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Śledzińska-Bebyn P, Furtak J, Bebyn M, Serafin Z. Beyond conventional imaging: Advancements in MRI for glioma malignancy prediction and molecular profiling. Magn Reson Imaging 2024; 112:63-81. [PMID: 38914147 DOI: 10.1016/j.mri.2024.06.004] [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: 04/04/2024] [Revised: 05/20/2024] [Accepted: 06/20/2024] [Indexed: 06/26/2024]
Abstract
This review examines the advancements in magnetic resonance imaging (MRI) techniques and their pivotal role in diagnosing and managing gliomas, the most prevalent primary brain tumors. The paper underscores the importance of integrating modern MRI modalities, such as diffusion-weighted imaging and perfusion MRI, which are essential for assessing glioma malignancy and predicting tumor behavior. Special attention is given to the 2021 WHO Classification of Tumors of the Central Nervous System, emphasizing the integration of molecular diagnostics in glioma classification, significantly impacting treatment decisions. The review also explores radiogenomics, which correlates imaging features with molecular markers to tailor personalized treatment strategies. Despite technological progress, MRI protocol standardization and result interpretation challenges persist, affecting diagnostic consistency across different settings. Furthermore, the review addresses MRI's capacity to distinguish between tumor recurrence and pseudoprogression, which is vital for patient management. The necessity for greater standardization and collaborative research to harness MRI's full potential in glioma diagnosis and personalized therapy is highlighted, advocating for an enhanced understanding of glioma biology and more effective treatment approaches.
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Affiliation(s)
- Paulina Śledzińska-Bebyn
- Department of Radiology, 10th Military Research Hospital and Polyclinic, 85-681 Bydgoszcz, Poland.
| | - Jacek Furtak
- Department of Clinical Medicine, Faculty of Medicine, University of Science and Technology, Bydgoszcz, Poland; Department of Neurosurgery, 10th Military Research Hospital and Polyclinic, 85-681 Bydgoszcz, Poland
| | - Marek Bebyn
- Department of Internal Diseases, 10th Military Clinical Hospital and Polyclinic, 85-681 Bydgoszcz, Poland
| | - Zbigniew Serafin
- Department of Radiology and Diagnostic Imaging, Nicolaus Copernicus University, Collegium Medicum, Bydgoszcz, Poland
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Kesari A, Yadav VK, Gupta RK, Singh A. Automatic removal of large blood vasculature for objective assessment of brain tumors using quantitative dynamic contrast-enhanced magnetic resonance imaging. NMR IN BIOMEDICINE 2024:e5218. [PMID: 39051137 DOI: 10.1002/nbm.5218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 06/13/2024] [Accepted: 06/18/2024] [Indexed: 07/27/2024]
Abstract
The presence of a normal large blood vessel (LBV) in a tumor region can impact the evaluation of quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters and tumor classification. Hence, there is a need for automatic removal of LBVs from brain tissues including intratumoral regions for achieving an objective assessment of tumors. This retrospective study included 103 histopathologically confirmed brain tumor patients who underwent MRI, including DCE-MRI data acquisition. Quantitative DCE-MRI analysis was performed for computing various parameters such as wash-out slope (Slope-2), relative cerebral blood volume (rCBV), relative cerebral blood flow (rCBF), blood plasma volume fraction (Vp), and volume transfer constant (Ktrans). An approach based on data-clustering algorithm, morphological operations, and quantitative DCE-MRI maps was proposed for the segmentation of normal LBVs in brain tissues, including the tumor region. Here, three widely used data-clustering algorithms were evaluated on two types of quantitative maps: (a) Slope-2, and (b) a new proposed combination of rCBV and Slope-2 maps. Fluid-attenuated inversion recovery-MRI hyperintense lesions were also automatically segmented using deep learning-based architecture. The accuracy of LBV segmentation was qualitatively assessed blindly by two experienced observers, and Likert scoring was also obtained from each individual and compared using Cohen's Kappa test, and multiple statistical features from quantitative DCE-MRI parameters were obtained in the segmented tumor. t-test and receiver operating characteristic (ROC) curve analysis were performed for comparing the effect of removal of LBVs on parameters as well as on tumor grading. k-means clustering exhibited better accuracy and computational efficiency. Tumors, in particular high-grade gliomas (HGGs), showed a high contrast compared with normal tissues (relative % difference = 18.5%) on quantitative maps after the removal of LBVs. Statistical features (95th percentile values) of all parameters in the tumor region showed a statistically significant difference (p < 0.05) between with and without LBV maps. Similar results were obtained for the ROC curve analysis for differentiation between low-grade gliomas and HGGs. Moreover, after the removal of LBVs, the rCBV, rCBF, and Vp maps show better visualization of tumor regions.
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Affiliation(s)
- Anshika Kesari
- Centre for Biomedical Engineering, Indian Institute of Technology, Delhi, New Delhi, India
| | - Virendra Kumar Yadav
- Centre for Biomedical Engineering, Indian Institute of Technology, Delhi, New Delhi, India
| | - Rakesh Kumar Gupta
- Department of Radiology, Fortis Memorial Research Institute, Gurugram, India
| | - Anup Singh
- Centre for Biomedical Engineering, Indian Institute of Technology, Delhi, New Delhi, India
- Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, India
- Yardi School for Artificial Intelligence, Indian Institute of Technology, Delhi, New Delhi, India
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Alorfi NM, Ashour AM, Alharbi AS, Alshehri FS. Targeting inflammation in glioblastoma: An updated review from pathophysiology to novel therapeutic approaches. Medicine (Baltimore) 2024; 103:e38245. [PMID: 38788009 PMCID: PMC11124608 DOI: 10.1097/md.0000000000038245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 04/25/2024] [Indexed: 05/26/2024] Open
Abstract
Glioblastoma (GBM) is a highly aggressive primary malignant brain tumor with a dismal prognosis despite current treatment strategies. Inflammation plays an essential role in GBM pathophysiology, contributing to tumor growth, invasion, immunosuppression, and angiogenesis. As a result, pharmacological intervention with anti-inflammatory drugs has been used as a potential approach for the management of GBM. To provide an overview of the current understanding of GBM pathophysiology, potential therapeutic applications of anti-inflammatory drugs in GBM, conventional treatments of glioblastoma and emerging therapeutic approaches currently under investigation. A narrative review was carried out, scanning publications from 2000 to 2023 on PubMed and Google Scholar. The search was not guided by a set research question or a specific search method but rather focused on the area of interest. Conventional treatments such as surgery, radiotherapy, and chemotherapy have shown some benefits, but their effectiveness is limited by various factors such as tumor heterogeneity and resistance.
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Affiliation(s)
- Nasser M. Alorfi
- Pharmacology and Toxicology Department, College of Pharmacy, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Ahmed M. Ashour
- Pharmacology and Toxicology Department, College of Pharmacy, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Adnan S. Alharbi
- Pharmacy Practice Department, College of Pharmacy, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Fahad S. Alshehri
- Pharmacology and Toxicology Department, College of Pharmacy, Umm Al-Qura University, Makkah, Saudi Arabia
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Han Q, Lu Y, Wang D, Li X, Ruan Z, Mei N, Ji X, Geng D, Yin B. Glioblastomas with and without peritumoral fluid-attenuated inversion recovery (FLAIR) hyperintensity present morphological and microstructural differences on conventional MR images. Eur Radiol 2023; 33:9139-9151. [PMID: 37495706 DOI: 10.1007/s00330-023-09924-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 05/04/2023] [Accepted: 05/14/2023] [Indexed: 07/28/2023]
Abstract
OBJECTIVES Glioblastoma (GB) without peritumoral fluid-attenuated inversion recovery (FLAIR) hyperintensity is atypical and its characteristics are barely known. The aim of this study was to explore the differences in pathological and MRI-based intrinsic features (including morphologic and first-order features) between GBs with peritumoral FLAIR hyperintensity (PFH-bearing GBs) and GBs without peritumoral FLAIR hyperintensity (PFH-free GBs). METHODS In total, 155 patients with pathologically diagnosed GBs were retrospectively collected, which included 110 PFH-bearing GBs and 45 PFH-free GBs. The pathological and imaging data were collected. The Visually AcceSAble Rembrandt Images (VASARI) features were carefully evaluated. The first-order radiomics features from the tumor region were extracted from FLAIR, apparent diffusion coefficient (ADC), and T1CE (T1-contrast enhanced) images. All parameters were compared between the two groups of GBs. RESULTS The pathological data showed more alpha thalassemia/mental retardation syndrome X-linked (ATRX)-loss in PFH-free GBs compared to PFH-bearing ones (p < 0.001). Based on VASARI evaluation, PFH-free GBs had larger intra-tumoral enhancing proportion and smaller necrotic proportion (both, p < 0.001), more common non-enhancing tumor (p < 0.001), mild/minimal enhancement (p = 0.003), expansive T1/FLAIR ratio (p < 0.001) and solid enhancement (p = 0.009), and less pial invasion (p = 0.010). Moreover, multiple ADC- and T1CE-based first-order radiomics features demonstrated differences, especially the lower intensity heterogeneity in PFH-free GBs (for all, adjusted p < 0.05). CONCLUSIONS Compared to PFH-bearing GBs, PFH-free ones demonstrated less immature neovascularization and lower intra-tumoral heterogeneity, which would be helpful in clinical treatment stratification. CLINICAL RELEVANCE STATEMENT Glioblastomas without peritumoral FLAIR hyperintensity show less immature neovascularization and lower heterogeneity leading to potential higher treatment benefits due to less drug resistance and treatment failure. KEY POINTS • The study explored the differences between glioblastomas with and without peritumoral FLAIR hyperintensity. • Glioblastomas without peritumoral FLAIR hyperintensity showed less necrosis and contrast enhancement and lower intensity heterogeneity. • Glioblastomas without peritumoral FLAIR hyperintensity had less immature neovascularization and lower tumor heterogeneity.
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Affiliation(s)
- Qiuyue Han
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, 200040, Shanghai, China
- Shanghai Institute of Medical Imaging, Shanghai, China
| | - Yiping Lu
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, 200040, Shanghai, China
| | - Dongdong Wang
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, 200040, Shanghai, China
| | - Xuanxuan Li
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, 200040, Shanghai, China
| | - Zhuoying Ruan
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, 200040, Shanghai, China
| | - Nan Mei
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, 200040, Shanghai, China
| | - Xiong Ji
- Department of Pathology, Huashan Hospital, Fudan University, Shanghai, China
| | - Daoying Geng
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, 200040, Shanghai, China.
- Center for Shanghai Intelligent Imaging for Critical Brain Diseases Engineering and Technology Research, Shanghai, China.
| | - Bo Yin
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, 200040, Shanghai, China.
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5
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Cho NS, Hagiwara A, Yao J, Nathanson DA, Prins RM, Wang C, Raymond C, Desousa BR, Divakaruni A, Morrow DH, Nghiemphu PL, Lai A, Liau LM, Everson RG, Salamon N, Pope WB, Cloughesy TF, Ellingson BM. Amine-weighted chemical exchange saturation transfer magnetic resonance imaging in brain tumors. NMR IN BIOMEDICINE 2023; 36:e4785. [PMID: 35704275 DOI: 10.1002/nbm.4785] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 05/31/2022] [Accepted: 06/10/2022] [Indexed: 05/23/2023]
Abstract
Amine-weighted chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) is particularly valuable as an amine- and pH-sensitive imaging technique in brain tumors, targeting the intrinsically high concentration of amino acids with exchangeable amine protons and reduced extracellular pH in brain tumors. Amine-weighted CEST MRI contrast is dependent on the glioma genotype, likely related to differences in degree of malignancy and metabolic behavior. Amine-weighted CEST MRI may provide complementary value to anatomic imaging in conventional and exploratory therapies in brain tumors, including chemoradiation, antiangiogenic therapies, and immunotherapies. Continual improvement and clinical testing of amine-weighted CEST MRI has the potential to greatly impact patients with brain tumors by understanding vulnerabilities in the tumor microenvironment that may be therapeutically exploited.
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Affiliation(s)
- Nicholas S Cho
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, California, USA
- Medical Scientist Training Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Akifumi Hagiwara
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Jingwen Yao
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, California, USA
| | - David A Nathanson
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Robert M Prins
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Chencai Wang
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Brandon R Desousa
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Ajit Divakaruni
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Danielle H Morrow
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Phioanh L Nghiemphu
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
- UCLA Brain Tumor Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Albert Lai
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
- UCLA Brain Tumor Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Linda M Liau
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Richard G Everson
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Noriko Salamon
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Whitney B Pope
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Timothy F Cloughesy
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
- UCLA Brain Tumor Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, California, USA
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
- UCLA Brain Tumor Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
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6
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Qi D, Li J, Quarles CC, Fonkem E, Wu E. Assessment and prediction of glioblastoma therapy response: challenges and opportunities. Brain 2023; 146:1281-1298. [PMID: 36445396 PMCID: PMC10319779 DOI: 10.1093/brain/awac450] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 11/03/2022] [Accepted: 11/10/2022] [Indexed: 11/30/2022] Open
Abstract
Glioblastoma is the most aggressive type of primary adult brain tumour. The median survival of patients with glioblastoma remains approximately 15 months, and the 5-year survival rate is <10%. Current treatment options are limited, and the standard of care has remained relatively constant since 2011. Over the last decade, a range of different treatment regimens have been investigated with very limited success. Tumour recurrence is almost inevitable with the current treatment strategies, as glioblastoma tumours are highly heterogeneous and invasive. Additionally, another challenging issue facing patients with glioblastoma is how to distinguish between tumour progression and treatment effects, especially when relying on routine diagnostic imaging techniques in the clinic. The specificity of routine imaging for identifying tumour progression early or in a timely manner is poor due to the appearance similarity of post-treatment effects. Here, we concisely describe the current status and challenges in the assessment and early prediction of therapy response and the early detection of tumour progression or recurrence. We also summarize and discuss studies of advanced approaches such as quantitative imaging, liquid biomarker discovery and machine intelligence that hold exceptional potential to aid in the therapy monitoring of this malignancy and early prediction of therapy response, which may decisively transform the conventional detection methods in the era of precision medicine.
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Affiliation(s)
- Dan Qi
- Department of Neurosurgery and Neuroscience Institute, Baylor Scott & White Health, Temple, TX 76502, USA
| | - Jing Li
- School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - C Chad Quarles
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Ekokobe Fonkem
- Department of Neurosurgery and Neuroscience Institute, Baylor Scott & White Health, Temple, TX 76502, USA
- Department of Medical Education, School of Medicine, Texas A&M University, Bryan, TX 77807, USA
| | - Erxi Wu
- Department of Neurosurgery and Neuroscience Institute, Baylor Scott & White Health, Temple, TX 76502, USA
- Department of Medical Education, School of Medicine, Texas A&M University, Bryan, TX 77807, USA
- Department of Pharmaceutical Sciences, Irma Lerma Rangel School of Pharmacy, Texas A&M University, College Station, TX 77843, USA
- Department of Oncology and LIVESTRONG Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA
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Kim MJ, Park JS, Jeun SS, Ahn S. A clinical evaluation of cystic features in patients with newly diagnosed glioblastoma with IDH-wildtype. Clin Neurol Neurosurg 2023; 228:107708. [PMID: 37043844 DOI: 10.1016/j.clineuro.2023.107708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 03/17/2023] [Accepted: 04/01/2023] [Indexed: 04/05/2023]
Abstract
BACKGROUND The prognostic significance of the presence of cystic features in patients with newly diagnosed glioblastoma (GB) is highly controversial. The purpose of this study was to determine whether cystic GB patients have a more favorable prognosis compared to non-cystic GB patients. METHODS The records of all GB patients diagnosed between August 2008 and December 2020 at Seoul St. Mary's Hospital were reviewed retrospectively. Out of 254 GB patients, we excluded patients with a confirmed isocitrate dehydrogenase (IDH) mutation or an unknown IDH mutation status. A total of 145 patients met our eligibility criteria. RESULTS Of the 145 patients we analyzed, 16 patients were classified as the cystic group, and 129 patients were classified into the non-cystic group. As there was a significant difference in the extent of resection between the two groups, 32 patients were matched according to propensity score matching. A Kaplan-Meier survival curve of the two groups indicated that the cystic group had better survival than the non-cystic group (28.6 months versus 18.8 months, respectively; p = 0.055). On multivariate analysis, the presence of cystic features (hazard ratio [HR]: 0.40, 95% confidence interval [CI]: 0.17-0.91, p = 0.029) was significantly related with a longer OS. Longer OS was also related with well-known prognostic factors, such as grossly total resection (HR: 0.05, CI: 0.01-0.31, respectively; p = 0.001) and lower European Cooperative Oncology Group (ECOG) score (HR: 3.67, CI: 1.56-9.02, respectively; p = 0.003). CONCLUSION Our results suggest that the presence of cystic features could be an independent prognostic factor suggesting better survival in GB patients. Further larger and prospective studies to validate our findings are needed.
