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Kamimura S, Mitobe Y, Nakamura K, Matsuda K, Kanemura Y, Kanoto M, Futakuchi M, Sonoda Y. Association of ADC of hyperintense lesions on FLAIR images with TERT promoter mutation status in glioblastoma IDH wild type. Surg Neurol Int 2024; 15:108. [PMID: 38628517 PMCID: PMC11021064 DOI: 10.25259/sni_63_2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 03/03/2024] [Indexed: 04/19/2024] Open
Abstract
Background Although mutations in telomerase reverse transcriptase (TERT) promoter (TERTp) are the most common alterations in glioblastoma (GBM), predicting TERTp mutation status by preoperative imaging is difficult. We determined whether tumour-surrounding hyperintense lesions on fluid-attenuated inversion recovery (FLAIR) were superior to those of contrast-enhanced lesions (CELs) in assessing TERTp mutation status using magnetic resonance imaging (MRI). Methods This retrospective study included 114 consecutive patients with primary isocitrate dehydrogenase (IDH)-wild-type GBM. The apparent diffusion coefficient (ADC) and volume of CELs and FLAIR hyperintense lesions (FHLs) were determined, and the correlation between MRI features and TERTp mutation status was analyzed. In a subset of cases, FHLs were histopathologically analyzed to determine the correlation between tumor cell density and ADC. Results TERTp mutations were present in 77 (67.5%) patients. The minimum ADC of FHLs was significantly lower in the TERTp-mutant group than in the TERTp-wild-type group (mean, 958.9 × 10-3 and 1092.1 × 10-3 mm2/s, respectively, P < 0.01). However, other MRI features, such as CEL and FHL volumes, minimum ADC of CELs, and FHL/CEL ratio, were not significantly different between the two groups. Histopathologic analysis indicated high tumor cell density in FHLs with low ADC. Conclusion The ADC of FHLs was significantly lower in IDH-wild-type GBM with TERTp mutations, suggesting that determining the ADC of FHLs on preoperative MRI might be helpful in predicting TERTp mutation status and surgical planning.
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Affiliation(s)
- Shigeru Kamimura
- Department of Neurosurgery, Yamagata University, Yamagata, Japan
| | - Yuta Mitobe
- Department of Neurosurgery, Yamagata University, Yamagata, Japan
| | - Kazuki Nakamura
- Department of Neurosurgery, Yamagata University, Yamagata, Japan
| | | | - Yonehiro Kanemura
- Department of Biomedical Research and Innovation, National Hospital Organization Osaka National Hospital, Osaka, Japan
| | - Masafumi Kanoto
- Department of Radiology, Division of Diagnostic Radiology, Yamagata University, Yamagata, Japan
| | - Mitsuru Futakuchi
- Department of Pathological Diagnostics, Yamagata University, Yamagata, Japan
| | - Yukihiko Sonoda
- Department of Neurosurgery, Yamagata University, Yamagata, Japan
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Jabehdar Maralani P, Stewart J, Hiremath S, Lawrence L, Chan R, Lau A, Chen H, Chan A, Zeng LK, Tseng CL, Myrehaug S, Soliman H, Detsky J, Heyn C, Lim Fat M, Lipsman N, Sahgal A. Relationship between apparent diffusion coefficient and survival as a function of distance from gross tumor volume on radiation planning MRI in newly diagnosed glioblastoma. J Neurooncol 2023; 164:597-605. [PMID: 37707752 DOI: 10.1007/s11060-023-04440-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 08/26/2023] [Indexed: 09/15/2023]
Abstract
PURPOSE To investigate the changes in apparent diffusion coefficient (ADC) within incrementally-increased margins beyond the gross tumor volume (GTV) on post-operative radiation planning MRI and their prognostic utility in glioblastoma. METHODS Radiation planning MRIs of adult patients with newly diagnosed glioblastoma from 2017 to 2020 were assessed. The ADC values were normalized to contralateral normal white matter (nADC). Using 1 mm isotropic incremental margin increases from the GTV, the nADC values were calculated at each increment. Age, ECOG performance status, extent of resection and MGMT promoter methylation status were obtained from medical records. Using univariate and multivariable Cox regression analysis, association of nADC to progression-free and overall survival (PFS, OS) was assessed at each increment. RESULTS Seventy consecutive patients with mean age of 53.6 ± 10.3 years, were evaluated. The MGMT promoter was methylated in 31 (44.3%), unmethylated in 36 (51.6%) and unknown in 3 (4.3%) patients. 11 (16%) underwent biopsy, 41 (44%) subtotal resection and 18 (26%) gross total resection. For each 1 mm increase in distance from GTV, the nADC decreased by 0.16% (p < 0.0001). At 1-5 mm increment, the nADC was associated with OS (p < 0.01). From 6 to 11 mm increment the nADC was associated with OS with the p-value gradually increasing from 0.018 to 0.046. nADC was not associated with PFS. CONCLUSION The nADC values at 1-11 mm increments from the GTV margin were associated with OS. Future prospective multicenter studies are needed to validate the findings and to pave the way for the utilization of ADC for margin reduction in radiation planning.
