/2023] Open
Abstract
There is considerable disparity in the published apparent diffusion coefficient (ADC) values across different anatomies. Institutions are increasingly assessing repeatability and reproducibility of the derived ADC to determine its variation, which could potentially be used as an indicator in determining tumour aggressiveness or assessing tumour response. In this manuscript, a review of selected articles published to date in healthy extra-cranial body diffusion-weighted magnetic resonance imaging is presented, detailing reported ADC values and discussing their variation across different studies. In total 115 studies were selected including 28 for liver parenchyma, 15 for kidney (renal parenchyma), 14 for spleen, 13 for pancreatic body, 6 for gallbladder, 13 for prostate, 13 for uterus (endometrium, myometrium, cervix) and 13 for fibroglandular breast tissue. Median ADC values in selected studies were found to be 1.28 × 10(-3) mm(2)/s in liver, 1.94 × 10(-3) mm(2)/s in kidney, 1.60 × 10(-3) mm(2)/s in pancreatic body, 0.85 × 10(-3) mm(2)/s in spleen, 2.73 × 10(-3) mm(2)/s in gallbladder, 1.64 × 10(-3) mm(2)/s and 1.31 × 10(-3) mm(2)/s in prostate peripheral zone and central gland respectively (combined median value of 1.54×10(-3) mm(2)/s), 1.44 × 10(-3) mm(2)/s in endometrium, 1.53 × 10(-3) mm(2)/s in myometrium, 1.71 × 10(-3) mm(2)/s in cervix and 1.92 × 10(-3) mm(2)/s in breast. In addition, six phantom studies and thirteen in vivo studies were summarized to compare repeatability and reproducibility of the measured ADC. All selected phantom studies demonstrated lower intra-scanner and inter-scanner variation compared to in vivo studies. Based on the findings of this manuscript, it is recommended that protocols need to be optimised for the body part studied and that system-induced variability must be established using a standardized phantom in any clinical study. Reproducibility of the measured ADC must also be assessed in a volunteer population, as variations are far more significant in vivo compared with phantom studies.
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Bokacheva L, Ackerstaff E, LeKaye HC, Zakian K, Koutcher JA. High-field small animal magnetic resonance oncology studies. Phys Med Biol 2013; 59:R65-R127. [PMID: 24374985 DOI: 10.1088/0031-9155/59/2/r65] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
This review focuses on the applications of high magnetic field magnetic resonance imaging (MRI) and spectroscopy (MRS) to cancer studies in small animals. High-field MRI can provide information about tumor physiology, the microenvironment, metabolism, vascularity and cellularity. Such studies are invaluable for understanding tumor growth and proliferation, response to treatment and drug development. The MR techniques reviewed here include (1)H, (31)P, chemical exchange saturation transfer imaging and hyperpolarized (13)C MRS as well as diffusion-weighted, blood oxygen level dependent contrast imaging and dynamic contrast-enhanced MRI. These methods have been proven effective in animal studies and are highly relevant to human clinical studies.
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Affiliation(s)
- Louisa Bokacheva
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, 415 East 68 Street, New York, NY 10065, USA
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