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Affiliation(s)
- Min Joo Kim
- College of Medicine, the Catholic University of Korea, Seoul, South Korea
| | - Jae-Sung Park
- Department of Neurosurgery, Seoul St. Mary's Hospital, College of Medicine, the Catholic University of Korea, Seoul, South Korea
| | - Sin-Soo Jeun
- Department of Neurosurgery, Seoul St. Mary's Hospital, College of Medicine, the Catholic University of Korea, Seoul, South Korea
| | - Stephen Ahn
- Department of Neurosurgery, Seoul St. Mary's Hospital, College of Medicine, the Catholic University of Korea, Seoul, South Korea.
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Anil A, Stokes AM, Chao R, Hu LS, Alhilali L, Karis JP, Bell LC, Quarles CC. Identification of single-dose, dual-echo based CBV threshold for fractional tumor burden mapping in recurrent glioblastoma. Front Oncol 2023; 13:1046629. [PMID: 36733305 PMCID: PMC9887158 DOI: 10.3389/fonc.2023.1046629] [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: 09/16/2022] [Accepted: 01/03/2023] [Indexed: 01/18/2023] Open
Abstract
Background Relative cerebral blood volume (rCBV) obtained from dynamic susceptibility contrast (DSC) MRI is widely used to distinguish high grade glioma recurrence from post treatment radiation effects (PTRE). Application of rCBV thresholds yield maps to distinguish between regional tumor burden and PTRE, a biomarker termed the fractional tumor burden (FTB). FTB is generally measured using conventional double-dose, single-echo DSC-MRI protocols; recently, a single-dose, dual-echo DSC-MRI protocol was clinically validated by direct comparison to the conventional double-dose, single-echo protocol. As the single-dose, dual-echo acquisition enables reduction in the contrast agent dose and provides greater pulse sequence parameter flexibility, there is a compelling need to establish dual-echo DSC-MRI based FTB mapping. In this study, we determine the optimum standardized rCBV threshold for the single-dose, dual-echo protocol to generate FTB maps that best match those derived from the reference standard, double-dose, single-echo protocol. Methods The study consisted of 23 high grade glioma patients undergoing perfusion scans to confirm suspected tumor recurrence. We sequentially acquired single dose, dual-echo and double dose, single-echo DSC-MRI data. For both protocols, we generated leakage-corrected standardized rCBV maps. Standardized rCBV (sRCBV) thresholds of 1.0 and 1.75 were used to compute single-echo FTB maps as the reference for delineating PTRE (sRCBV < 1.0), tumor with moderate angiogenesis (1.0 < sRCBV < 1.75), and tumor with high angiogenesis (sRCBV > 1.75) regions. To assess the sRCBV agreement between acquisition protocols, the concordance correlation coefficient (CCC) was computed between the mean tumor sRCBV values across the patients. A receiver operating characteristics (ROC) analysis was performed to determine the optimum dual-echo sRCBV threshold. The sensitivity, specificity, and accuracy were compared between the obtained optimized threshold (1.64) and the standard reference threshold (1.75) for the dual-echo sRCBV threshold. Results The mean tumor sRCBV values across the patients showed a strong correlation (CCC = 0.96) between the two protocols. The ROC analysis showed maximum accuracy at thresholds of 1.0 (delineate PTRE from tumor) and 1.64 (differentiate aggressive tumors). The reference threshold (1.75) and the obtained optimized threshold (1.64) yielded similar accuracy, with slight differences in sensitivity and specificity which were not statistically significant (1.75 threshold: Sensitivity = 81.94%; Specificity: 87.23%; Accuracy: 84.58% and 1.64 threshold: Sensitivity = 84.48%; Specificity: 84.97%; Accuracy: 84.73%). Conclusions The optimal sRCBV threshold for single-dose, dual-echo protocol was found to be 1.0 and 1.64 for distinguishing tumor recurrence from PTRE; however, minimal differences were observed when using the standard threshold (1.75) as the upper threshold, suggesting that the standard threshold could be used for both protocols. While the prior study validated the agreement of the mean sRCBV values between the protocols, this study confirmed that their voxel-wise agreement is suitable for reliable FTB mapping. Dual-echo DSC-MRI acquisitions enable robust single-dose sRCBV and FTB mapping, provide pulse sequence parameter flexibility and should improve reproducibility by mitigating variations in preload dose and incubation time.
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Affiliation(s)
- Aliya Anil
- Division of Neuroimaging Research and Barrow Neuroimaging Innovation Center, Barrow Neuroimaging Institute, Phoenix, AZ, United States
| | - Ashley M. Stokes
- Division of Neuroimaging Research and Barrow Neuroimaging Innovation Center, Barrow Neuroimaging Institute, Phoenix, AZ, United States
| | - Renee Chao
- Division of Neuroimaging Research and Barrow Neuroimaging Innovation Center, Barrow Neuroimaging Institute, Phoenix, AZ, United States
| | - Leland S. Hu
- Department of Radiology, Division of Neuroradiology, Mayo Clinic Arizona, Phoenix, AZ, United States
| | - Lea Alhilali
- Neuroradiology, Southwest Neuroimaging at Barrow Neurological Institute, Phoenix, AZ, United States
| | - John P. Karis
- Neuroradiology, Southwest Neuroimaging at Barrow Neurological Institute, Phoenix, AZ, United States
| | - Laura C. Bell
- Early Clinical Development, Genentech, San Francisco, CA, United States
| | - C. Chad Quarles
- Cancer System Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States,*Correspondence: C. Chad Quarles,
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Sequential and Hybrid PET/MRI Acquisition in Follow-Up Examination of Glioblastoma Show Similar Diagnostic Performance. Cancers (Basel) 2022; 15:cancers15010083. [PMID: 36612079 PMCID: PMC9817523 DOI: 10.3390/cancers15010083] [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: 10/28/2022] [Revised: 12/09/2022] [Accepted: 12/20/2022] [Indexed: 12/25/2022] Open
Abstract
Both positron emission tomography (PET) and magnetic resonance imaging (MRI), including dynamic susceptibility contrast perfusion (DSC-PWI), are crucial for treatment monitoring of patients with high-grade gliomas. In clinical practice, they are usually conducted at separate time points. Whether this affects their diagnostic performance is presently unclear. To this end, we retrospectively reviewed 38 patients with pathologically confirmed glioblastoma (IDH wild-type) and suspected tumor recurrence after radiotherapy. Only patients who received both a PET−MRI (where DSC perfusion was acquired simultaneously with a FET-PET) and a separate MRI exam (including DSC perfusion) were included. Tumors were automatically segmented into contrast-enhancing tumor (CET), necrosis, and edema. To compare the simultaneous as well as the sequential DSC perfusion to the FET-PET, we calculated Dice overlap, global mutual information as well as voxel-wise Spearman correlation of hotspot areas. For the joint assessment of PET and MRI, we computed logistic regression models for the differentiation between true progression (PD) and treatment-related changes (TRC) using simultaneously or sequentially acquired images as input data. We observed no significant differences between Dice overlap (p = 0.17; paired t-test), mutual information (p = 0.18; paired t-test) and Spearman correlation (p = 0.90; paired t-test) when comparing simultaneous PET−MRI and sequential PET/MRI acquisition. This also held true for the subgroup of patients with >14 days between exams. Importantly, for the diagnostic performance, ROC analysis showed similar AUCs for differentiation of PD and TRC (AUC simultaneous PET: 0.77; AUC sequential PET: 0.78; p = 0.83, DeLong’s test). We found no relevant differences between simultaneous and sequential acquisition of FET-PET and DSC perfusion, also regarding their diagnostic performance. Given the increasing attention to multi-parametric assessment of glioma treatment response, our results reassuringly suggest that sequential acquisition is clinically and scientifically acceptable.
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10
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Starck L, Skeie BS, Moen G, Grüner R. Dynamic Susceptibility Contrast MRI May Contribute in Prediction of Stereotactic Radiosurgery Outcome in Brain Metastases. Neurooncol Adv 2022; 4:vdac070. [PMID: 35673606 PMCID: PMC9167634 DOI: 10.1093/noajnl/vdac070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Background Following stereotactic radiosurgery (SRS), predicting treatment response is not possible at an early stage using structural imaging alone. Hence, the current study aims at investigating whether dynamic susceptibility contrast (DSC)-MRI estimated prior to SRS can provide predictive biomarkers in response to SRS treatment and characterize vascular characteristics of pseudo-progression. Methods In this retrospective study, perfusion-weighted DSC-MRI image data acquired with a temporal resolution of 1.45 seconds were collected from 41 patients suffering from brain metastases. Outcome was defined based on lesion volume changes in time (determined on structural images) or death. Motion correction and manual lesion delineation were performed prior to semi-automated, voxel-wise perfusion analysis. Statistical testing was performed using linear regression and a significance threshold at P = .05. Age, sex, primary cancers (pulmonary cancer and melanoma), lesion volume, and dichotomized survival time were added as covariates in the linear regression models (ANOVA). Results Relative cerebral blood volume (rCBV) and relative cerebral blood flow (rCBF) were found to be significantly lower prior to SRS treatment in patients with increasing lesion volume or early death post-SRS (P ≤ .01). Conclusion Unfavorable treatment outcome may be linked to low perfusion prior to SRS. Pseudo-progression may be preceded by a transient rCBF increase post-SRS. However, results should be verified in different or larger patient material.
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Affiliation(s)
- Lea Starck
- Department of Physics and Technology, University of Bergen, Norway
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | | | - Gunnar Moen
- Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Renate Grüner
- Department of Physics and Technology, University of Bergen, Norway
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Department of Radiology, Haukeland University Hospital, Bergen, Norway
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11
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A Comparative and Summative Study of Radiomics-based Overall Survival Prediction in Glioblastoma Patients. J Comput Assist Tomogr 2022; 46:470-479. [PMID: 35405713 DOI: 10.1097/rct.0000000000001300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
PURPOSE This study aimed to assess different machine learning models based on radiomic features, Visually Accessible Rembrandt Images features and clinical characteristics in overall survival prediction of glioblastoma and to identify the reproducible features. MATERIALS AND METHODS Patients with preoperative magnetic resonance scans were allocated into 3 data sets. The Least Absolute Shrinkage and Selection Operator was used for feature selection. The prediction models were built by random survival forest (RSF) and Cox regression. C-index and integrated Brier scores were calculated to compare model performances. RESULTS Patients with cortical involvement had shorter survival times in the training set (P = 0.006). Random survival forest showed higher C-index than Cox, and the RSF model based on the radiomic features was the best one (testing set: C-index = 0.935 ± 0.023). Ten reproducible radiomic features were summarized. CONCLUSIONS The RSF model based on radiomic features had promising potential in predicting overall survival of glioblastoma. Ten reproducible features were identified.
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12
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Henriksen OM, del Mar Álvarez-Torres M, Figueiredo P, Hangel G, Keil VC, Nechifor RE, Riemer F, Schmainda KM, Warnert EAH, Wiegers EC, Booth TC. High-Grade Glioma Treatment Response Monitoring Biomarkers: A Position Statement on the Evidence Supporting the Use of Advanced MRI Techniques in the Clinic, and the Latest Bench-to-Bedside Developments. Part 1: Perfusion and Diffusion Techniques. Front Oncol 2022; 12:810263. [PMID: 35359414 PMCID: PMC8961422 DOI: 10.3389/fonc.2022.810263] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 01/05/2022] [Indexed: 01/16/2023] Open
Abstract
Objective Summarize evidence for use of advanced MRI techniques as monitoring biomarkers in the clinic, and highlight the latest bench-to-bedside developments. Methods Experts in advanced MRI techniques applied to high-grade glioma treatment response assessment convened through a European framework. Current evidence regarding the potential for monitoring biomarkers in adult high-grade glioma is reviewed, and individual modalities of perfusion, permeability, and microstructure imaging are discussed (in Part 1 of two). In Part 2, we discuss modalities related to metabolism and/or chemical composition, appraise the clinic readiness of the individual modalities, and consider post-processing methodologies involving the combination of MRI approaches (multiparametric imaging) or machine learning (radiomics). Results High-grade glioma vasculature exhibits increased perfusion, blood volume, and permeability compared with normal brain tissue. Measures of cerebral blood volume derived from dynamic susceptibility contrast-enhanced MRI have consistently provided information about brain tumor growth and response to treatment; it is the most clinically validated advanced technique. Clinical studies have proven the potential of dynamic contrast-enhanced MRI for distinguishing post-treatment related effects from recurrence, but the optimal acquisition protocol, mode of analysis, parameter of highest diagnostic value, and optimal cut-off points remain to be established. Arterial spin labeling techniques do not require the injection of a contrast agent, and repeated measurements of cerebral blood flow can be performed. The absence of potential gadolinium deposition effects allows widespread use in pediatric patients and those with impaired renal function. More data are necessary to establish clinical validity as monitoring biomarkers. Diffusion-weighted imaging, apparent diffusion coefficient analysis, diffusion tensor or kurtosis imaging, intravoxel incoherent motion, and other microstructural modeling approaches also allow treatment response assessment; more robust data are required to validate these alone or when applied to post-processing methodologies. Conclusion Considerable progress has been made in the development of these monitoring biomarkers. Many techniques are in their infancy, whereas others have generated a larger body of evidence for clinical application.
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Affiliation(s)
- Otto M. Henriksen
- Department of Clinical Physiology, Nuclear Medicine and PET, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | | | - Patricia Figueiredo
- Department of Bioengineering and Institute for Systems and Robotics-Lisboa, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Gilbert Hangel
- Department of Neurosurgery, Medical University, Vienna, Austria
- High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University, Vienna, Austria
| | - Vera C. Keil
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Amsterdam, Netherlands
| | - Ruben E. Nechifor
- International Institute for the Advanced Studies of Psychotherapy and Applied Mental Health, Department of Clinical Psychology and Psychotherapy, Babes-Bolyai University, Cluj-Napoca, Romania
| | - Frank Riemer
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Kathleen M. Schmainda
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, United States
| | | | - Evita C. Wiegers
- Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Thomas C. Booth
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School of Biomedical Engineering and Imaging Sciences, St. Thomas’ Hospital, King’s College London, London, United Kingdom
- Department of Neuroradiology, King’s College Hospital NHS Foundation Trust, London, United Kingdom
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13
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Zhou Q, Xue C, Ke X, Zhou J. Treatment Response and Prognosis Evaluation in High-Grade Glioma: An Imaging Review Based on MRI. J Magn Reson Imaging 2022; 56:325-340. [PMID: 35129845 DOI: 10.1002/jmri.28103] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 01/25/2022] [Accepted: 01/25/2022] [Indexed: 12/19/2022] Open
Abstract
In recent years, the development of advanced magnetic resonance imaging (MRI) technology and machine learning (ML) have created new tools for evaluating treatment response and prognosis of patients with high-grade gliomas (HGG); however, patient prognosis has not improved significantly. This is mainly due to the heterogeneity between and within HGG tumors, resulting in standard treatment methods not benefitting all patients. Moreover, the survival of patients with HGG is not only related to tumor cells, but also to noncancer cells in the tumor microenvironment (TME). Therefore, during preoperative diagnosis and follow-up treatment of patients with HGG, noninvasive imaging markers are needed to characterize intratumoral heterogeneity, and then to evaluate treatment response and predict prognosis, timeously adjust treatment strategies, and achieve individualized diagnosis and treatment. In this review, we summarize the research progress of conventional MRI, advanced MRI technology, and ML in evaluation of treatment response and prognosis of patients with HGG. We further discuss the significance of the TME in the prognosis of HGG patients, associate imaging features with the TME, indirectly reflecting the heterogeneity within the tumor, and shifting treatment strategies from tumor cells alone to systemic therapy of the TME, which may be a major development direction in the future. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 4.