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Affiliation(s)
- Pejman Jabehdar Maralani
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada.
| | - James Stewart
- Department of Radiation Oncology, Sunnybrook Odette Cancer Centre, University of Toronto, Toronto, Canada
| | - Shivaprakash Hiremath
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - Liam Lawrence
- Department of Radiation Oncology, Sunnybrook Odette Cancer Centre, University of Toronto, Toronto, Canada
| | - Rachel Chan
- Department of Radiation Oncology, Sunnybrook Odette Cancer Centre, University of Toronto, Toronto, Canada
| | - Angus Lau
- Department of Radiation Oncology, Sunnybrook Odette Cancer Centre, University of Toronto, Toronto, Canada
| | - Hanbo Chen
- Department of Radiation Oncology, Sunnybrook Odette Cancer Centre, University of Toronto, Toronto, Canada
| | - Aimee Chan
- Department of Radiation Oncology, Sunnybrook Odette Cancer Centre, University of Toronto, Toronto, Canada
| | - Liang K Zeng
- Department of Radiation Oncology, Sunnybrook Odette Cancer Centre, University of Toronto, Toronto, Canada
| | - Chia-Lin Tseng
- Department of Radiation Oncology, Sunnybrook Odette Cancer Centre, University of Toronto, Toronto, Canada
| | - Sten Myrehaug
- Department of Radiation Oncology, Sunnybrook Odette Cancer Centre, University of Toronto, Toronto, Canada
| | - Hany Soliman
- Department of Radiation Oncology, Sunnybrook Odette Cancer Centre, University of Toronto, Toronto, Canada
| | - Jay Detsky
- Department of Radiation Oncology, Sunnybrook Odette Cancer Centre, University of Toronto, Toronto, Canada
| | - Chinthaka Heyn
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - MaryJane Lim Fat
- Division of Neurology, Department of Medicine, Sunnybrook Odette Cancer Centre, University of Toronto, Toronto, Canada
| | - Nir Lipsman
- Division of Neurosurgery, Department of Surgery, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - Arjun Sahgal
- Department of Radiation Oncology, Sunnybrook Odette Cancer Centre, University of Toronto, Toronto, Canada
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Tang PLY, Méndez Romero A, Jaspers JPM, Warnert EAH. The potential of advanced MR techniques for precision radiotherapy of glioblastoma. MAGMA (NEW YORK, N.Y.) 2022; 35:127-143. [PMID: 35129718 PMCID: PMC8901515 DOI: 10.1007/s10334-021-00997-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 12/23/2021] [Accepted: 12/24/2021] [Indexed: 06/14/2023]
Abstract
As microscopic tumour infiltration of glioblastomas is not visible on conventional magnetic resonance (MR) imaging, an isotropic expansion of 1-2 cm around the visible tumour is applied to define the clinical target volume for radiotherapy. An opportunity to visualize microscopic infiltration arises with advanced MR imaging. In this review, various advanced MR biomarkers are explored that could improve target volume delineation for radiotherapy of glioblastomas. Various physiological processes in glioblastomas can be visualized with different advanced MR techniques. Combining maps of oxygen metabolism (CMRO2), relative cerebral blood volume (rCBV), vessel size imaging (VSI), and apparent diffusion coefficient (ADC) or amide proton transfer (APT) can provide early information on tumour infiltration and high-risk regions of future recurrence. Oxygen consumption is increased 6 months prior to tumour progression being visible on conventional MR imaging. However, presence of the Warburg effect, marking a switch from an infiltrative to a proliferative phenotype, could result in CMRO2 to appear unaltered in high-risk regions. Including information on biomarkers representing angiogenesis (rCBV and VSI) and hypercellularity (ADC) or protein concentration (APT) can omit misinterpretation due to the Warburg effect. Future research should evaluate these biomarkers in radiotherapy planning to explore the potential of advanced MR techniques to personalize target volume delineation with the aim to improve local tumour control and/or reduce radiation-induced toxicity.
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Affiliation(s)
- Patrick L Y Tang
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center, Rotterdam, The Netherlands.
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands.
| | - Alejandra Méndez Romero
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center, Rotterdam, The Netherlands
| | - Jaap P M Jaspers
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center, Rotterdam, The Netherlands
| | - Esther A H Warnert
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
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MATSUDA K, KOKUBO Y, KANEMURA Y, KANOTO M, SONODA Y. Preoperative Apparent Diffusion Coefficient of Peritumoral Lesion Associate with Recurrence in Patients with Glioblastoma. Neurol Med Chir (Tokyo) 2022; 62:28-34. [PMID: 34707068 PMCID: PMC8754683 DOI: 10.2176/nmc.oa.2021-0182] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 09/13/2021] [Indexed: 11/20/2022] Open
Abstract
Additional resection beyond contrast enhanced lesion on MRI is recently considered to prolong survival in glioblastoma. Prediction of future recurrent site in the peritumoral lesion on preoperative MRI could be useful for surgical planning. The objective of this study was to determine if the preoperative ADC value was associated with the site of future recurrence in patients with glioblastoma. We retrospectively analyzed 21 patients with primary GBM. The ADC value on MRI were analyzed before and after operation and at recurrence. The region of interests (ROIs) were set to cover almost the FLAIR high-signal lesion surrounding contrast enhanced lesion. We determined whether the value of ADC on MRI was correlated with the spot of future recurrence. Among 1844 ROIs determined in the FLAIR high-signal lesion on preoperative MRI, new enhanced lesions occurred in 186 sites. The other 1258 sites showed no change or decrease in size on follow up MRI, and the other 400 sites were removed in first operation. The pre-operative ADC values of sites corresponding to future recurrence were significantly lower than that of non-recurrent sites (p <0.001). We suggest that a low ADC values in FLAIR high-signal lesion is corresponding to recurrence, and useful for predicting recurrence of the lesion in cases of GBM. These results will be helpful for planning of surgery or radiation therapy and facilitate future prospective studies on GBM.