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Affiliation(s)
- Qing Zhou
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, Gansu, China.,Second Clinical School, Lanzhou University, Lanzhou, Gansu, China.,Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, Gansu, China.,Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, Gansu, China
| | - Caiqiang Xue
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, Gansu, China.,Second Clinical School, Lanzhou University, Lanzhou, Gansu, China.,Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, Gansu, China.,Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, Gansu, China
| | - Xiaoai Ke
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, Gansu, China.,Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, Gansu, China.,Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, Gansu, China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, Gansu, China.,Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, Gansu, China.,Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, Gansu, China
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14
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Stumpo V, Sebök M, van Niftrik CHB, Seystahl K, Hainc N, Kulcsar Z, Weller M, Regli L, Fierstra J. Feasibility of glioblastoma tissue response mapping with physiologic BOLD imaging using precise oxygen and carbon dioxide challenge. MAGMA (NEW YORK, N.Y.) 2022; 35:29-44. [PMID: 34874499 DOI: 10.1007/s10334-021-00980-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 11/15/2021] [Accepted: 11/19/2021] [Indexed: 12/15/2022]
Abstract
OBJECTIVES Innovative physiologic MRI development focuses on depiction of heterogenous vascular and metabolic features in glioblastoma. For this feasibility study, we employed blood oxygenation level-dependent (BOLD) MRI with standardized and precise carbon dioxide (CO2) and oxygen (O2) modulation to investigate specific tumor tissue response patterns in patients with newly diagnosed glioblastoma. MATERIALS AND METHODS Seven newly diagnosed untreated patients with suspected glioblastoma were prospectively included to undergo a BOLD study with combined CO2 and O2 standardized protocol. %BOLD signal change/mmHg during hypercapnic, hypoxic, and hyperoxic stimulus was calculated in the whole brain, tumor lesion and segmented volumes of interest (VOI) [contrast-enhancing (CE) - tumor, necrosis and edema] to analyze their tissue response patterns. RESULTS Quantification of BOLD signal change after gas challenges can be used to identify specific responses to standardized stimuli in glioblastoma patients. Integration of this approach with automatic VOI segmentation grants improved characterization of tumor subzones and edema. Magnitude of BOLD signal change during the 3 stimuli can be visualized at voxel precision through color-coded maps overlayed onto whole brain and identified VOIs. CONCLUSIONS Our preliminary investigation shows good feasibility of BOLD with standardized and precise CO2 and O2 modulation as an emerging physiologic imaging technique to detail specific glioblastoma characteristics. The unique tissue response patterns generated can be further investigated to better detail glioblastoma lesions and gauge treatment response.
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Affiliation(s)
- Vittorio Stumpo
- Department of Neurosurgery, University Hospital Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland. .,Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
| | - Martina Sebök
- Department of Neurosurgery, University Hospital Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland.,Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Christiaan Hendrik Bas van Niftrik
- Department of Neurosurgery, University Hospital Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland.,Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Katharina Seystahl
- Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland.,Department of Neurology, University Hospital Zurich, Zurich, Switzerland
| | - Nicolin Hainc
- Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland.,Department of Neuroradiology, University Hospital Zurich, Zurich, Switzerland
| | - Zsolt Kulcsar
- Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland.,Department of Neuroradiology, University Hospital Zurich, Zurich, Switzerland
| | - Michael Weller
- Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland.,Department of Neurology, University Hospital Zurich, Zurich, Switzerland
| | - Luca Regli
- Department of Neurosurgery, University Hospital Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland.,Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Jorn Fierstra
- Department of Neurosurgery, University Hospital Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland.,Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
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15
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Karami G, Giuseppe Orlando M, Delli Pizzi A, Caulo M, Del Gratta C. Predicting Overall Survival Time in Glioblastoma Patients Using Gradient Boosting Machines Algorithm and Recursive Feature Elimination Technique. Cancers (Basel) 2021; 13:4976. [PMID: 34638460 PMCID: PMC8507924 DOI: 10.3390/cancers13194976] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 09/20/2021] [Accepted: 09/29/2021] [Indexed: 11/16/2022] Open
Abstract
Despite advances in tumor treatment, the inconsistent response is a major challenge among glioblastoma multiform (GBM) that lead to different survival time. Our aim was to integrate multimodal MRI with non-supervised and supervised machine learning methods to predict GBM patients' survival time. To this end, we identified different compartments of the tumor and extracted their features. Next, we applied Random Forest-Recursive Feature Elimination (RF-RFE) to identify the most relevant features to feed into a GBoost machine. This study included 29 GBM patients with known survival time. RF-RFE GBoost model was evaluated to assess the survival prediction performance using optimal features. Furthermore, overall survival (OS) was analyzed using univariate and multivariate Cox regression analyses, to evaluate the effect of ROIs and their features on survival. The results showed that a RF-RFE Gboost machine was able to predict survival time with 75% accuracy. The results also revealed that the rCBV in the low perfusion area was significantly different between groups and had the greatest effect size in terms of the rate of change of the response variable (survival time). In conclusion, not only integration of multi-modality MRI but also feature selection method can enhance the classifier performance.
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Affiliation(s)
- Golestan Karami
- Department of Neuroscience, Imaging and Clinical Sciences, Gabriele D'Annunzio University, 66100 Chieti, Italy
- Institute for Advanced Biomedical Technologies, Gabriele D'Annunzio University, 66100 Chieti, Italy
| | - Marco Giuseppe Orlando
- Department of Neuroscience, Imaging and Clinical Sciences, Gabriele D'Annunzio University, 66100 Chieti, Italy
| | - Andrea Delli Pizzi
- Department of Neuroscience, Imaging and Clinical Sciences, Gabriele D'Annunzio University, 66100 Chieti, Italy
- Institute for Advanced Biomedical Technologies, Gabriele D'Annunzio University, 66100 Chieti, Italy
| | - Massimo Caulo
- Department of Neuroscience, Imaging and Clinical Sciences, Gabriele D'Annunzio University, 66100 Chieti, Italy
- Institute for Advanced Biomedical Technologies, Gabriele D'Annunzio University, 66100 Chieti, Italy
| | - Cosimo Del Gratta
- Department of Neuroscience, Imaging and Clinical Sciences, Gabriele D'Annunzio University, 66100 Chieti, Italy
- Institute for Advanced Biomedical Technologies, Gabriele D'Annunzio University, 66100 Chieti, Italy
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16
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Advanced Imaging and Computational Techniques for the Diagnostic and Prognostic Assessment of Malignant Gliomas. Cancer J 2021; 27:344-352. [PMID: 34570448 DOI: 10.1097/ppo.0000000000000545] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
ABSTRACT Advanced imaging techniques provide a powerful tool to assess the intratumoral and intertumoral heterogeneity of gliomas. Advances in the molecular understanding of glioma subgroups may allow improved diagnostic assessment combining imaging and molecular tumor features, with enhanced prognostic utility and implications for patient treatment. In this article, a comprehensive overview of the physiologic basis for conventional and advanced imaging techniques is presented, and clinical applications before and after treatment are discussed. An introduction to the principles of radiomics and the advanced integration of imaging, clinical outcomes, and genomic data highlights the future potential for this field of research to better stratify and select patients for standard as well as investigational therapies.
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17
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Oguntade AS, Al-Amodi F, Alrumayh A, Alobaida M, Bwalya M. Anti-angiogenesis in cancer therapeutics: the magic bullet. J Egypt Natl Canc Inst 2021; 33:15. [PMID: 34212275 DOI: 10.1186/s43046-021-00072-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 06/08/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Angiogenesis is the formation of new vascular networks from preexisting ones through the migration and proliferation of differentiated endothelial cells. Available evidence suggests that while antiangiogenic therapy could inhibit tumour growth, the response to these agents is not sustained. The aim of this paper was to review the evidence for anti-angiogenic therapy in cancer therapeutics and the mechanisms and management of tumour resistance to antiangiogenic agents. We also explored the latest advances and challenges in this field. MEDLINE and EMBASE databases were searched for publications on antiangiogenic therapy in cancer therapeutics from 1990 to 2020. Vascular endothelial growth factor (VEGF) is the master effector of the angiogenic response in cancers. Anti-angiogenic agents targeting the VEGF and HIF-α pathways include monoclonal antibodies to VEGF (e.g. bevacizumab), small-molecule tyrosine kinase inhibitors (TKIs) e.g. sorafenib, decoy receptor or VEGF trap e.g. aflibercept and VEGFR2 inhibitors (e.g. ramucirumab). These classes of drugs are vascular targeting which in many ways are advantageous over tumour cell targeting drugs. Their use leads to a reduction in the tumour blood supply and growth of the tumour blood vessels. Tumour resistance and cardiovascular toxicity are important challenges which limit the efficacy and long-term use of anti-angiogenic agents in cancer therapeutics. Tumour resistance can be overcome by dual anti-angiogenic therapy or combination with conventional chemotherapy and immunotherapy. Emerging nanoparticle-based therapy which can silence the expression of HIF-α gene expression by antisense oligonucleotides or miRNAs has been developed. Effective delivery platforms are required for such therapy. SHORT CONCLUSION Clinical surveillance is important for the early detection of tumour resistance and treatment failure using reliable biomarkers. It is hoped that the recent interest in mesenchymal cell-based and exosome-based nanoparticle delivery platforms will improve the cellular delivery of newer anti-angiogenics in cancer therapeutics.
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Affiliation(s)
- Ayodipupo S Oguntade
- Nuffield Department of Population Health, University of Oxford, Oxford, UK. .,Institute of Cardiovascular Science, University College London, London, UK.
| | - Faez Al-Amodi
- Institute of Cardiovascular Science, University College London, London, UK
| | - Abdullah Alrumayh
- Institute of Cardiovascular Science, University College London, London, UK.,Department of Basic Science, Prince Sultan Bin Abdulaziz College for Emergency Medical Services, King Saud University, Riyadh, Saudi Arabia
| | - Muath Alobaida
- Institute of Cardiovascular Science, University College London, London, UK.,Department of Basic Science, Prince Sultan Bin Abdulaziz College for Emergency Medical Services, King Saud University, Riyadh, Saudi Arabia
| | - Mwango Bwalya
- Institute of Cardiovascular Science, University College London, London, UK
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18
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Kim MM, Sun Y, Aryal MP, Parmar HA, Piert M, Rosen B, Mayo CS, Balter JM, Schipper M, Gabel N, Briceño EM, You D, Heth J, Al-Holou W, Umemura Y, Leung D, Junck L, Wahl DR, Lawrence TS, Cao Y. A Phase 2 Study of Dose-intensified Chemoradiation Using Biologically Based Target Volume Definition in Patients With Newly Diagnosed Glioblastoma. Int J Radiat Oncol Biol Phys 2021; 110:792-803. [PMID: 33524546 PMCID: PMC8920120 DOI: 10.1016/j.ijrobp.2021.01.033] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 01/14/2021] [Accepted: 01/21/2021] [Indexed: 12/15/2022]
Abstract
PURPOSE We hypothesized that dose-intensified chemoradiation therapy targeting adversely prognostic hypercellular (TVHCV) and hyperperfused (TVCBV) tumor volumes would improve outcomes in patients with glioblastoma. METHODS AND MATERIALS This single-arm, phase 2 trial enrolled adult patients with newly diagnosed glioblastoma. Patients with a TVHCV/TVCBV >1 cm3, identified using high b-value diffusion-weighted magnetic resonance imaging (MRI) and dynamic contrast-enhanced perfusion MRI, were treated over 30 fractions to 75 Gy to the TVHCV/TVCBV with temozolomide. The primary objective was to estimate improvement in 12-month overall survival (OS) versus historical control. Secondary objectives included evaluating the effect of 3-month TVHCV/TVCBV reduction on OS using Cox proportional-hazard regression and characterizing coverage (95% isodose line) of metabolic tumor volumes identified using correlative 11C-methionine positron emission tomography. Clinically meaningful change was assessed for quality of life by the European Organisation for the Research and Treatment of Cancer Quality of Life Questionnaire C30, for symptom burden by the MD Anderson Symptom Inventory for brain tumor, and for neurocognitive function (NCF) by the Controlled Oral Word Association Test, the Trail Making Test, parts A and B, and the Hopkins Verbal Learning Test-Revised. RESULTS Between 2016 and 2018, 26 patients were enrolled. Initial patients were boosted to TVHCV alone, and 13 patients were boosted to both TVHCV/TVCBV. Gross or subtotal resection was performed in 87% of patients; 22% were O6-methylguanine-DNA methyltransferase (MGMT) methylated. With 26-month follow-up (95% CI, 19-not reached), the 12-month OS rate among patients boosted to the combined TVHCV/TVCBV was 92% (95% CI, 78%-100%; P = .03) and the median OS was 20 months (95% CI, 18-not reached); the median OS for the whole study cohort was 20 months (95% CI, 14-29 months). Patients whose 3-month TVHCV/TVCBV decreased to less than the median volume (3 cm3) had superior OS (29 vs 12 months; P = .02). Only 5 patients had central or in-field failures, and 93% (interquartile range, 59%-100%) of the 11C-methionine metabolic tumor volumes received high-dose coverage. Late grade 3 neurologic toxicity occurred in 2 patients. Among non-progressing patients, 1-month and 7-month deterioration in quality of life, symptoms, and NCF were similar in incidence to standard therapy. CONCLUSIONS Dose intensification against hypercellular/hyperperfused tumor regions in glioblastoma yields promising OS with favorable outcomes for NCF, symptom burden, and quality of life, particularly among patients with greater tumor reduction 3 months after radiation therapy.
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Affiliation(s)
- Michelle M Kim
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan.
| | - Yilun Sun
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan; Department of Biostatistics, University of Michigan, Ann Arbor, Michigan
| | - Madhava P Aryal
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Hemant A Parmar
- Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - Morand Piert
- Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - Benjamin Rosen
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Charles S Mayo
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - James M Balter
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Matthew Schipper
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan; Department of Biostatistics, University of Michigan, Ann Arbor, Michigan
| | - Nicolette Gabel
- Department of Physical Medicine and Rehabilitation, University of Michigan, Ann Arbor, Michigan
| | - Emily M Briceño
- Department of Physical Medicine and Rehabilitation, University of Michigan, Ann Arbor, Michigan
| | - Daekeun You
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Jason Heth
- Department of Neurosurgery, University of Michigan, Ann Arbor, Michigan
| | - Wajd Al-Holou
- Department of Neurosurgery, University of Michigan, Ann Arbor, Michigan
| | - Yoshie Umemura
- Department of Neurology, University of Michigan, Ann Arbor, Michigan
| | - Denise Leung
- Department of Neurology, University of Michigan, Ann Arbor, Michigan
| | - Larry Junck
- Department of Neurology, University of Michigan, Ann Arbor, Michigan
| | - Daniel R Wahl
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Theodore S Lawrence
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Yue Cao
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan; Department of Radiology, University of Michigan, Ann Arbor, Michigan; Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan
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19
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Al Feghali KA, Randall JW, Liu DD, Wefel JS, Brown PD, Grosshans DR, McAvoy SA, Farhat MA, Li J, McGovern SL, McAleer MF, Ghia AJ, Paulino AC, Sulman EP, Penas-Prado M, Wang J, de Groot J, Heimberger AB, Armstrong TS, Gilbert MR, Mahajan A, Guha-Thakurta N, Chung C. Phase II trial of proton therapy versus photon IMRT for GBM: secondary analysis comparison of progression-free survival between RANO versus clinical assessment. Neurooncol Adv 2021; 3:vdab073. [PMID: 34337411 PMCID: PMC8320688 DOI: 10.1093/noajnl/vdab073] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Background This secondary image analysis of a randomized trial of proton radiotherapy (PT) versus photon intensity-modulated radiotherapy (IMRT) compares tumor progression based on clinical radiological assessment versus Response Assessment in Neuro-Oncology (RANO). Methods Eligible patients were enrolled in the randomized trial and had MR imaging at baseline and follow-up beyond 12 weeks from completion of radiotherapy. “Clinical progression” was based on a clinical radiology report of progression and/or change in treatment for progression. Results Of 90 enrolled patients, 66 were evaluable. Median clinical progression-free survival (PFS) was 10.8 (range: 9.4–14.7) months; 10.8 months IMRT versus 11.2 months PT (P = .14). Median RANO-PFS was 8.2 (range: 6.9, 12): 8.9 months IMRT versus 6.6 months PT (P = .24). RANO-PFS was significantly shorter than clinical PFS overall (P = .001) and for both the IMRT (P = .01) and PT (P = .04) groups. There were 31 (46.3%) discrepant cases of which 17 had RANO progression more than a month prior to clinical progression, and 14 had progression by RANO but not clinical criteria. Conclusions Based on this secondary analysis of a trial of PT versus IMRT for glioblastoma, while no difference in PFS was noted relative to treatment technique, RANO criteria identified progression more often and earlier than clinical assessment. This highlights the disconnect between measures of tumor response in clinical trials versus clinical practice. With growing efforts to utilize real-world data and personalized treatment with timely adaptation, there is a growing need to improve the consistency of determining tumor progression within clinical trials and clinical practice.
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Affiliation(s)
- Karine A Al Feghali
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, Texas, USA
| | - James W Randall
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, Texas, USA
| | - Diane D Liu
- Department of Biostatistics, MD Anderson Cancer Center, Houston, Texas, USA
| | - Jeffrey S Wefel
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, Texas, USA.,Department of Neuro-Oncology, MD Anderson Cancer Center, Houston, Texas, USA
| | - Paul D Brown
- Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota, USA
| | - David R Grosshans
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, Texas, USA
| | - Sarah A McAvoy
- Department of Radiation Oncology, University of Maryland, Baltimore, Maryland, USA
| | - Maguy A Farhat
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, Texas, USA
| | - Jing Li
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, Texas, USA
| | - Susan L McGovern
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, Texas, USA
| | - Mary F McAleer
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, Texas, USA
| | - Amol J Ghia
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, Texas, USA
| | - Arnold C Paulino
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, Texas, USA
| | - Erik P Sulman
- Department of Radiation Oncology, NYU Langone, New York, New York, USA
| | - Marta Penas-Prado
- Department of Neuro-Oncology, National Institutes of Health, Bethesda, Maryland, USA
| | - Jihong Wang
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, Texas, USA
| | - John de Groot
- Department of Neuro-Oncology, MD Anderson Cancer Center, Houston, Texas, USA
| | - Amy B Heimberger
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, Texas, USA
| | - Terri S Armstrong
- Department of Neuro-Oncology, National Institutes of Health, Bethesda, Maryland, USA
| | - Mark R Gilbert
- Department of Neuro-Oncology, National Institutes of Health, Bethesda, Maryland, USA
| | - Anita Mahajan
- Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Caroline Chung
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, Texas, USA
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20
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Multimodal assessment of disease activity in glioblastoma : A single center experience. Wien Klin Wochenschr 2021; 133:1148-1154. [PMID: 33877437 DOI: 10.1007/s00508-021-01859-2] [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: 02/11/2021] [Accepted: 03/22/2021] [Indexed: 10/21/2022]
Abstract
BACKGROUND Assessment of disease activity in glioblastoma (GBM) can be challenging due to several clinical and radiological pitfalls. Besides MRI, FET-PET and neurocognitive assessment (NA) are used in several neuro-oncological centers in order to improve the specificity of response assessment. We performed a retrospective study to investigate whether the assessment by RANO (Response Assessment in NeuroOncology) corresponds to FET-PET imaging and NA results. Moreover, the concordance of RANO with a final recommendation of an interdisciplinary neuro-oncological tumor board recommendation (TBR) was analyzed. METHODS We enrolled 25 consecutive patients with newly diagnosed histologically confirmed GBM in a pilot study, accounting for 81 multimodal test results. All patients were selected after undergoing consecutive follow-up comprising MRI, FET-PET, and NA with a subsequent TBR. Results were analyzed for correlations between RANO, FET-PET and NA. An additional consistency analysis was performed to elucidate the impact of RANO on decision making. RESULTS A highly statistically significant correlation was found between RANO and FET-PET and NA results (all P < 0.01); however, 26% of follow-up tests exhibited inconsistent results in multimodal assessment, among which RANO was only 48% in accordance with the final TBR. The concordance of NA and FET-PET with the final TBR was 67% and 86%, respectively. CONCLUSION The RANO proved its value in the context of multimodal assessment of disease activity in GBM; however, because the implementation of multimodal assessment showed a considerably high percentage of inconsistent results, further studies are required to investigate the relationship between different assessment techniques, in addition to their overall significance to response rating.
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21
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Theranostics in Boron Neutron Capture Therapy. Life (Basel) 2021; 11:life11040330. [PMID: 33920126 PMCID: PMC8070338 DOI: 10.3390/life11040330] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 04/07/2021] [Accepted: 04/08/2021] [Indexed: 12/15/2022] Open
Abstract
Boron neutron capture therapy (BNCT) has the potential to specifically destroy tumor cells without damaging the tissues infiltrated by the tumor. BNCT is a binary treatment method based on the combination of two agents that have no effect when applied individually: 10B and thermal neutrons. Exclusively, the combination of both produces an effect, whose extent depends on the amount of 10B in the tumor but also on the organs at risk. It is not yet possible to determine the 10B concentration in a specific tissue using non-invasive methods. At present, it is only possible to measure the 10B concentration in blood and to estimate the boron concentration in tissues based on the assumption that there is a fixed uptake of 10B from the blood into tissues. On this imprecise assumption, BNCT can hardly be developed further. A therapeutic approach, combining the boron carrier for therapeutic purposes with an imaging tool, might allow us to determine the 10B concentration in a specific tissue using a non-invasive method. This review provides an overview of the current clinical protocols and preclinical experiments and results on how innovative drug development for boron delivery systems can also incorporate concurrent imaging. The last section focuses on the importance of proteomics for further optimization of BNCT, a highly precise and personalized therapeutic approach.
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22
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Overcast WB, Davis KM, Ho CY, Hutchins GD, Green MA, Graner BD, Veronesi MC. Advanced imaging techniques for neuro-oncologic tumor diagnosis, with an emphasis on PET-MRI imaging of malignant brain tumors. Curr Oncol Rep 2021; 23:34. [PMID: 33599882 PMCID: PMC7892735 DOI: 10.1007/s11912-021-01020-2] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/11/2021] [Indexed: 12/15/2022]
Abstract
PURPOSE OF REVIEW This review will explore the latest in advanced imaging techniques, with a focus on the complementary nature of multiparametric, multimodality imaging using magnetic resonance imaging (MRI) and positron emission tomography (PET). RECENT FINDINGS Advanced MRI techniques including perfusion-weighted imaging (PWI), MR spectroscopy (MRS), diffusion-weighted imaging (DWI), and MR chemical exchange saturation transfer (CEST) offer significant advantages over conventional MR imaging when evaluating tumor extent, predicting grade, and assessing treatment response. PET performed in addition to advanced MRI provides complementary information regarding tumor metabolic properties, particularly when performed simultaneously. 18F-fluoroethyltyrosine (FET) PET improves the specificity of tumor diagnosis and evaluation of post-treatment changes. Incorporation of radiogenomics and machine learning methods further improve advanced imaging. The complementary nature of combining advanced imaging techniques across modalities for brain tumor imaging and incorporating technologies such as radiogenomics has the potential to reshape the landscape in neuro-oncology.
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Affiliation(s)
- Wynton B. Overcast
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 550 N University Blvd. Room 0663, Indianapolis, IN 46202 USA
| | - Korbin M. Davis
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 550 N University Blvd. Room 0663, Indianapolis, IN 46202 USA
| | - Chang Y. Ho
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Goodman Hall, 355 West 16th Street, Suite 4100, Indianapolis, IN 46202 USA
| | - Gary D. Hutchins
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Research 2 Building (R2), Room E124, 920 W. Walnut Street, Indianapolis, IN 46202-5181 USA
| | - Mark A. Green
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Research 2 Building (R2), Room E124, 920 W. Walnut Street, Indianapolis, IN 46202-5181 USA
| | - Brian D. Graner
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Goodman Hall, 355 West 16th Street, Suite 4100, Indianapolis, IN 46202 USA
| | - Michael C. Veronesi
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Research 2 Building (R2), Room E174, 920 W. Walnut Street, Indianapolis, IN 46202-5181 USA
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23
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Riva M, Lopci E, Gay LG, Nibali MC, Rossi M, Sciortino T, Castellano A, Bello L. Advancing Imaging to Enhance Surgery: From Image to Information Guidance. Neurosurg Clin N Am 2021; 32:31-46. [PMID: 33223024 DOI: 10.1016/j.nec.2020.08.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Conventional magnetic resonance imaging (cMRI) has an established role as a crucial disease parameter in the multidisciplinary management of glioblastoma, guiding diagnosis, treatment planning, assessment, and follow-up. Yet, cMRI cannot provide adequate information regarding tissue heterogeneity and the infiltrative extent beyond the contrast enhancement. Advanced magnetic resonance imaging and PET and newer analytical methods are transforming images into data (radiomics) and providing noninvasive biomarkers of molecular features (radiogenomics), conveying enhanced information for improving decision making in surgery. This review analyzes the shift from image guidance to information guidance that is relevant for the surgical treatment of glioblastoma.
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Affiliation(s)
- Marco Riva
- Department of Medical Biotechnology and Translational Medicine, Università degli Studi di Milano, Via Festa del Perdono 7, Milan 20122, Italy; IRCCS Istituto Ortopedico Galeazzi, U.O. Neurochirurgia Oncologica, Milan, Italy.
| | - Egesta Lopci
- Unit of Nuclear Medicine, Humanitas Clinical and Research Center - IRCCS, Via Manzoni 56, Rozzano, Milan 20089, Italy. https://twitter.com/LopciEgesta
| | - Lorenzo G Gay
- IRCCS Istituto Ortopedico Galeazzi, U.O. Neurochirurgia Oncologica, Milan, Italy; Department of Oncology and Hemato-Oncology, Via Festa del Perdono 7, Milan 20122, Italy
| | - Marco Conti Nibali
- IRCCS Istituto Ortopedico Galeazzi, U.O. Neurochirurgia Oncologica, Milan, Italy; Department of Oncology and Hemato-Oncology, Via Festa del Perdono 7, Milan 20122, Italy. https://twitter.com/dr_mcn
| | - Marco Rossi
- IRCCS Istituto Ortopedico Galeazzi, U.O. Neurochirurgia Oncologica, Milan, Italy; Department of Oncology and Hemato-Oncology, Via Festa del Perdono 7, Milan 20122, Italy
| | - Tommaso Sciortino
- IRCCS Istituto Ortopedico Galeazzi, U.O. Neurochirurgia Oncologica, Milan, Italy; Department of Oncology and Hemato-Oncology, Via Festa del Perdono 7, Milan 20122, Italy
| | - Antonella Castellano
- Neuroradiology Unit and CERMAC, Vita-Salute San Raffaele University, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan 20123, Italy. https://twitter.com/antocastella
| | - Lorenzo Bello
- IRCCS Istituto Ortopedico Galeazzi, U.O. Neurochirurgia Oncologica, Milan, Italy; Department of Oncology and Hemato-Oncology, Via Festa del Perdono 7, Milan 20122, Italy
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24
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Hu LS, Brat DJ, Bloch O, Ramkissoon S, Lesser GJ. The Practical Application of Emerging Technologies Influencing the Diagnosis and Care of Patients With Primary Brain Tumors. Am Soc Clin Oncol Educ Book 2020; 40:1-12. [PMID: 32324425 DOI: 10.1200/edbk_280955] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Over the past decade, a variety of new and innovative technologies has led to important advances in the diagnosis and management of patients with primary malignant brain tumors. New approaches to surgical navigation and tumor localization, advanced imaging to define tumor biology and treatment response, and the widespread adoption of a molecularly defined integrated diagnostic paradigm that complements traditional histopathologic diagnosis continue to impact the day-to-day care of these patients. In the neuro-oncology clinic, discussions with patients about the role of tumor treating fields (TTFields) and the incorporation of next-generation sequencing (NGS) data into therapeutic decision-making are now a standard practice. This article summarizes newer applications of technology influencing the pathologic, neuroimaging, neurosurgical, and medical management of patients with malignant primary brain tumors.
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Affiliation(s)
- Leland S Hu
- Neuroradiology Section, Department of Radiology, Mayo Clinic, Phoenix, AZ
| | - Daniel J Brat
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Orin Bloch
- Department of Neurologic Surgery, UC Davis Comprehensive Cancer Center, Sacramento, CA
| | - Shakti Ramkissoon
- Foundation Medicine, Inc., Morrisville, NC.,Comprehensive Cancer Center, Wake Forest Baptist Health, Winston-Salem, NC.,Department of Pathology, Wake Forest School of Medicine, Winston-Salem, NC
| | - Glenn J Lesser
- Comprehensive Cancer Center, Wake Forest Baptist Health, Winston-Salem, NC
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25
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Swellam M, Bakr NM, El Magdoub HM, Hamza MS, Ezz El Arab LR. Emerging role of miRNAs as liquid biopsy markers for prediction of glioblastoma multiforme prognosis. J Mol Neurosci 2020; 71:836-844. [PMID: 32989634 DOI: 10.1007/s12031-020-01706-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 09/07/2020] [Indexed: 01/01/2023]
Abstract
Serum miRNAs (miRs) have gained consideration as encouraging molecular markers for cancer diagnosis and prediction of prognosis. The authors aimed to identify the exact role of miR-17-5p, miR-125b, and miR-221 among glioblastoma multiforme (GBM) patients before and after standard treatment, and correlate their expression with survival pattern. The study included 25 GBM patients and 20 healthy controls. Serum miR-17-5p, miR-125b, and miR-221 expression were analyzed before and after treatment using quantitative real-time polymerase chain reaction (qPCR). The diagnostic efficacy for the tested miRs was evaluated using the receiver operating characteristic (ROC) curve, and the relation of miRs expression versus clinical criteria for GBM was assessed. Patients' survival patterns were examined versus miRs expression levels. A significant difference was reported between miRs expression among the enrolled individuals. Both miR-17-5p and miR-221 reported significant elevations in GBM patients who: are above 60 years old, underwent biopsy resection, have a non-frontal lesion, with tumor size above 5 cm, and with performance status equals 2 according to the Eastern Cooperative Oncology Group (ECOG) Performance Status. With regard to miR-125b, a significant difference was detected according to surgery strategy, primary lesion of the tumor, and ECOG status. MiRs levels were significantly decreased for GBM patients after treatment. Survival patterns demonstrated an increase in miR-17-5p, miR-125b, and miR-221 in GBM patients with worse progression-free survival and among those with worse overall survival. Detection of serum miR-17-5p, miR-125b, and miR-221 aids in the prediction of prognosis and response to treatment strategy for GBM patients.