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Affiliation(s)
- Kenichiro MATSUDA
- Department of Neurosurgery, Faculty of Medicine, Yamagata University, Yamagata, Yamagata, Japan
| | - Yasuaki KOKUBO
- Department of Neurosurgery, Faculty of Medicine, Yamagata University, Yamagata, Yamagata, Japan
| | - Yonehiro KANEMURA
- Department of Biomedical Research and Innovation, Institute for Clinical Research, National Hospital Organization Osaka National Hospital, Osaka, Osaka, Japan
| | - Masafumi KANOTO
- Department of Radiology, Division of Diagnositc Radiology, Faculty of Medicine, Yamagata University, Yamagata, Yamagata, Japan
| | - Yukihiko SONODA
- Department of Neurosurgery, Faculty of Medicine, Yamagata University, Yamagata, Yamagata, Japan
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Jabehdar Maralani P, Myrehaug S, Mehrabian H, Chan AKM, Wintermark M, Heyn C, Conklin J, Ellingson BM, Rahimi S, Lau AZ, Tseng CL, Soliman H, Detsky J, Daghighi S, Keith J, Munoz DG, Das S, Atenafu EG, Lipsman N, Perry J, Stanisz G, Sahgal A. Intravoxel incoherent motion (IVIM) modeling of diffusion MRI during chemoradiation predicts therapeutic response in IDH wildtype glioblastoma. Radiother Oncol 2021; 156:258-265. [PMID: 33418005 PMCID: PMC8186561 DOI: 10.1016/j.radonc.2020.12.037] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 12/14/2020] [Accepted: 12/22/2020] [Indexed: 12/12/2022]
Abstract
Background: Prediction of early progression in glioblastoma may provide an opportunity to personalize treatment. Simplified intravoxel incoherent motion (IVIM) MRI offers quantitative estimates of diffusion and perfusion metrics. We investigated whether these metrics, during chemoradiation, could predict treatment outcome. Methods: 38 patients with newly diagnosed IDH-wildtype glioblastoma undergoing 6-week/30-fraction chemoradiation had standardized post-operative MRIs at baseline (radiation planning), and at the 10th and 20th fractions. Non-overlapping T1-enhancing (T1C) and non-enhancing T2-FLAIR hyperintense regions were independently segmented. Apparent diffusion coefficient (ADCT1C, ADCT2-FLAIR) and perfusion fraction (fT1C, fT2-FLAIR) maps were generated with simplified IVIM modelling. Parameters associated with progression before or after 6.9 months (early vs late progression, respectively), overall survival (OS) and progression-free survival (PFS) were investigated. Results: Higher ADCT2-FLAIR at baseline [Odds Ratio (OR) = 1.06, 95% CI 1.01–1.15, p = 0.025], lower fT2-FLAIR at fraction 10 (OR = 2.11, 95% CI 1.04–4.27, p = 0.018), and lack of increase in ADCT2-FLAIR at fraction 20 compared to baseline (OR = 1.12, 95% CI 1.02–1.22, p = 0.02) were associated with early progression. Combining ADCT2-FLAIR at baseline, fT2-FLAIR at fraction 10, ECOG and MGMT promoter methylation status significantly improved AUC to 90.3% compared to a model with only ECOG and MGMT promoter methylation status (p = 0.001). Using multivariable analysis, neither IVIM metrics were associated with OS but higher fT2-FLAIR at fraction 10 (HR = 0.72, 95% CI 0.56–0.95, p = 0.018) was associated with longer PFS. Conclusion: ADCT2-FLAIR at baseline, its lack of increase from baseline to fraction 20, or fT2-FLAIR at fraction 10 significantly predicted early progression. fT2-FLAIR at fraction 10 was associated with PFS.
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Affiliation(s)
- Pejman Jabehdar Maralani
- Department of Medical Imaging, Sunnybrook Health Sciences Center, University of Toronto, Canada.