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Affiliation(s)
- Menha Swellam
- Biochemistry Department, Genetic Engineering and Biotechnology Research Division, National Research Centre, Giza, Dokki, Egypt.,High Throughput Molecular and Genetic Laboratory, Centers of Excellence for Advanced Sciences, National Research Centre, Giza, Dokki, Egypt
| | - Noha M Bakr
- Biochemistry Department, Genetic Engineering and Biotechnology Research Division, National Research Centre, Giza, Dokki, Egypt.,High Throughput Molecular and Genetic Laboratory, Centers of Excellence for Advanced Sciences, National Research Centre, Giza, Dokki, Egypt
| | - Hekmat M El Magdoub
- Biochemistry Department, Faculty of Pharmacy, Translational Research Unit, Misr International University, Cairo, Egypt.
| | - Marwa S Hamza
- Clinical Pharmacy Practice Department, Faculty of Pharmacy, The British University in Egypt, El-Sherouk City, Cairo, Egypt.,The Center for Drug Research and Development (CDRD), Faculty of Pharmacy, The British University in Egypt, El-Sherouk City, Cairo, Egypt
| | - Lobna R Ezz El Arab
- Clinical Oncology Department, Faculty of Medicine, Ain Shams University, Cairo, Egypt
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26
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Sanders JW, Chen HSM, Johnson JM, Schomer DF, Jimenez JE, Ma J, Liu HL. Synthetic generation of DSC-MRI-derived relative CBV maps from DCE MRI of brain tumors. Magn Reson Med 2020; 85:469-479. [PMID: 32726488 DOI: 10.1002/mrm.28432] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Revised: 06/21/2020] [Accepted: 06/24/2020] [Indexed: 12/26/2022]
Abstract
PURPOSE Perfusion MRI with gadolinium-based contrast agents is useful for diagnosis and treatment response evaluation of brain tumors. Dynamic susceptibility contrast (DSC) MRI and dynamic contrast enhanced (DCE) MRI are two gadolinium-based contrast agent perfusion imaging techniques that provide complementary information about the tumor vasculature. However, each requires a separate administration of a gadolinium-based contrast agent. The purpose of this retrospective study was to determine the feasibility of synthesizing relative cerebral blood volume (rCBV) maps, as computed from DSC MRI, from DCE MRI of brain tumors. METHODS One hundred nine brain-tumor patients underwent both DCE and DSC MRI. Relative CBV maps were computed from the DSC MRI, and blood plasma volume fraction maps were computed from the DCE MRIs. Conditional generative adversarial networks were developed to synthesize rCBV maps from the DCE MRIs. Tumor-to-white matter ratios were calculated from real rCBV, synthetic rCBV, and plasma volume fraction maps and compared using correlation analysis. Real and synthetic rCBV in white and gray matter regions were also compared. RESULTS Pearson correlation analysis showed that both the tumor rCBV and tumor-to-white matter ratios in the synthetic and real rCBV maps were strongly correlated (ρ = 0.87, P < .05 and ρ = 0.86, P < .05, respectively). Tumor plasma volume fraction and real rCBV were not strongly correlated (ρ = 0.47). Bland-Altman analysis showed a mean difference between the synthetic and real rCBV tumor-to-white matter ratios of 0.20 with a 95% confidence interval of ±0.47. CONCLUSION Realistic rCBV maps can be synthesized from DCE MRI and contain quantitative information, enabling robust brain-tumor perfusion imaging of DSC and DCE parameters with a single gadolinium-based contrast agent administration.
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Affiliation(s)
- Jeremiah W Sanders
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.,Medical Physics Graduate Program, The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas, USA
| | - Henry Szu-Meng Chen
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jason M Johnson
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Donald F Schomer
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jorge E Jimenez
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jingfei Ma
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.,Medical Physics Graduate Program, The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas, USA
| | - Ho-Ling Liu
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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27
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Hoxworth JM, Eschbacher JM, Gonzales AC, Singleton KW, Leon GD, Smith KA, Stokes AM, Zhou Y, Mazza GL, Porter AB, Mrugala MM, Zimmerman RS, Bendok BR, Patra DP, Krishna C, Boxerman JL, Baxter LC, Swanson KR, Quarles CC, Schmainda KM, Hu LS. Performance of Standardized Relative CBV for Quantifying Regional Histologic Tumor Burden in Recurrent High-Grade Glioma: Comparison against Normalized Relative CBV Using Image-Localized Stereotactic Biopsies. AJNR Am J Neuroradiol 2020; 41:408-415. [PMID: 32165359 DOI: 10.3174/ajnr.a6486] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 12/23/2019] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Perfusion MR imaging measures of relative CBV can distinguish recurrent tumor from posttreatment radiation effects in high-grade gliomas. Currently, relative CBV measurement requires normalization based on user-defined reference tissues. A recently proposed method of relative CBV standardization eliminates the need for user input. This study compares the predictive performance of relative CBV standardization against relative CBV normalization for quantifying recurrent tumor burden in high-grade gliomas relative to posttreatment radiation effects. MATERIALS AND METHODS We recruited 38 previously treated patients with high-grade gliomas (World Health Organization grades III or IV) undergoing surgical re-resection for new contrast-enhancing lesions concerning for recurrent tumor versus posttreatment radiation effects. We recovered 112 image-localized biopsies and quantified the percentage of histologic tumor content versus posttreatment radiation effects for each sample. We measured spatially matched normalized and standardized relative CBV metrics (mean, median) and fractional tumor burden for each biopsy. We compared relative CBV performance to predict tumor content, including the Pearson correlation (r), against histologic tumor content (0%-100%) and the receiver operating characteristic area under the curve for predicting high-versus-low tumor content using binary histologic cutoffs (≥50%; ≥80% tumor). RESULTS Across relative CBV metrics, fractional tumor burden showed the highest correlations with tumor content (0%-100%) for normalized (r = 0.63, P < .001) and standardized (r = 0.66, P < .001) values. With binary cutoffs (ie, ≥50%; ≥80% tumor), predictive accuracies were similar for both standardized and normalized metrics and across relative CBV metrics. Median relative CBV achieved the highest area under the curve (normalized = 0.87, standardized = 0.86) for predicting ≥50% tumor, while fractional tumor burden achieved the highest area under the curve (normalized = 0.77, standardized = 0.80) for predicting ≥80% tumor. CONCLUSIONS Standardization of relative CBV achieves similar performance compared with normalized relative CBV and offers an important step toward workflow optimization and consensus methodology.
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Affiliation(s)
- J M Hoxworth
- From the Departments of Radiology (J.M.H., Y.Z., L.S.H.)
| | | | | | - K W Singleton
- Precision Neurotherapeutics Lab (K.W.S., G.D.L., B.R.B., K.R.S.), Mayo Clinic in Arizona, Phoenix, Arizona
| | - G D Leon
- Precision Neurotherapeutics Lab (K.W.S., G.D.L., B.R.B., K.R.S.), Mayo Clinic in Arizona, Phoenix, Arizona
| | - K A Smith
- Keller Center for Imaging Innovation (A.M.S.), Barrow Neurological Institute, Phoenix, Arizona
| | - A M Stokes
- Keller Center for Imaging Innovation (A.M.S.), Barrow Neurological Institute, Phoenix, Arizona
| | - Y Zhou
- From the Departments of Radiology (J.M.H., Y.Z., L.S.H.)
| | - G L Mazza
- Department of Health Sciences Research (G.L.M.), Division of Biomedical Statistics and Informatics, Mayo Clinic Scottsdale, Scottsdale, Arizona
| | | | | | | | - B R Bendok
- Precision Neurotherapeutics Lab (K.W.S., G.D.L., B.R.B., K.R.S.), Mayo Clinic in Arizona, Phoenix, Arizona
| | - D P Patra
- Departments of Neurosurgery (D.P.P.)
| | | | - J L Boxerman
- Department of Diagnostic Imaging (J.L.B.), Rhode Island Hospital, Providence, Rhode Island
| | - L C Baxter
- Neuropsychology (L.C.B.), Mayo Clinic Hospital, Phoenix, Arizona
| | - K R Swanson
- Precision Neurotherapeutics Lab (K.W.S., G.D.L., B.R.B., K.R.S.), Mayo Clinic in Arizona, Phoenix, Arizona
| | | | - K M Schmainda
- Department of Radiology (K.M.S.), Medical College of Wisconsin, Milwaukee, Wisconsin
| | - L S Hu
- From the Departments of Radiology (J.M.H., Y.Z., L.S.H.)
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28
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Hu LS, Hawkins-Daarud A, Wang L, Li J, Swanson KR. Imaging of intratumoral heterogeneity in high-grade glioma. Cancer Lett 2020; 477:97-106. [PMID: 32112907 DOI: 10.1016/j.canlet.2020.02.025] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 02/17/2020] [Accepted: 02/19/2020] [Indexed: 12/19/2022]
Abstract
High-grade glioma (HGG), and particularly Glioblastoma (GBM), can exhibit pronounced intratumoral heterogeneity that confounds clinical diagnosis and management. While conventional contrast-enhanced MRI lacks the capability to resolve this heterogeneity, advanced MRI techniques and PET imaging offer a spectrum of physiologic and biophysical image features to improve the specificity of imaging diagnoses. Published studies have shown how integrating these advanced techniques can help better define histologically distinct targets for surgical and radiation treatment planning, and help evaluate the regional heterogeneity of tumor recurrence and response assessment following standard adjuvant therapy. Application of texture analysis and machine learning (ML) algorithms has also enabled the emerging field of radiogenomics, which can spatially resolve the regional and genetically distinct subpopulations that coexist within a single GBM tumor. This review focuses on the latest advances in neuro-oncologic imaging and their clinical applications for the assessment of intratumoral heterogeneity.
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Affiliation(s)
- Leland S Hu
- Department of Radiology, Mayo Clinic Arizona, 5777 E Mayo Blvd, Phoenix, AZ, 85054, USA.
| | - Andrea Hawkins-Daarud
- Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic, 5777 East Mayo Blvd, Support, Services Building Suite 2-700, Phoenix, AZ, 85054, USA.
| | - Lujia Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, AZ, 85281, USA.
| | - Jing Li
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, AZ, 85281, USA.
| | - Kristin R Swanson
- Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic, 5777 East Mayo Blvd, Support, Services Building Suite 2-700, Phoenix, AZ, 85054, USA.
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29
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Vakamudi K, Posse S, Jung R, Cushnyr B, Chohan MO. Real-time presurgical resting-state fMRI in patients with brain tumors: Quality control and comparison with task-fMRI and intraoperative mapping. Hum Brain Mapp 2019; 41:797-814. [PMID: 31692177 PMCID: PMC7268088 DOI: 10.1002/hbm.24840] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Revised: 10/09/2019] [Accepted: 10/10/2019] [Indexed: 12/11/2022] Open
Abstract
Resting-state functional magnetic resonance imaging (rsfMRI) is a promising task-free functional imaging approach, which may complement or replace task-based fMRI (tfMRI) in patients who have difficulties performing required tasks. However, rsfMRI is highly sensitive to head movement and physiological noise, and validation relative to tfMRI and intraoperative electrocortical mapping is still necessary. In this study, we investigate (a) the feasibility of real-time rsfMRI for presurgical mapping of eloquent networks with monitoring of data quality in patients with brain tumors and (b) rsfMRI localization of eloquent cortex compared with tfMRI and intraoperative electrocortical stimulation (ECS) in retrospective analysis. Five brain tumor patients were studied with rsfMRI and tfMRI on a clinical 3T scanner using MultiBand(8)-echo planar imaging (EPI) with repetition time: 400 ms. Moving-averaged sliding-window correlation analysis with regression of motion parameters and signals from white matter and cerebrospinal fluid was used to map sensorimotor and language resting-state networks. Data quality monitoring enabled rapid optimization of scan protocols, early identification of task noncompliance, and head movement-related false-positive connectivity to determine scan continuation or repetition. Sensorimotor and language resting-state networks were identifiable within 1 min of scan time. The Euclidean distance between ECS and rsfMRI connectivity and task-activation in motor cortex, Broca's, and Wernicke's areas was 5-10 mm, with the exception of discordant rsfMRI and ECS localization of Wernicke's area in one patient due to possible cortical reorganization and/or altered neurovascular coupling. This study demonstrates the potential of real-time high-speed rsfMRI for presurgical mapping of eloquent cortex with real-time data quality control, and clinically acceptable concordance of rsfMRI with tfMRI and ECS localization.
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Affiliation(s)
- Kishore Vakamudi
- Department of Neurology, University of New Mexico, Albuquerque, New Mexico
| | - Stefan Posse
- Department of Neurology, University of New Mexico, Albuquerque, New Mexico.,Department of Physics and Astronomy, University of New Mexico, Albuquerque, New Mexico
| | - Rex Jung
- Department of Neurosurgery, University of New Mexico, Albuquerque, New Mexico
| | - Brad Cushnyr
- Department of Radiology, University of New Mexico, Albuquerque, New Mexico
| | - Muhammad O Chohan
- Department of Neurosurgery, University of New Mexico, Albuquerque, New Mexico
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30
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Sanders JW, Fletcher JR, Frank SJ, Liu HL, Johnson JM, Zhou Z, Chen HSM, Venkatesan AM, Kudchadker RJ, Pagel MD, Ma J. Deep learning application engine (DLAE): Development and integration of deep learning algorithms in medical imaging. SOFTWAREX 2019; 10:100347. [PMID: 34113706 PMCID: PMC8188855 DOI: 10.1016/j.softx.2019.100347] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Herein we introduce a deep learning (DL) application engine (DLAE) system concept, present potential uses of it, and describe pathways for its integration in clinical workflows. An open-source software application was developed to provide a code-free approach to DL for medical imaging applications. DLAE supports several DL techniques used in medical imaging, including convolutional neural networks, fully convolutional networks, generative adversarial networks, and bounding box detectors. Several example applications using clinical images were developed and tested to demonstrate the capabilities of DLAE. Additionally, a model deployment example was demonstrated in which DLAE was used to integrate two trained models into a commercial clinical software package.
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Affiliation(s)
- Jeremiah W. Sanders
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1472, Houston, TX 77030, United States of America
- Medical Physics Graduate Program, MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, 1515 Holcombe Blvd., Unit 1472, TX 77030, United States of America
| | - Justin R. Fletcher
- Odyssey Systems Consulting, LLC, 550 Lipoa Parkway, Kihei, Maui, HI, United States of America
| | - Steven J. Frank
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1422, Houston, TX 77030, United States of America
| | - Ho-Ling Liu
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1472, Houston, TX 77030, United States of America
- Medical Physics Graduate Program, MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, 1515 Holcombe Blvd., Unit 1472, TX 77030, United States of America
| | - Jason M. Johnson
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1473, Houston, TX 77030, United States of America
| | - Zijian Zhou
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1472, Houston, TX 77030, United States of America
| | - Henry Szu-Meng Chen
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1472, Houston, TX 77030, United States of America
| | - Aradhana M. Venkatesan
- Medical Physics Graduate Program, MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, 1515 Holcombe Blvd., Unit 1472, TX 77030, United States of America
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1473, Houston, TX 77030, United States of America
| | - Rajat J. Kudchadker
- Medical Physics Graduate Program, MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, 1515 Holcombe Blvd., Unit 1472, TX 77030, United States of America
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1420, Houston, TX 77030, United States of America
| | - Mark D. Pagel
- Medical Physics Graduate Program, MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, 1515 Holcombe Blvd., Unit 1472, TX 77030, United States of America
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1907, Houston, TX 77030, United States of America
| | - Jingfei Ma
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1472, Houston, TX 77030, United States of America
- Medical Physics Graduate Program, MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, 1515 Holcombe Blvd., Unit 1472, TX 77030, United States of America
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Li C, Yan JL, Torheim T, McLean MA, Boonzaier NR, Zou J, Huang Y, Yuan J, van Dijken BRJ, Matys T, Markowetz F, Price SJ. Low perfusion compartments in glioblastoma quantified by advanced magnetic resonance imaging and correlated with patient survival. Radiother Oncol 2019; 134:17-24. [PMID: 31005212 PMCID: PMC6486398 DOI: 10.1016/j.radonc.2019.01.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Revised: 12/10/2018] [Accepted: 01/09/2019] [Indexed: 12/02/2022]
Abstract
BACKGROUND AND PURPOSE Glioblastoma exhibits profound intratumoral heterogeneity in perfusion. Particularly, low perfusion may induce treatment resistance. Thus, imaging approaches that define low perfusion compartments are crucial for clinical management. MATERIALS AND METHODS A total of 112 newly diagnosed glioblastoma patients were prospectively recruited for maximal safe resection. The apparent diffusion coefficient (ADC) and relative cerebral blood volume (rCBV) were calculated from diffusion and perfusion imaging, respectively. Based on the overlapping regions of lowest rCBV quartile (rCBVL) with the lowest ADC quartile (ADCL) and highest ADC quartile (ADCH) in each tumor, two low perfusion compartments (ADCH-rCBVL and ADCL-rCBVL) were identified for volumetric analysis. Lactate and macromolecule/lipid levels were determined from multivoxel MR spectroscopic imaging. Progression-free survival (PFS) and overall survival (OS) were analyzed using Kaplan-Meier's and multivariate Cox regression analyses, to evaluate the effects of compartment volume and lactate level on survival. RESULTS Two compartments displayed higher lactate and macromolecule/lipid levels compared to contralateral normal-appearing white matter (each P < 0.001). The proportion of the ADCL-rCBVL compartment in the contrast-enhancing tumor was associated with a larger infiltration on FLAIR (P < 0.001, rho = 0.42). The minimally invasive phenotype displayed a lower proportion of the ADCL-rCBVL compartment than the localized (P = 0.031) and diffuse phenotypes (not significant). Multivariate Cox regression showed higher lactate level in the ADCL-rCBVL compartment was associated with worsened survival (PFS: HR 2.995, P = 0.047; OS: HR 4.974, P = 0.005). CONCLUSIONS Our results suggest that the ADCL-rCBVL compartment may potentially indicate a clinically measurable resistant compartment.