| | - Sten Myrehaug
- Department of Radiation Oncology, Sunnybrook Health Sciences Center, University of Toronto, Canada
| | - Hatef Mehrabian
- Department of Radiation Oncology, Sunnybrook Health Sciences Center, University of Toronto, Canada
| | - Aimee K M Chan
- Department of Medical Imaging, Sunnybrook Health Sciences Center, University of Toronto, Canada
| | - Max Wintermark
- Department of Radiology, Stanford University, United States
| | - Chris Heyn
- Department of Medical Imaging, Sunnybrook Health Sciences Center, University of Toronto, Canada
| | - John Conklin
- Department of Radiology, Massachusetts General Hospital, United States
| | - Benjamin M Ellingson
- Department of Radiological Sciences and Psychiatry, University of California Los Angeles, United States
| | - Saba Rahimi
- Department of Biomedical Engineering, University of Toronto, Canada
| | - Angus Z Lau
- Department of Medical Biophysics, Sunnybrook Research Institute, University of Toronto, Canada
| | - Chia-Lin Tseng
- Department of Radiation Oncology, Sunnybrook Health Sciences Center, University of Toronto, Canada
| | - Hany Soliman
- Department of Radiation Oncology, Sunnybrook Health Sciences Center, University of Toronto, Canada
| | - Jay Detsky
- Department of Radiation Oncology, Sunnybrook Health Sciences Center, University of Toronto, Canada
| | - Shadi Daghighi
- Department of Medical Imaging, Sunnybrook Health Sciences Center, University of Toronto, Canada
| | - Julia Keith
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Canada
| | - David G Munoz
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Canada
| | - Sunit Das
- Department of Surgery, Division of Neurosurgery, University of Toronto, Canada
| | | | - Nir Lipsman
- Department of Surgery, Division of Neurosurgery, University of Toronto, Canada
| | - James Perry
- Department of Medicine, Division of Neurology, University of Toronto, Canada
| | - Greg Stanisz
- Department of Medical Biophysics, Sunnybrook Research Institute, University of Toronto, Canada
| | - Arjun Sahgal
- Department of Radiation Oncology, Sunnybrook Health Sciences Center, University of Toronto, Canada
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Applications of radiomics and machine learning for radiotherapy of malignant brain tumors. Strahlenther Onkol 2020; 196:856-867. [PMID: 32394100 PMCID: PMC7498494 DOI: 10.1007/s00066-020-01626-8] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Accepted: 04/22/2020] [Indexed: 12/12/2022]
Abstract
Background Magnetic resonance imaging (MRI) and amino acid positron-emission tomography (PET) of the brain contain a vast amount of structural and functional information that can be analyzed by machine learning algorithms and radiomics for the use of radiotherapy in patients with malignant brain tumors. Methods This study is based on comprehensive literature research on machine learning and radiomics analyses in neuroimaging and their potential application for radiotherapy in patients with malignant glioma or brain metastases. Results Feature-based radiomics and deep learning-based machine learning methods can be used to improve brain tumor diagnostics and automate various steps of radiotherapy planning. In glioma patients, important applications are the determination of WHO grade and molecular markers for integrated diagnosis in patients not eligible for biopsy or resection, automatic image segmentation for target volume planning, prediction of the location of tumor recurrence, and differentiation of pseudoprogression from actual tumor progression. In patients with brain metastases, radiomics is applied for additional detection of smaller brain metastases, accurate segmentation of multiple larger metastases, prediction of local response after radiosurgery, and differentiation of radiation injury from local brain metastasis relapse. Importantly, high diagnostic accuracies of 80–90% can be achieved by most approaches, despite a large variety in terms of applied imaging techniques and computational methods. Conclusion Clinical application of automated image analyses based on radiomics and artificial intelligence has a great potential for improving radiotherapy in patients with malignant brain tumors. However, a common problem associated with these techniques is the large variability and the lack of standardization of the methods applied.
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Trip AK, Jensen MB, Kallehauge JF, Lukacova S. Individualizing the radiotherapy target volume for glioblastoma using DTI-MRI: a phase 0 study on coverage of recurrences. Acta Oncol 2019; 58:1532-1535. [PMID: 31303079 DOI: 10.1080/0284186x.2019.1637018] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Affiliation(s)
- Anouk K. Trip
- Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - Morten B. Jensen
- Department of Medical Physics, Aarhus University Hospital, Aarhus, Denmark
| | | | - Slavka Lukacova
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
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Molecular and Clinical Insights into the Invasive Capacity of Glioblastoma Cells. JOURNAL OF ONCOLOGY 2019; 2019:1740763. [PMID: 31467533 PMCID: PMC6699388 DOI: 10.1155/2019/1740763] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Revised: 07/01/2019] [Accepted: 07/07/2019] [Indexed: 12/22/2022]
Abstract
The invasive capacity of GBM is one of the key tumoral features associated with treatment resistance, recurrence, and poor overall survival. The molecular machinery underlying GBM invasiveness comprises an intricate network of signaling pathways and interactions with the extracellular matrix and host cells. Among them, PI3k/Akt, Wnt, Hedgehog, and NFkB play a crucial role in the cellular processes related to invasion. A better understanding of these pathways could potentially help in developing new therapeutic approaches with better outcomes. Nevertheless, despite significant advances made over the last decade on these molecular and cellular mechanisms, they have not been translated into the clinical practice. Moreover, targeting the infiltrative tumor and its significance regarding outcome is still a major clinical challenge. For instance, the pre- and intraoperative methods used to identify the infiltrative tumor are limited when trying to accurately define the tumor boundaries and the burden of tumor cells in the infiltrated parenchyma. Besides, the impact of treating the infiltrative tumor remains unclear. Here we aim to highlight the molecular and clinical hallmarks of invasion in GBM.
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Zeng Q, Jiang B, Shi F, Ling C, Dong F, Zhang J. Bright Edge Sign on High b-Value Diffusion-Weighted Imaging as a New Imaging Biomarker to Predict Poor Prognosis in Glioma Patients: A Retrospective Pilot Study. Front Oncol 2019; 9:424. [PMID: 31179243 PMCID: PMC6537606 DOI: 10.3389/fonc.2019.00424] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Accepted: 05/03/2019] [Indexed: 11/13/2022] Open
Abstract
Purpose: To investigate the prognostic value of bright edge sign observed on high b-value diffusion-weighted imaging (DWI) map in glioma patients. Methods: We retrospectively reviewed our prospectively collected database for gliomas. Bright edge sign was defined as the presence of extremely high signal in tumor margin on high b-value DWI map (b = 3,000 s/mm2) with the signal intensity higher than those in contralateral normal white matter and tumor central region. Extremely poor prognosis was defined as overall survival time < 9 months. Survival analyses were conducted by using the Cox regression for both the univariable and multivariable analyses. Results: A total of 52 patients were enrolled (WHO IV, 25; WHO III, 13; WHO II, 14). Bright edge sign presented in 10 (19.2%) patients (WHO IV, 5; WHO III, 3; WHO II, 2). Nine (90.0%) patients with bright edge sign had extremely poor prognosis, while only 1 (2.4 %) patient without bright edge sign had extremely poor prognosis. The sensitivity and specificity of bright edge sign in determining extremely poor prognosis were 90 and 97.7%, respectively. Bright edge sign (HR [95% CI] = 25.11 [7.26-86.81], p < 0.001) was an independent predictor of poor prognosis after adjustment. Conclusion: Bright edge sign on high b-value DWI may be an accurate predictor of extremely poor prognosis in glioma patients, regardless of pathologic grades.