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Affiliation(s)
- Chao Li
- Cambridge Brain Tumour Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neuroscience, University of Cambridge, UK; Department of Neurosurgery, Shanghai General Hospital (originally named "Shanghai First People's Hospital"), Shanghai Jiao Tong University School of Medicine, China; EPSRC Centre for Mathematical Imaging in Healthcare, University of Cambridge, UK.
| | - Jiun-Lin Yan
- Cambridge Brain Tumour Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neuroscience, University of Cambridge, UK; Department of Neurosurgery, Chang Gung Memorial Hospital, Keelung, Taiwan; Chang Gung University College of Medicine, Taoyuan, Taiwan
| | - Turid Torheim
- Cancer Research UK Cambridge Institute, University of Cambridge, UK; CRUK & EPSRC Cancer Imaging Centre in Cambridge and Manchester, Cambridge, UK
| | - Mary A McLean
- Cancer Research UK Cambridge Institute, University of Cambridge, UK
| | - Natalie R Boonzaier
- Developmental Imaging and Biophysics Section, Great Ormond Street Institute of Child Health, University College London, UK
| | - Jingjing Zou
- Statistical Laboratory, Centre for Mathematical Sciences, University of Cambridge, UK
| | - Yuan Huang
- EPSRC Centre for Mathematical Imaging in Healthcare, University of Cambridge, UK; Department of Radiology, University of Cambridge, UK
| | - Jianmin Yuan
- Department of Radiology, University of Cambridge, UK
| | - Bart R J van Dijken
- Department of Radiology, University Medical Center Groningen, University of Groningen, the Netherlands
| | - Tomasz Matys
- Statistical Laboratory, Centre for Mathematical Sciences, University of Cambridge, UK; Cancer Trials Unit Department of Oncology, Addenbrooke's Hospital, Cambridge, UK
| | - Florian Markowetz
- Cancer Research UK Cambridge Institute, University of Cambridge, UK; CRUK & EPSRC Cancer Imaging Centre in Cambridge and Manchester, Cambridge, UK
| | - Stephen J Price
- Cambridge Brain Tumour Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neuroscience, University of Cambridge, UK; Wolfson Brain Imaging Centre, Department of Clinical Neuroscience, University of Cambridge, UK
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Affiliation(s)
- Ronald L. Wolf
- From the Department of Radiology, Neuroradiology Section, Perelman School of Medicine, University of Pennsylvania Health System, Dulles 219, 3400 Spruce St, Philadelphia, PA 19104
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Warntjes M, Blystad I, Tisell A, Larsson EM. Synthesizing a Contrast-Enhancement Map in Patients with High-Grade Gliomas Based on a Postcontrast MR Imaging Quantification Only. AJNR Am J Neuroradiol 2018; 39:2194-2199. [PMID: 30409854 DOI: 10.3174/ajnr.a5870] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2018] [Accepted: 09/24/2018] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Administration of a gadolinium-based contrast agent is an important diagnostic biomarker for blood-brain barrier damage. In clinical use, detection is based on subjective comparison of native and postgadolinium-based contrast agent T1-weighted images. Quantitative MR imaging studies have suggested a relation between the longitudinal relaxation rate and proton-density in the brain parenchyma, which is disturbed by gadolinium-based contrast agents. This discrepancy can be used to synthesize a contrast-enhancement map based solely on the postgadolinium-based contrast agent acquisition. The aim of this study was to compare synthetic enhancement maps with subtraction maps of native and postgadolinium-based contrast agent images. MATERIALS AND METHODS For 14 patients with high-grade gliomas, quantitative MR imaging was performed before and after gadolinium-based contrast agent administration. The quantification sequence was multidynamic and multiecho, with a scan time of 6 minutes. The 2 image stacks were coregistered using in-plane transformation. The longitudinal relaxation maps were subtracted and correlated with the synthetic longitudinal relaxation enhancement maps on the basis of the postgadolinium-based contrast agent images only. ROIs were drawn for tumor delineation. RESULTS Linear regression of the subtraction and synthetic longitudinal relaxation enhancement maps showed a slope of 1.02 ± 0.19 and an intercept of 0.05 ± 0.12. The Pearson correlation coefficient was 0.861 ± 0.059, and the coefficient of variation was 0.18 ± 0.04. On average, a volume of 1.71 ± 1.28 mL of low-intensity enhancement was detected in the synthetic enhancement maps outside the borders of the drawn ROI. CONCLUSIONS The study shows that there was a good correlation between subtraction longitudinal relaxation enhancement maps and synthetic longitudinal relaxation enhancement maps in patients with high-grade gliomas. The method may improve the sensitivity and objectivity for the detection of gadolinium-based contrast agent enhancement.
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Affiliation(s)
- M Warntjes
- From the Centre for Medical Image Science and Visualization (M.W., I.B.., A.T.) .,Division of Cardiovascular Medicine (M.W.).,SyntheticMR AB (M.W.), Linköping, Sweden
| | - I Blystad
- From the Centre for Medical Image Science and Visualization (M.W., I.B.., A.T.).,Departments of Radiology (I.B.)
| | - A Tisell
- From the Centre for Medical Image Science and Visualization (M.W., I.B.., A.T.).,Radiation Physics (A.T.), Department of Medical and Health Sciences, Linköping University, Linköping, Sweden
| | - E-M Larsson
- Department of Surgical Sciences and Radiology (E.-M.L.), Uppsala University, Uppsala, Sweden
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34
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Li Q, Wang D, Zhu X, Shen K, Xu F, Chen Y. Combination of renal apparent diffusion coefficient and renal parenchymal volume for better assessment of split renal function in chronic kidney disease. Eur J Radiol 2018; 108:194-200. [DOI: 10.1016/j.ejrad.2018.10.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2018] [Revised: 09/30/2018] [Accepted: 10/01/2018] [Indexed: 12/18/2022]
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35
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Jiang S, Eberhart CG, Lim M, Heo HY, Zhang Y, Blair L, Wen Z, Holdhoff M, Lin D, Huang P, Qin H, Quinones-Hinojosa A, Weingart JD, Barker PB, Pomper MG, Laterra J, van Zijl PCM, Blakeley JO, Zhou J. Identifying Recurrent Malignant Glioma after Treatment Using Amide Proton Transfer-Weighted MR Imaging: A Validation Study with Image-Guided Stereotactic Biopsy. Clin Cancer Res 2018; 25:552-561. [PMID: 30366937 DOI: 10.1158/1078-0432.ccr-18-1233] [Citation(s) in RCA: 86] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2018] [Revised: 09/06/2018] [Accepted: 10/22/2018] [Indexed: 12/13/2022]
Abstract
PURPOSE To quantify the accuracy of amide proton transfer-weighted (APTw) MRI for identifying active glioma after treatment via radiographically guided stereotactic tissue validation.Experimental Design: Twenty-one patients who were referred for surgery for MRI features concerning for tumor progression versus treatment effect underwent preoperative APTw imaging. Stereotactic biopsy samples were taken from regions of interest with varying APTw signal intensities. The relationship between final clinical pathology and the histopathology of each of the 64 specimens was analyzed relative to APTw results. Analysis of confirmed recurrent tumor or treatment effect tissue was used to perform ROC analysis. RESULTS Eighteen of 21 patients had recurrent tumor, and 3 had treatment effect on clinical pathology. In 12 patients, there were multiple histopathologic assignments confirmed within the same tumor. Of the 64 total specimens, 20 specimens were active glioma, 27 mixed active and quiescent glioma, and 17 quiescent/no identifiable tumor. APTw signal intensity and histopathologic assignment, cellularity, and proliferation index had significant positive correlations (R = 0.651, 0.580, and 0.458, respectively; all P < 0.001). ROC analysis with a 1.79% APTw intensity cutoff differentiated active from nonactive tumor (AUC of 0.881) with 85.1% sensitivity and 94.1% specificity. Analysis of clinical pathology showed the mean APTw intensity for each patient had 94.4% sensitivity and 100% positive predictive value for identifying recurrent glioma at this cutoff. CONCLUSIONS APTw imaging hyperintensity may be a marker of active malignant glioma. It is able to distinguish between regions of heterogeneous abnormality on anatomic brain MRI with high sensitivity and specificity.
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Affiliation(s)
- Shanshan Jiang
- Department of Radiology, Johns Hopkins University, Baltimore, Maryland
| | | | - Michael Lim
- Department of Neurosurgery, Johns Hopkins University, Baltimore, Maryland
| | - Hye-Young Heo
- Department of Radiology, Johns Hopkins University, Baltimore, Maryland
| | - Yi Zhang
- Department of Radiology, Johns Hopkins University, Baltimore, Maryland
| | - Lindsay Blair
- Department of Neurology, Johns Hopkins University, Baltimore, Maryland
| | - Zhibo Wen
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Matthias Holdhoff
- Department of Oncology, Johns Hopkins University, Baltimore, Maryland
| | - Doris Lin
- Department of Radiology, Johns Hopkins University, Baltimore, Maryland
| | - Peng Huang
- Department of Oncology, Johns Hopkins University, Baltimore, Maryland
| | - Huamin Qin
- Department of Pathology, Johns Hopkins University, Baltimore, Maryland
| | | | - Jon D Weingart
- Department of Neurosurgery, Johns Hopkins University, Baltimore, Maryland
| | - Peter B Barker
- Department of Radiology, Johns Hopkins University, Baltimore, Maryland
| | - Martin G Pomper
- Department of Radiology, Johns Hopkins University, Baltimore, Maryland
| | - John Laterra
- Department of Neurology, Johns Hopkins University, Baltimore, Maryland.,Department of Oncology, Johns Hopkins University, Baltimore, Maryland.,Department of Neuroscience, Johns Hopkins University, Baltimore, Maryland.,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland
| | - Peter C M van Zijl
- Department of Radiology, Johns Hopkins University, Baltimore, Maryland.,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland
| | | | - Jinyuan Zhou
- Department of Radiology, Johns Hopkins University, Baltimore, Maryland. .,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland
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Semmineh NB, Bell LC, Stokes AM, Hu LS, Boxerman JL, Quarles CC. Optimization of Acquisition and Analysis Methods for Clinical Dynamic Susceptibility Contrast MRI Using a Population-Based Digital Reference Object. AJNR Am J Neuroradiol 2018; 39:1981-1988. [PMID: 30309842 PMCID: PMC6239921 DOI: 10.3174/ajnr.a5827] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2018] [Accepted: 06/08/2018] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE The accuracy of DSC-MR imaging CBV maps in glioblastoma depends on acquisition and analysis protocols. Multisite protocol heterogeneity has challenged standardization initiatives due to the difficulties of in vivo validation. This study sought to compare the accuracy of routinely used protocols using a digital reference object. MATERIALS AND METHODS The digital reference object consisted of approximately 10,000 simulated voxels recapitulating typical signal heterogeneity encountered in vivo. The influence of acquisition and postprocessing methods on CBV reliability was evaluated across 6912 parameter combinations, including contrast agent dosing schemes, pulse sequence parameters, field strengths, and postprocessing methods. Accuracy and precision were assessed using the concordance correlation coefficient and coefficient of variation. RESULTS Across all parameter space, the optimal protocol included full-dose contrast agent preload and bolus, intermediate (60°) flip angle, 30-ms TE, and postprocessing with a leakage-correction algorithm (concordance correlation coefficient = 0.97, coefficient of variation = 6.6%). Protocols with no preload or fractional dose preload and bolus using these acquisition parameters were generally less robust. However, a protocol with no preload, full-dose bolus, and low (30°) flip angle performed very well (concordance correlation coefficient = 0.93, coefficient of variation = 8.7% at 1.5T and concordance correlation coefficient = 0.92, coefficient of variation = 8.2% at 3T). CONCLUSIONS Schemes with full-dose preload and bolus maximize CBV accuracy and reduce variability, which could enable smaller sample sizes and more reliable detection of CBV changes in clinical trials. When a lower total contrast agent dose is desired, use of a low flip angle, no preload, and full-dose bolus protocol may provide an attractive alternative.
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Affiliation(s)
- N B Semmineh
- From the Department of Imaging Research (N.B.S., L.C.B., A.M.S., C.C.Q.), Barrow Neurological Institute, Phoenix, Arizona
| | - L C Bell
- From the Department of Imaging Research (N.B.S., L.C.B., A.M.S., C.C.Q.), Barrow Neurological Institute, Phoenix, Arizona
| | - A M Stokes
- From the Department of Imaging Research (N.B.S., L.C.B., A.M.S., C.C.Q.), Barrow Neurological Institute, Phoenix, Arizona
| | - L S Hu
- Department of Radiology (L.S.H.), Mayo Clinic Arizona, Phoenix, Arizona
| | - J L Boxerman
- Department of Diagnostic Imaging (J.L.B.), Rhode Island Hospital, Providence, Rhode Island
| | - C C Quarles
- From the Department of Imaging Research (N.B.S., L.C.B., A.M.S., C.C.Q.), Barrow Neurological Institute, Phoenix, Arizona
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37
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Eijgelaar RS, Bruynzeel AME, Lagerwaard FJ, Müller DMJ, Teunissen FR, Barkhof F, van Herk M, De Witt Hamer PC, Witte MG. Earliest radiological progression in glioblastoma by multidisciplinary consensus review. J Neurooncol 2018; 139:591-598. [PMID: 29777418 PMCID: PMC6132963 DOI: 10.1007/s11060-018-2896-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2018] [Accepted: 05/02/2018] [Indexed: 01/13/2023]
Abstract
BACKGROUND Detection of glioblastoma progression is important for clinical decision-making on cessation or initiation of therapy, for enrollment in clinical trials, and for response measurement in time and location. The RANO-criteria are considered standard for the timing of progression. To evaluate local treatment, we aim to find the most accurate progression location. We determined the differences in progression free survival (PFS) and in tumor volumes at progression (Vprog) by three definitions of progression. METHODS In a consecutive cohort of 73 patients with newly-diagnosed glioblastoma between 1/1/2012 and 31/12/2013, progression was established according to three definitions. We determined (1) earliest radiological progression (ERP) by retrospective multidisciplinary consensus review using all available imaging and follow-up, (2) clinical practice progression (CPP) from multidisciplinary tumor board conclusions, and (3) progression by the RANO-criteria. RESULTS ERP was established in 63 (86%), CPP in 64 (88%), RANO progression in 42 (58%). Of the 63 patients who had died, 37 (59%) did with prior RANO-progression, compared to 57 (90%) for both ERP and CPP. The median overall survival was 15.3 months. The median PFS was 8.8 months for ERP, 9.5 months for CPP, and 11.8 months for RANO. The PFS by ERP was shorter than CPP (HR 0.57, 95% CI 0.38-0.84, p = 0.004) and RANO-progression (HR 0.29, 95% CI 0.19-0.43, p < 0.001). The Vprog were significantly smaller for ERP (median 8.8 mL), than for CPP (17 mL) and RANO (22 mL). CONCLUSION PFS and Vprog vary considerably between progression definitions. Earliest radiological progression by retrospective consensus review should be considered to accurately localize progression and to address confounding of lead time bias in clinical trial enrollment.
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Affiliation(s)
- Roelant S Eijgelaar
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Anna M E Bruynzeel
- Department of Radiation Oncology, VU University Medical Center, Amsterdam, The Netherlands
| | - Frank J Lagerwaard
- Department of Radiation Oncology, VU University Medical Center, Amsterdam, The Netherlands
| | - Domenique M J Müller
- Neurosurgical Center Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Freek R Teunissen
- Neurosurgical Center Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
- Institutes of Neurology & Healthcare Engineering, University College London, London, UK
| | - Marcel van Herk
- Division of Cancer Sciences, Faculty of Biology, Medicine & Health, University of Manchester and Christie NHS Trust, Manchester, UK
| | - Philip C De Witt Hamer
- Neurosurgical Center Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Marnix G Witte
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
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Ly KI, Gerstner ER. The Role of Advanced Brain Tumor Imaging in the Care of Patients with Central Nervous System Malignancies. Curr Treat Options Oncol 2018; 19:40. [PMID: 29931476 DOI: 10.1007/s11864-018-0558-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
OPINION STATEMENT T1-weighted post-contrast and T2-weighted fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) constitute the gold standard for diagnosis and response assessment in neuro-oncologic patients but are limited in their ability to accurately reflect tumor biology and metabolism, particularly over the course of a patient's treatment. Advanced MR imaging methods are sensitized to different biophysical processes in tissue, including blood perfusion, tumor metabolism, and chemical composition of tissue, and provide more specific information on tissue physiology than standard MRI. This review provides an overview of the most common and emerging advanced imaging modalities in the field of brain tumor imaging and their applications in the care of neuro-oncologic patients.
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Affiliation(s)
- K Ina Ly
- Stephen E. and Catherine Pappas Center for Neuro-Oncology, Massachusetts General Hospital, 55 Fruit Street, Yawkey 9E, Boston, MA, 02114, USA
| | - Elizabeth R Gerstner
- Stephen E. and Catherine Pappas Center for Neuro-Oncology, Massachusetts General Hospital, 55 Fruit Street, Yawkey 9E, Boston, MA, 02114, USA.
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39
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Thust SC, van den Bent MJ, Smits M. Pseudoprogression of brain tumors. J Magn Reson Imaging 2018; 48:571-589. [PMID: 29734497 PMCID: PMC6175399 DOI: 10.1002/jmri.26171] [Citation(s) in RCA: 178] [Impact Index Per Article: 29.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2018] [Accepted: 04/07/2018] [Indexed: 12/11/2022] Open
Abstract
This review describes the definition, incidence, clinical implications, and magnetic resonance imaging (MRI) findings of pseudoprogression of brain tumors, in particular, but not limited to, high-grade glioma. Pseudoprogression is an important clinical problem after brain tumor treatment, interfering not only with day-to-day patient care but also the execution and interpretation of clinical trials. Radiologically, pseudoprogression is defined as a new or enlarging area(s) of contrast agent enhancement, in the absence of true tumor growth, which subsides or stabilizes without a change in therapy. The clinical definitions of pseudoprogression have been quite variable, which may explain some of the differences in reported incidences, which range from 9-30%. Conventional structural MRI is insufficient for distinguishing pseudoprogression from true progressive disease, and advanced imaging is needed to obtain higher levels of diagnostic certainty. Perfusion MRI is the most widely used imaging technique to diagnose pseudoprogression and has high reported diagnostic accuracy. Diagnostic performance of MR spectroscopy (MRS) appears to be somewhat higher, but MRS is less suitable for the routine and universal application in brain tumor follow-up. The combination of MRS and diffusion-weighted imaging and/or perfusion MRI seems to be particularly powerful, with diagnostic accuracy reaching up to or even greater than 90%. While diagnostic performance can be high with appropriate implementation and interpretation, even a combination of techniques, however, does not provide 100% accuracy. It should also be noted that most studies to date are small, heterogeneous, and retrospective in nature. Future improvements in diagnostic accuracy can be expected with harmonization of acquisition and postprocessing, quantitative MRI and computer-aided diagnostic technology, and meticulous evaluation with clinical and pathological data. LEVEL OF EVIDENCE 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.
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Affiliation(s)
- Stefanie C. Thust
- Lysholm Neuroradiology DepartmentNational Hospital for Neurology and NeurosurgeryLondonUK
- Department of Brain Rehabilitation and RepairUCL Institute of NeurologyLondonUK
- Imaging DepartmentUniversity College London HospitalLondonUK
| | - Martin J. van den Bent
- Department of NeurologyThe Brain Tumor Centre at Erasmus MC Cancer InstituteRotterdamThe Netherlands
| | - Marion Smits
- Department of Radiology and Nuclear Medicine, Erasmus MCUniversity Medical Centre RotterdamRotterdamThe Netherlands
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40
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Dolera M, Malfassi L, Carrara N, Finesso S, Marcarini S, Mazza G, Pavesi S, Sala M, Urso G. Volumetric Modulated Arc (Radio) Therapy in Pets Treatment: The "La Cittadina Fondazione" Experience. Cancers (Basel) 2018; 10:E30. [PMID: 29364837 PMCID: PMC5836062 DOI: 10.3390/cancers10020030] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Revised: 12/29/2017] [Accepted: 01/16/2018] [Indexed: 12/31/2022] Open
Abstract
Volumetric Modulated Arc Therapy (VMAT) is a modern technique, widely used in human radiotherapy, which allows a high dose to be delivered to tumor volumes and low doses to the surrounding organs at risk (OAR). Veterinary clinics takes advantage of this feature due to the small target volumes and distances between the target and the OAR. Sparing the OAR permits dose escalation, and hypofractionation regimens reduce the number of treatment sessions with a simpler manageability in the veterinary field. Multimodal volumes definition is mandatory for the small volumes involved and a positioning device precisely reproducible with a setup confirmation is needed before each session for avoiding missing the target. Additionally, the elaborate treatment plan must pursue hard constraints and objectives, and its feasibility must be evaluated with a per patient quality control. The aim of this work is to report results with regard to brain meningiomas and gliomas, trigeminal nerve tumors, brachial plexus tumors, adrenal tumors with vascular invasion and rabbit thymomas, in comparison with literature to determine if VMAT is a safe and viable alternative to surgery or chemotherapy alone, or as an adjuvant therapy in pets.
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Affiliation(s)
- Mario Dolera
- La Cittadina Fondazione Studi e Ricerche Veterinarie, 26014 Romanengo, Italy.
| | - Luca Malfassi
- La Cittadina Fondazione Studi e Ricerche Veterinarie, 26014 Romanengo, Italy.
| | - Nancy Carrara
- La Cittadina Fondazione Studi e Ricerche Veterinarie, 26014 Romanengo, Italy.
| | - Sara Finesso
- La Cittadina Fondazione Studi e Ricerche Veterinarie, 26014 Romanengo, Italy.
| | - Silvia Marcarini
- La Cittadina Fondazione Studi e Ricerche Veterinarie, 26014 Romanengo, Italy.
| | - Giovanni Mazza
- La Cittadina Fondazione Studi e Ricerche Veterinarie, 26014 Romanengo, Italy.
| | - Simone Pavesi
- La Cittadina Fondazione Studi e Ricerche Veterinarie, 26014 Romanengo, Italy.
| | - Massimo Sala
- La Cittadina Fondazione Studi e Ricerche Veterinarie, 26014 Romanengo, Italy.
| | - Gaetano Urso
- Azienda Socio Sanitaria Territoriale della provincia di Lodi, 26841 Casalpusterlengo, Italy.
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Dongas J, Asahina AT, Bacchi S, Patel S. Magnetic Resonance Perfusion Imaging in the Diagnosis of High-Grade Glioma Progression and Treatment-Related Changes: A Systematic Review. ACTA ACUST UNITED AC 2018. [DOI: 10.4236/ojmn.2018.83024] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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42
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Razek AAKA, El-Serougy L, Abdelsalam M, Gaballa G, Talaat M. Differentiation of residual/recurrent gliomas from postradiation necrosis with arterial spin labeling and diffusion tensor magnetic resonance imaging-derived metrics. Neuroradiology 2017; 60:169-177. [PMID: 29218370 DOI: 10.1007/s00234-017-1955-3] [Citation(s) in RCA: 84] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2017] [Accepted: 11/27/2017] [Indexed: 12/25/2022]
Abstract
PURPOSE The aim of this study is to differentiate recurrent/residual gliomas from postradiation changes using arterial spin labeling (ASL) perfusion and diffusion tensor imaging (DTI)-derived metrics. METHODS Prospective study was conducted upon 42 patients with high-grade gliomas after radiotherapy only or prior to other therapies that underwent routine MR imaging, ASL, and DTI. The tumor blood flow (TBF), fractional anisotropy (FA), and mean diffusivity (MD) of the enhanced lesion and related edema were calculated. The lesion was categorized as recurrence/residual or postradiation changes. RESULTS There was significant differences between residual/recurrent gliomas and postradiation changes of TBF (P = 0.001), FA (P = 0.001 and 0.04), and MD (P = 0.001) of enhanced lesion and related edema respectively. The area under the curve (AUC) of TBF of enhanced lesion and related edema used to differentiate residual/recurrent gliomas from postradiation changes were 0.95 and 0.93 and of MD were 0.95 and 0.81 and of FA were 0.81 and 0.695, respectively. Combined ASL and DTI metrics of the enhanced lesion revealed AUC of 0.98, accuracy of 95%, sensitivity of 93.8%, specificity of 95.8%, positive predictive value (PPV) of 93.8%, and negative predictive value (NPV) of 95.8%. Combined metrics of ASL and DTI of related edema revealed AUC of 0.97, accuracy of 92.5%, sensitivity of 93.8%, specificity of 91.7%, PPV of 88.2%, and NPV of 95.7. CONCLUSION Combined ASL and DTI metrics of enhanced lesion and related edema are valuable noninvasive tools in differentiating residual/recurrent gliomas from postradiation changes.
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Affiliation(s)
| | - Lamiaa El-Serougy
- Department of Diagnostic Radiology, Mansoura Faculty of Medicine, Mansoura, 13551, Egypt
| | | | - Gada Gaballa
- Department of Diagnostic Radiology, Mansoura Faculty of Medicine, Mansoura, 13551, Egypt
| | - Mona Talaat
- Department of Diagnostic Radiology, Mansoura Faculty of Medicine, Mansoura, 13551, Egypt
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43
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Belliveau JG, Bauman GS, Macdonald D, Macdonald M, Klassen LM, Menon RS. Apparent transverse relaxation (R2∗) on MRI as a method to differentiate treatment effect (pseudoprogression) versus progressive disease in chemoradiation for malignant glioma. J Med Imaging Radiat Oncol 2017; 62:224-231. [PMID: 29193849 DOI: 10.1111/1754-9485.12694] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2017] [Accepted: 10/23/2017] [Indexed: 12/12/2022]
Abstract
INTRODUCTION Pseudoprogression (psPD) is a transient post-treatment imaging change that is commonly seen when treating glioma with chemotherapy and radiation. The use of apparent transverse relaxation rate (R2∗), which is calculated from a contrast-free multi-echo gradient echo Magnetic Resonance Imaging (MRI) sequence, may allow for quantitative identification of patients with suspected psPD. METHODS We acquired a multi-echo gradient echo sequence using a 3T-Siemens Prisma MRI. The signal decay through the echoes was fitted to provide the R2∗ coefficient. We segmented the T1 -gadolinium enhancing the image to provide a contrast enhancing lesion (CEL) and the FLAIR hyperintensity to provide a non-enhancing lesion (NEL). These regions of interest were applied to the multi-echo gradient echo to acquire a mean R2∗ within the CEL and NEL. We additionally acquired ADC data to attempt to corroborate our findings. RESULTS We found that patients who later exhibited PD exhibited a higher R2∗ within the CEL as well as a higher ratio of CEL to NEL. Our data correctly distinguished pseudoprogression from treatment effect in 9/9 patients, while ADC corrected identified 7/9 patients using an absolute ADC of 1200 × 10-6 mm2 /s. CONCLUSIONS Our method seems promising for the accurate identification of psPD, and the technique is amenable to evaluation in larger, multi-centre patient cohorts.
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Affiliation(s)
- Jean-Guy Belliveau
- Department of Medical Biophysics, University of Western Ontario, London, Ontario, Canada.,Center for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, Ontario, Canada
| | - Glenn S Bauman
- Department of Medical Biophysics, University of Western Ontario, London, Ontario, Canada.,Department of Oncology, University of Western Ontario, London, Ontario, Canada.,London Regional Cancer Program, London, Ontario, Canada
| | - David Macdonald
- Department of Oncology, University of Western Ontario, London, Ontario, Canada.,London Regional Cancer Program, London, Ontario, Canada
| | - Maria Macdonald
- Department of Oncology, University of Western Ontario, London, Ontario, Canada.,London Regional Cancer Program, London, Ontario, Canada
| | - L Martyn Klassen
- Center for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, Ontario, Canada
| | - Ravi S Menon
- Department of Medical Biophysics, University of Western Ontario, London, Ontario, Canada.,Center for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, Ontario, Canada
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44
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Liu X, Mangla R, Tian W, Qiu X, Li D, Walter KA, Ekholm S, Johnson MD. The preliminary radiogenomics association between MR perfusion imaging parameters and genomic biomarkers, and their predictive performance of overall survival in patients with glioblastoma. J Neurooncol 2017; 135:553-560. [PMID: 28889246 DOI: 10.1007/s11060-017-2602-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2017] [Accepted: 08/20/2017] [Indexed: 12/20/2022]
Abstract
The radiogenomics association of neovascularization is important for overall survival (OS) in glioblastoma patients and remains unclear. The purpose of this study is to assess the association between MR perfusion imaging derived parameters and genomic biomarkers of glioblastoma, and to evaluate their prognostic value. This retrospective study enrolled 41 patients with newly diagnosed glioblastoma. The mean and maximal relative cerebral blood volume (rCBV) ratio (rCBVmean and rCBVmax), derived from MR perfusion weighted imaging, of the enhancing tumor, as well as maximal rCBV ratio of peri-enhancing tumor area (rCBVperi-tumor) were measured. The ki-67 labeling index, mammalian target of rapamycin (mTOR) activation, epidermal growth factor receptor (EGFR) amplification, isocitrate dehydrogenase (IDH) mutation and TP53 were assessed. There was a significant correlation between rCBVmax and mTOR based on Pearson's correlations with Benjamini-Hochberg adjustment for controlling false discovery rate, p = 0.047. The rCBVperi-tumor showed significant correlation with mTOR (p = 0.0183) after adjustment of gender and EGFR status. The mean rCBVperi-tumor value of the patients with OS shorter than 14 months was significantly higher than patients with OS longer than 14 months, p = 0.002. The rCBVperi-tumor and age were the two strongest predictors of OS (hazard ratio = 1.29 and 1.063 respectively) by Cox regression analysis. This study showed that hemodynamic abnormalities of glioblastoma were associated with genomics activation status of mTOR-EGFR pathway, however, the radiogenomics associations are different in enhancing and peri-enhancing area of glioblastoma. The rCBVperi-tumor has better prognostic value than genomic biomarkers alone.