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Affiliation(s)
- Qiang Zeng
- Department of Neurosurgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Biao Jiang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Feina Shi
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chenhan Ling
- Department of Neurosurgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Fei Dong
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jianmin Zhang
- Department of Neurosurgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Brain Research Institute, Zhejiang University, Hangzhou, China.,Collaborative Innovation Center for Brain Science, Zhejiang University, Hangzhou, China
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Press RH, Zhong J, Gurbani SS, Weinberg BD, Eaton BR, Shim H, Shu HKG. The Role of Standard and Advanced Imaging for the Management of Brain Malignancies From a Radiation Oncology Standpoint. Neurosurgery 2018; 85:165-179. [DOI: 10.1093/neuros/nyy461] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Accepted: 08/30/2018] [Indexed: 01/20/2023] Open
Abstract
Abstract
Radiation therapy (RT) plays a critical role in the overall management of many central nervous system (CNS) tumors. Advances in RT treatment planning, with techniques such as intensity modulated radiation therapy, volumetric modulated arc therapy, and stereotactic radiosurgery, now allow the delivery of highly conformal dose with great precision. These techniques rely on high-resolution 3-dimensional anatomical imaging modalities such as computed tomography or magnetic resonance imaging (MRI) scans to accurately and reliably define CNS targets and normal tissue avoidance structures. The integration of cross-sectional imaging into radiation oncology has directly translated into improvements in the therapeutic window of RT, and the union between radiation oncology and imaging is only expected to grow stronger. In addition, advanced imaging modalities including diffusion, perfusion, and spectroscopic MRIs as well as positron emission tomography (PET) scans with novel tracers are being utilized to provide additional insight into tumor biology and behavior beyond anatomy. Together, these standard and advanced imaging modalities hold significant potential to improve future RT delivery and response assessment. In this review, we will discuss the current utilization of standard/advanced imaging for CNS tumors from a radiation oncology perspective as well as the implications of novel MRI and PET modalities currently under investigation.
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Affiliation(s)
- Robert H Press
- Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, Georgia
| | - Jim Zhong
- Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, Georgia
| | - Saumya S Gurbani
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, Georgia
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, Georgia
| | - Brent D Weinberg
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, Georgia
| | - Bree R Eaton
- Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, Georgia
| | - Hyunsuk Shim
- Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, Georgia
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, Georgia
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, Georgia
| | - Hui-Kuo G Shu
- Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, Georgia
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Piper RJ, Senthil KK, Yan JL, Price SJ. Neuroimaging classification of progression patterns in glioblastoma: a systematic review. J Neurooncol 2018; 139:77-88. [PMID: 29603080 DOI: 10.1007/s11060-018-2843-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2017] [Accepted: 03/21/2018] [Indexed: 01/05/2023]
Abstract
BACKGROUND Our primary objective was to report the current neuroimaging classification systems of spatial patterns of progression in glioblastoma. In addition, we aimed to report the terminology used to describe 'progression' and to assess the compliance with the Response Assessment in Neuro-Oncology (RANO) Criteria. METHODS We conducted a systematic review to identify all neuroimaging studies of glioblastoma that have employed a categorical classification system of spatial progression patterns. Our review was registered with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) registry. RESULTS From the included 157 results, we identified 129 studies that used labels of spatial progression patterns that were not based on radiation volumes (Group 1) and 50 studies that used labels that were based on radiation volumes (Group 2). In Group 1, we found 113 individual labels and the most frequent were: local/localised (58%), distant/distal (51%), diffuse (20%), multifocal (15%) and subependymal/subventricular zone (15%). We identified 13 different labels used to refer to 'progression', of which the most frequent were 'recurrence' (99%) and 'progression' (92%). We identified that 37% (n = 33/90) of the studies published following the release of the RANO classification were adherent compliant with the RANO criteria. CONCLUSIONS Our review reports significant heterogeneity in the published systems used to classify glioblastoma spatial progression patterns. Standardization of terminology and classification systems used in studying progression would increase the efficiency of our research in our attempts to more successfully treat glioblastoma.
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Affiliation(s)
- Rory J Piper
- Cambridge Brain Tumour Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Hill's Road, Cambridge, CB2 0QQ, UK.