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Affiliation(s)
- Xiang Liu
- Department of Imaging Sciences, University of Rochester Medical Center, 601 Elmwood Avenue, PO Box 648, Rochester, NY, 14642-8638, USA.
| | - Rajiv Mangla
- Department of Imaging Sciences, University of Rochester Medical Center, 601 Elmwood Avenue, PO Box 648, Rochester, NY, 14642-8638, USA
| | - Wei Tian
- Department of Imaging Sciences, University of Rochester Medical Center, 601 Elmwood Avenue, PO Box 648, Rochester, NY, 14642-8638, USA
| | - Xing Qiu
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA
| | - Dongmei Li
- Clinical and Translational Research and Public Health Sciences, University of Rochester Medical Center, Rochester, NY, USA
| | - Kevin A Walter
- Department of Neurosurgey, University of Rochester Medical Center, Rochester, NY, USA
| | - Sven Ekholm
- Department of Imaging Sciences, University of Rochester Medical Center, 601 Elmwood Avenue, PO Box 648, Rochester, NY, 14642-8638, USA
| | - Mahlon D Johnson
- Department of Pathology, University of Rochester Medical Center, Rochester, NY, USA
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45
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Villanueva-Meyer JE, Mabray MC, Cha S. Current Clinical Brain Tumor Imaging. Neurosurgery 2017; 81:397-415. [PMID: 28486641 PMCID: PMC5581219 DOI: 10.1093/neuros/nyx103] [Citation(s) in RCA: 197] [Impact Index Per Article: 28.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2016] [Accepted: 02/23/2017] [Indexed: 01/12/2023] Open
Abstract
Neuroimaging plays an ever evolving role in the diagnosis, treatment planning, and post-therapy assessment of brain tumors. This review provides an overview of current magnetic resonance imaging (MRI) methods routinely employed in the care of the brain tumor patient. Specifically, we focus on advanced techniques including diffusion, perfusion, spectroscopy, tractography, and functional MRI as they pertain to noninvasive characterization of brain tumors and pretreatment evaluation. The utility of both structural and physiological MRI in the post-therapeutic brain evaluation is also reviewed with special attention to the challenges presented by pseudoprogression and pseudoresponse.
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Affiliation(s)
- Javier E. Villanueva-Meyer
- Department of Radiology and Biomedical Imaging, Neuroradiology Section, University of California San Francisco, San Francisco, California
| | - Marc C. Mabray
- Department of Radiology and Biomedical Imaging, Neuroradiology Section, University of California San Francisco, San Francisco, California
| | - Soonmee Cha
- Department of Radiology and Biomedical Imaging, Neuroradiology Section, University of California San Francisco, San Francisco, California
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46
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Dehkordi ANV, Kamali-Asl A, Wen N, Mikkelsen T, Chetty IJ, Bagher-Ebadian H. DCE-MRI prediction of survival time for patients with glioblastoma multiforme: using an adaptive neuro-fuzzy-based model and nested model selection technique. NMR IN BIOMEDICINE 2017; 30:e3739. [PMID: 28543885 DOI: 10.1002/nbm.3739] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2017] [Revised: 03/28/2017] [Accepted: 03/30/2017] [Indexed: 06/07/2023]
Abstract
This pilot study investigates the construction of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for the prediction of the survival time of patients with glioblastoma multiforme (GBM). ANFIS is trained by the pharmacokinetic (PK) parameters estimated by the model selection (MS) technique in dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) data analysis, and patient age. DCE-MRI investigations of 33 treatment-naïve patients with GBM were studied. Using the modified Tofts model and MS technique, the following physiologically nested models were constructed: Model 1, no vascular leakage (normal tissue); Model 2, leakage without efflux; Model 3, leakage with bidirectional exchange (influx and efflux). For each patient, the PK parameters of the three models were estimated as follows: blood plasma volume (vp ) for Model 1; vp and volume transfer constant (Ktrans ) for Model 2; vp , Ktrans and rate constant (kep ) for Model 3. Using Cox regression analysis, the best combination of the estimated PK parameters, together with patient age, was identified for the design and training of ANFIS. A K-fold cross-validation (K = 33) technique was employed for training, testing and optimization of ANFIS. Given the survival time distribution, three classes of survival were determined and a confusion matrix for the correct classification fraction (CCF) of the trained ANFIS was estimated as an accuracy index of ANFIS's performance. Patient age, kep and ve (Ktrans /kep ) of Model 3, and Ktrans of Model 2, were found to be the most effective parameters for training ANFIS. The CCF of the trained ANFIS was 84.8%. High diagonal elements of the confusion matrix (81.8%, 90.1% and 81.8% for Class 1, Class 2 and Class 3, respectively), with low off-diagonal elements, strongly confirmed the robustness and high performance of the trained ANFIS for predicting the three survival classes. This study confirms that DCE-MRI PK analysis, combined with the MS technique and ANFIS, allows the construction of a DCE-MRI-based fuzzy integrated predictor for the prediction of the survival of patients with GBM.
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Affiliation(s)
- Azimeh N V Dehkordi
- Department of Radiation Medicine Engineering, Shahid Beheshti University, Tehran, Iran
| | - Alireza Kamali-Asl
- Department of Radiation Medicine Engineering, Shahid Beheshti University, Tehran, Iran
| | - Ning Wen
- Department of Radiation Oncology, Henry Ford Hospital, Detroit, Michigan, USA
| | - Tom Mikkelsen
- Department of Neurosurgery, Henry Ford Hospital, Detroit, Michigan, USA
- Ontario Brain Institute, Toronto, Ontario, Canada
| | - Indrin J Chetty
- Department of Radiation Oncology, Henry Ford Hospital, Detroit, Michigan, USA
| | - Hassan Bagher-Ebadian
- Department of Radiation Oncology, Henry Ford Hospital, Detroit, Michigan, USA
- Department of Physics, Oakland University, Rochester, Michigan, USA
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Abstract
Radiogenomics is a relatively new and exciting field within radiology that links different imaging features with diverse genomic events. Genomics advances provided by the Cancer Genome Atlas and the Human Genome Project have enabled us to harness and integrate this information with noninvasive imaging phenotypes to create a better 3-dimensional understanding of tumor behavior and biology. Beyond imaging-histopathology, imaging genomic linkages provide an important layer of complexity that can help in evaluating and stratifying patients into clinical trials, monitoring treatment response, and enhancing patient outcomes. This article reviews some of the important radiogenomic literatures in brain tumors.
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48
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Semmineh NB, Stokes AM, Bell LC, Boxerman JL, Quarles CC. A Population-Based Digital Reference Object (DRO) for Optimizing Dynamic Susceptibility Contrast (DSC)-MRI Methods for Clinical Trials. ACTA ACUST UNITED AC 2017; 3:41-49. [PMID: 28584878 PMCID: PMC5454781 DOI: 10.18383/j.tom.2016.00286] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
The standardization and broad-scale integration of dynamic susceptibility contrast (DSC)-magnetic resonance imaging (MRI) have been confounded by a lack of consensus on DSC-MRI methodology for preventing potential relative cerebral blood volume inaccuracies, including the choice of acquisition protocols and postprocessing algorithms. Therefore, we developed a digital reference object (DRO), using physiological and kinetic parameters derived from in vivo data, unique voxel-wise 3-dimensional tissue structures, and a validated MRI signal computational approach, aimed at validating image acquisition and analysis methods for accurately measuring relative cerebral blood volume in glioblastomas. To achieve DSC-MRI signals representative of the temporal characteristics, magnitude, and distribution of contrast agent-induced T1 and T2* changes observed across multiple glioblastomas, the DRO's input parameters were trained using DSC-MRI data from 23 glioblastomas (>40 000 voxels). The DRO's ability to produce reliable signals for combinations of pulse sequence parameters and contrast agent dosing schemes unlike those in the training data set was validated by comparison with in vivo dual-echo DSC-MRI data acquired in a separate cohort of patients with glioblastomas. Representative applications of the DRO are presented, including the selection of DSC-MRI acquisition and postprocessing methods that optimize CBV accuracy, determination of the impact of DSC-MRI methodology choices on sample size requirements, and the assessment of treatment response in clinical glioblastoma trials.
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Affiliation(s)
- Natenael B Semmineh
- Department of Imaging Research, Barrow Neurological Institute, Phoenix, Arizona
| | - Ashley M Stokes
- Department of Imaging Research, Barrow Neurological Institute, Phoenix, Arizona
| | - Laura C Bell
- Department of Imaging Research, Barrow Neurological Institute, Phoenix, Arizona
| | - Jerrold L Boxerman
- Department of Diagnostic Imaging, RI Hospital and Alpert Medical School of Brown University, Providence, Rhode Island
| | - C Chad Quarles
- Department of Imaging Research, Barrow Neurological Institute, Phoenix, Arizona
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49
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Wen PY, Chang SM, Van den Bent MJ, Vogelbaum MA, Macdonald DR, Lee EQ. Response Assessment in Neuro-Oncology Clinical Trials. J Clin Oncol 2017; 35:2439-2449. [PMID: 28640707 PMCID: PMC5516482 DOI: 10.1200/jco.2017.72.7511] [Citation(s) in RCA: 276] [Impact Index Per Article: 39.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Development of novel therapies for CNS tumors requires reliable assessment of response and progression. This requirement has been particularly challenging in neuro-oncology for which contrast enhancement serves as an imperfect surrogate for tumor volume and is influenced by agents that affect vascular permeability, such as antiangiogenic therapies. In addition, most tumors have a nonenhancing component that can be difficult to accurately quantify. To improve the response assessment in neuro-oncology and to standardize the criteria that are used for different CNS tumors, the Response Assessment in Neuro-Oncology (RANO) working group was established. This multidisciplinary international working group consists of neuro-oncologists, medical oncologists, neuroradiologists, neurosurgeons, radiation oncologists, neuropsychologists, and experts in clinical outcomes assessments, working in collaboration with government and industry to enhance the interpretation of clinical trials. The RANO working group was originally created to update response criteria for high- and low-grade gliomas and to address such issues as pseudoresponse and nonenhancing tumor progression from antiangiogenic therapies, and pseudoprogression from radiochemotherapy. RANO has expanded to include working groups that are focused on other tumors, including brain metastases, leptomeningeal metastases, spine tumors, pediatric brain tumors, and meningiomas, as well as other clinical trial end points, such as clinical outcomes assessments, seizures, corticosteroid use, and positron emission tomography imaging. In an effort to standardize the measurement of neurologic function for clinical assessment, the Neurologic Assessment in Neuro-Oncology scale was drafted. Born out of a workshop conducted by the Jumpstarting Brain Tumor Drug Development Coalition and the US Food and Drug Administration, a standardized brain tumor imaging protocol now exists to reduce variability and improve reliability. Efforts by RANO have been widely accepted and are increasingly being used in neuro-oncology trials, although additional refinements will be needed.
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Affiliation(s)
- Patrick Y. Wen
- Patrick Y. Wen and Eudocia Q. Lee, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA; Susan M. Chang, University of California, San Francisco, San Francisco, CA; Michael A. Vogelbaum, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH; Martin J. Van den Bent, Erasmus University Medical Center Cancer Institute, Rotterdam, the Netherlands; and David R. Macdonald, London Regional Cancer Program, Western University, London, Ontario, Canada
| | - Susan M. Chang
- Patrick Y. Wen and Eudocia Q. Lee, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA; Susan M. Chang, University of California, San Francisco, San Francisco, CA; Michael A. Vogelbaum, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH; Martin J. Van den Bent, Erasmus University Medical Center Cancer Institute, Rotterdam, the Netherlands; and David R. Macdonald, London Regional Cancer Program, Western University, London, Ontario, Canada
| | - Martin J. Van den Bent
- Patrick Y. Wen and Eudocia Q. Lee, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA; Susan M. Chang, University of California, San Francisco, San Francisco, CA; Michael A. Vogelbaum, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH; Martin J. Van den Bent, Erasmus University Medical Center Cancer Institute, Rotterdam, the Netherlands; and David R. Macdonald, London Regional Cancer Program, Western University, London, Ontario, Canada
| | - Michael A. Vogelbaum
- Patrick Y. Wen and Eudocia Q. Lee, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA; Susan M. Chang, University of California, San Francisco, San Francisco, CA; Michael A. Vogelbaum, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH; Martin J. Van den Bent, Erasmus University Medical Center Cancer Institute, Rotterdam, the Netherlands; and David R. Macdonald, London Regional Cancer Program, Western University, London, Ontario, Canada
| | - David R. Macdonald
- Patrick Y. Wen and Eudocia Q. Lee, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA; Susan M. Chang, University of California, San Francisco, San Francisco, CA; Michael A. Vogelbaum, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH; Martin J. Van den Bent, Erasmus University Medical Center Cancer Institute, Rotterdam, the Netherlands; and David R. Macdonald, London Regional Cancer Program, Western University, London, Ontario, Canada
| | - Eudocia Q. Lee
- Patrick Y. Wen and Eudocia Q. Lee, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA; Susan M. Chang, University of California, San Francisco, San Francisco, CA; Michael A. Vogelbaum, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH; Martin J. Van den Bent, Erasmus University Medical Center Cancer Institute, Rotterdam, the Netherlands; and David R. Macdonald, London Regional Cancer Program, Western University, London, Ontario, Canada
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50
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Abstract
Modern imaging techniques, particularly functional imaging techniques that interrogate some specific aspect of underlying tumor biology, have enormous potential in neuro-oncology for disease detection, grading, and tumor delineation to guide biopsy and resection; monitoring treatment response; and targeting radiotherapy. This brief review considers the role of magnetic resonance imaging and spectroscopy, and positron emission tomography in these areas and discusses the factors that limit translation of new techniques to the clinic, in particular, the cost and difficulties associated with validation in multicenter clinical trials.
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Affiliation(s)
- Kevin M Brindle
- Kevin M. Brindle, Richard J. Mair, and Alan J. Wright, Cancer Research UK Cambridge Institute, Cambridge; David Y. Lewis, Cancer Research UK Beatson Institute, Glasgow, United Kingdom; José L. Izquierdo-García, Fundación Centro Nacional de Investigaciones Cardiovasculares Carlos III and Centro de Investigación Biomédica en Red Enfermedades Respiratorias, Madrid, Spain
| | - José L Izquierdo-García
- Kevin M. Brindle, Richard J. Mair, and Alan J. Wright, Cancer Research UK Cambridge Institute, Cambridge; David Y. Lewis, Cancer Research UK Beatson Institute, Glasgow, United Kingdom; José L. Izquierdo-García, Fundación Centro Nacional de Investigaciones Cardiovasculares Carlos III and Centro de Investigación Biomédica en Red Enfermedades Respiratorias, Madrid, Spain
| | - David Y Lewis
- Kevin M. Brindle, Richard J. Mair, and Alan J. Wright, Cancer Research UK Cambridge Institute, Cambridge; David Y. Lewis, Cancer Research UK Beatson Institute, Glasgow, United Kingdom; José L. Izquierdo-García, Fundación Centro Nacional de Investigaciones Cardiovasculares Carlos III and Centro de Investigación Biomédica en Red Enfermedades Respiratorias, Madrid, Spain
| | - Richard J Mair
- Kevin M. Brindle, Richard J. Mair, and Alan J. Wright, Cancer Research UK Cambridge Institute, Cambridge; David Y. Lewis, Cancer Research UK Beatson Institute, Glasgow, United Kingdom; José L. Izquierdo-García, Fundación Centro Nacional de Investigaciones Cardiovasculares Carlos III and Centro de Investigación Biomédica en Red Enfermedades Respiratorias, Madrid, Spain
| | - Alan J Wright
- Kevin M. Brindle, Richard J. Mair, and Alan J. Wright, Cancer Research UK Cambridge Institute, Cambridge; David Y. Lewis, Cancer Research UK Beatson Institute, Glasgow, United Kingdom; José L. Izquierdo-García, Fundación Centro Nacional de Investigaciones Cardiovasculares Carlos III and Centro de Investigación Biomédica en Red Enfermedades Respiratorias, Madrid, Spain
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