| | - Keerthi K Senthil
- Cambridge Brain Tumour Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Hill's Road, Cambridge, CB2 0QQ, UK
| | - Jiun-Lin Yan
- Cambridge Brain Tumour Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Hill's Road, Cambridge, CB2 0QQ, UK
| | - Stephen J Price
- Cambridge Brain Tumour Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Hill's Road, Cambridge, CB2 0QQ, UK
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Zeng Q, Ling C, Shi F, Dong F, Jiang B, Zhang J. Glioma infiltration sign on high b-value diffusion-weighted imaging in gliomas and its prognostic value. J Magn Reson Imaging 2018; 48:643-651. [PMID: 29498144 DOI: 10.1002/jmri.25986] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Accepted: 02/03/2018] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND Glioma cells may infiltrate beyond the tumor margins revealed on conventional structural images. PURPOSE To investigate whether the presence of a glioma infiltration sign on high b-value diffusion-weighted imaging (DWI) can predict the prognosis of gliomas. STUDY TYPE Retrospective cohort. SUBJECTS Fifty-two patients with gliomas (14 WHO grade II; 13 WHO grade III; 25 WHO grade IV). FIELD STRENGTH/SEQUENCE 3.0T, including a T1 -weighted contrast-enhanced (T1 w-CE) sequence, contrast-enhanced T2 -flair sequence, and a DWI sequence. ASSESSMENT T1 w-CE images and contrast-enhanced T2 -flair images were used for identifying the tumor region for enhancing and nonenhancing gliomas, respectively. The glioma infiltration sign was defined as the presence of a peritumoral abnormal high signal region on DWI map, which was adjacent to the tumor region and had higher signal than surrounding areas. This sign was assessed on a high b-value DWI map with b = 3000 s/mm2 . For patients with glioma infiltration sign, DWI3000max , DWI1000max , ADC3000min , and ADC1000min were measured by drawing a region of interest over the peritumoral abnormal high signal region. STATISTICAL TESTS Survival analysis was conducted by using Cox regression. RESULTS Glioma infiltration sign was observed in 28 (53.8%) patients. The occurrence rate of this sign was 92.0% in grade IV gliomas, 30.8% in grade III gliomas, and 7.1% in grade II gliomas. The glioma infiltration sign could independently predict both the progression-free survival (hazard ratio [HR], 95% confidence interval [CI] = 8.58 [3.19-23.03], P < 0.001) and overall survival (HR, 95% CI = 11.90 [3.41-41.55], P < 0.001) after adjustment. For patients with glioma infiltration sign, DWI3000max (P = 0.005) and ADC3000min (P = 0.008) were both independent predictors of overall survival after adjustment, while DWI1000max and ADC1000min were not. DATA CONCLUSION The glioma infiltration sign on high b-value DWI is an independent predictor of poor prognosis in glioma patients. High b-value DWI might be a convenient method to detect glioma infiltration. LEVEL OF EVIDENCE 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.
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Affiliation(s)
- Qiang Zeng
- Department of Neurosurgery, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Chenhan Ling
- Department of Neurosurgery, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Feina Shi
- Department of Neurology, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Fei Dong
- Department of Radiology, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Biao Jiang
- Department of Radiology, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jianmin Zhang
- Department of Neurosurgery, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Brain Research Institute, Zhejiang University, Hangzhou, Zhejiang, China
- Collaborative Innovation Center for Brain Science, Zhejiang University, Hangzhou, Zhejiang, China
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Hassanzadeh C, Rao YJ, Chundury A, Rowe J, Ponisio MR, Sharma A, Miller-Thomas M, Tsien CI, Ippolito JE. Multiparametric MRI and [ 18F]Fluorodeoxyglucose Positron Emission Tomography Imaging Is a Potential Prognostic Imaging Biomarker in Recurrent Glioblastoma. Front Oncol 2017; 7:178. [PMID: 28868256 PMCID: PMC5563320 DOI: 10.3389/fonc.2017.00178] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2017] [Accepted: 08/03/2017] [Indexed: 12/13/2022] Open
Abstract
Purpose/objectives Multiparametric advanced MR and [18F]fluorodeoxyglucose (FDG)-positron emission tomography (PET) imaging may be important biomarkers for prognosis as well for distinguishing recurrent glioblastoma multiforme (GBM) from treatment-related changes. Methods/materials We retrospectively evaluated 30 patients treated with chemoradiation for GBM and underwent advanced MR and FDG-PET for confirmation of tumor progression. Multiparametric MRI and FDG-PET imaging metrics were evaluated for their association with 6-month overall (OS) and progression-free survival (PFS) based on pathological, radiographic, and clinical criteria. Results 17 males and 13 females were treated between 2001 and 2014, and later underwent FDG-PET at suspected recurrence. Baseline FDG-PET and MRI imaging was obtained at a median of 7.5 months [interquartile range (IQR) 3.7–12.4] following completion of chemoradiation. Median follow-up after FDG-PET imaging was 10 months (IQR 7.2–13.0). Receiver-operator characteristic curve analysis identified that lesions characterized by a ratio of the SUVmax to the normal contralateral brain (SUVmax/NB index) >1.5 and mean apparent diffusion coefficient (ADC) value of ≤1,400 × 10−6 mm2/s correlated with worse 6-month OS and PFS. We defined three patient groups that predicted the probability of tumor progression: SUVmax/NB index >1.5 and ADC ≤1,400 × 10−6 mm2/s defined high-risk patients (n = 7), SUVmax/NB index ≤1.5 and ADC >1,400 × 10−6 mm2/s defined low-risk patients (n = 11), and intermediate-risk (n = 12) defined the remainder of the patients. Median OS following the time of the FDG-PET scan for the low, intermediate, and high-risk groups were 23.5, 10.5, and 3.8 months (p < 0.01). Median PFS were 10.0, 4.4, and 1.9 months (p = 0.03). Rates of progression at 6-months in the low, intermediate, and high-risk groups were 36, 67, and 86% (p = 0.04). Conclusion Recurrent GBM in the molecular era is associated with highly variable outcomes. Multiparametric MR and FDG-PET biomarkers may provide a clinically relevant, non-invasive and cost-effective method of predicting prognosis and improving clinical decision making in the treatment of patients with suspected tumor recurrence.
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Affiliation(s)
- Comron Hassanzadeh
- Department of Radiation Oncology, Washington University in St. Louis, St. Louis, MO, United States.,Department of Genetics, Washington University in St. Louis, St. Louis, MO, United States
| | - Yuan James Rao
- Department of Radiation Oncology, Washington University in St. Louis, St. Louis, MO, United States
| | - Anupama Chundury
- Department of Radiation Oncology, Washington University in St. Louis, St. Louis, MO, United States
| | - Jackson Rowe
- Department of Radiation Oncology, Washington University in St. Louis, St. Louis, MO, United States
| | - Maria Rosana Ponisio
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, United States
| | - Akash Sharma
- Department of Radiology, Mayo Clinic Florida, Jacksonville, FL, United States
| | - Michelle Miller-Thomas
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, United States
| | - Christina I Tsien
- Department of Radiation Oncology, Washington University in St. Louis, St. Louis, MO, United States
| | - Joseph E Ippolito
- Department of Genetics, Washington University in St. Louis, St. Louis, MO, United States.,Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, United States
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14
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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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Trusheim J, Dunbar E, Battiste J, Iwamoto F, Mohile N, Damek D, Bota DA, Connelly J. A state-of-the-art review and guidelines for tumor treating fields treatment planning and patient follow-up in glioblastoma. CNS Oncol 2016; 6:29-43. [PMID: 27628854 PMCID: PMC6027938 DOI: 10.2217/cns-2016-0032] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Tumor treating fields (TTFields) are an integral treatment modality in the management of glioblastoma and extend overall survival when combined with maintenance temozolomide in newly diagnosed patients. Complexities exist regarding correct selection of imaging sequences with which to perform TTFields treatment planning. Guidelines are warranted first, to facilitate treatment planning standardization across medical disciplines and institutions, to ensure optimal TTFields delivery to the tumor and peritumoral brain zone while maximizing patient safety, and also to mitigate the risk of premature cessation of a potentially beneficial treatment. This summary guideline outlines methods for starting patients on TTFields, for monitoring patient response to therapy and provides a framework for evaluating when therapy should be re-planned, based on the extent of sequential imaging changes.
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Affiliation(s)
- John Trusheim
- Abbott Northwestern Hospital Neuroscience Institute, Minneapolis, MN, USA
| | - Erin Dunbar
- PiedmontBrain Tumor Center, Atlanta, GA, USA
| | - James Battiste
- Oklahoma University Health Sciences Center, Oklahoma City, OK, USA
| | - Fabio Iwamoto
- The Neurological Institute of New York, Columbia University, New York, NY, USA
| | - Nimish Mohile
- University of Rochester Medical Center, Rochester, NY, USA
| | - Denise Damek
- University of Colorado Hospital, Aurora, CO, USA
| | - Daniela A Bota
- University of California, Irvine Medical Center, Orange, CA, USA
| | - Jennifer Connelly
- Froedtert Hospital & The Medical College of Wisconsin, Milwaukee, WI, USA
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16
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Do perfusion and diffusion MRI predict glioblastoma relapse sites following chemoradiation? J Neurooncol 2016; 130:181-192. [DOI: 10.1007/s11060-016-2232-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2016] [Accepted: 07/31/2016] [Indexed: 01/18/2023]
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17
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van der Hoorn A, Yan JL, Larkin TJ, Boonzaier NR, Matys T, Price SJ. Posttreatment Apparent Diffusion Coefficient Changes in the Periresectional Area in Patients with Glioblastoma. World Neurosurg 2016; 92:159-165. [DOI: 10.1016/j.wneu.2016.04.129] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2016] [Revised: 04/28/2016] [Accepted: 04/30/2016] [Indexed: 12/20/2022]
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18
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Chang K, Zhang B, Guo X, Zong M, Rahman R, Sanchez D, Winder N, Reardon DA, Zhao B, Wen PY, Huang RY. Multimodal imaging patterns predict survival in recurrent glioblastoma patients treated with bevacizumab. Neuro Oncol 2016; 18:1680-1687. [PMID: 27257279 DOI: 10.1093/neuonc/now086] [Citation(s) in RCA: 73] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2016] [Accepted: 03/30/2016] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Bevacizumab is a humanized antibody against vascular endothelial growth factor approved for treatment of recurrent glioblastoma. There is a need to discover imaging biomarkers that can aid in the selection of patients who will likely derive the most survival benefit from bevacizumab. METHODS The aim of the study was to examine if pre- and posttherapy multimodal MRI features could predict progression-free survival and overall survival (OS) for patients with recurrent glioblastoma treated with bevacizumab. The patient population included 84 patients in a training cohort and 42 patients in a testing cohort, separated based on pretherapy imaging date. Tumor volumes of interest were segmented from contrast-enhanced T1-weighted and fluid attenuated inversion recovery images and were used to derive volumetric, shape, texture, parametric, and histogram features. A total of 2293 pretherapy and 9811 posttherapy features were used to generate the model. RESULTS Using standard radiographic assessment criteria, the hazard ratio for predicting OS was 3.38 (P < .001). The hazard ratios for pre- and posttherapy features predicting OS were 5.10 (P < .001) and 3.64 (P < .005) for the training and testing cohorts, respectively. CONCLUSION With the use of machine learning techniques to analyze imaging features derived from pre- and posttherapy multimodal MRI, we were able to develop a predictive model for patient OS that could potentially assist clinical decision making.
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Affiliation(s)
- Ken Chang
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts (K.C., B.Z., R.R., D.S., N.W., R.Y.H.); Department of Radiology, College of Physicians and Surgeons, Columbia University, New York, New York (X.G., M.Z., B.Z.); Center for Neuro-Oncology, Dana-Farber/Brigham and Women's Cancer Center, Boston, Massachusetts (D.A.R., P.Y.W.)
| | - Biqi Zhang
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts (K.C., B.Z., R.R., D.S., N.W., R.Y.H.); Department of Radiology, College of Physicians and Surgeons, Columbia University, New York, New York (X.G., M.Z., B.Z.); Center for Neuro-Oncology, Dana-Farber/Brigham and Women's Cancer Center, Boston, Massachusetts (D.A.R., P.Y.W.)
| | - Xiaotao Guo
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts (K.C., B.Z., R.R., D.S., N.W., R.Y.H.); Department of Radiology, College of Physicians and Surgeons, Columbia University, New York, New York (X.G., M.Z., B.Z.); Center for Neuro-Oncology, Dana-Farber/Brigham and Women's Cancer Center, Boston, Massachusetts (D.A.R., P.Y.W.)
| | - Min Zong
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts (K.C., B.Z., R.R., D.S., N.W., R.Y.H.); Department of Radiology, College of Physicians and Surgeons, Columbia University, New York, New York (X.G., M.Z., B.Z.); Center for Neuro-Oncology, Dana-Farber/Brigham and Women's Cancer Center, Boston, Massachusetts (D.A.R., P.Y.W.)
| | - Rifaquat Rahman
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts (K.C., B.Z., R.R., D.S., N.W., R.Y.H.); Department of Radiology, College of Physicians and Surgeons, Columbia University, New York, New York (X.G., M.Z., B.Z.); Center for Neuro-Oncology, Dana-Farber/Brigham and Women's Cancer Center, Boston, Massachusetts (D.A.R., P.Y.W.)
| | - David Sanchez
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts (K.C., B.Z., R.R., D.S., N.W., R.Y.H.); Department of Radiology, College of Physicians and Surgeons, Columbia University, New York, New York (X.G., M.Z., B.Z.); Center for Neuro-Oncology, Dana-Farber/Brigham and Women's Cancer Center, Boston, Massachusetts (D.A.R., P.Y.W.)
| | - Nicolette Winder
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts (K.C., B.Z., R.R., D.S., N.W., R.Y.H.); Department of Radiology, College of Physicians and Surgeons, Columbia University, New York, New York (X.G., M.Z., B.Z.); Center for Neuro-Oncology, Dana-Farber/Brigham and Women's Cancer Center, Boston, Massachusetts (D.A.R., P.Y.W.)
| | - David A Reardon
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts (K.C., B.Z., R.R., D.S., N.W., R.Y.H.); Department of Radiology, College of Physicians and Surgeons, Columbia University, New York, New York (X.G., M.Z., B.Z.); Center for Neuro-Oncology, Dana-Farber/Brigham and Women's Cancer Center, Boston, Massachusetts (D.A.R., P.Y.W.)
| | - Binsheng Zhao
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts (K.C., B.Z., R.R., D.S., N.W., R.Y.H.); Department of Radiology, College of Physicians and Surgeons, Columbia University, New York, New York (X.G., M.Z., B.Z.); Center for Neuro-Oncology, Dana-Farber/Brigham and Women's Cancer Center, Boston, Massachusetts (D.A.R., P.Y.W.)
| | - Patrick Y Wen
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts (K.C., B.Z., R.R., D.S., N.W., R.Y.H.); Department of Radiology, College of Physicians and Surgeons, Columbia University, New York, New York (X.G., M.Z., B.Z.); Center for Neuro-Oncology, Dana-Farber/Brigham and Women's Cancer Center, Boston, Massachusetts (D.A.R., P.Y.W.)
| | - Raymond Y Huang
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts (K.C., B.Z., R.R., D.S., N.W., R.Y.H.); Department of Radiology, College of Physicians and Surgeons, Columbia University, New York, New York (X.G., M.Z., B.Z.); Center for Neuro-Oncology, Dana-Farber/Brigham and Women's Cancer Center, Boston, Massachusetts (D.A.R., P.Y.W.)
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Zolal A, Juratli TA, Linn J, Podlesek D, Sitoci Ficici KH, Kitzler HH, Schackert G, Sobottka SB, Rieger B, Krex D. Enhancing tumor apparent diffusion coefficient histogram skewness stratifies the postoperative survival in recurrent glioblastoma multiforme patients undergoing salvage surgery. J Neurooncol 2016; 127:551-7. [DOI: 10.1007/s11060-016-2063-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2015] [Accepted: 01/22/2016] [Indexed: 11/30/2022]
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20
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Jeong D, Malalis C, Arrington JA, Field AS, Choi JW, Kocak M. Mean apparent diffusion coefficient values in defining radiotherapy planning target volumes in glioblastoma. Quant Imaging Med Surg 2016; 5:835-45. [PMID: 26807366 DOI: 10.3978/j.issn.2223-4292.2015.12.05] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
BACKGROUND To evaluate mean apparent diffusion coefficient (ADC) values on pre-radiotherapy magnetic resonance (MR) at sites that gave rise to glioblastoma (GBM) recurrence compared to similar surrounding background tissue that did not progress to tumor. METHODS Twenty out of 110 consecutive patients with pathology proven GBM treated at our institution from 1/1/2009 to 5/31/2012 had definitive recurrence 6 months following radiotherapy. In this single-center retrospective cohort study, pre- and post-radiotherapy MR brain exams were evaluated. Sites of tumor recurrence on post-therapy exams were co-localized to pre-therapy exams and the background tissue type which gave rise to tumor was noted (i.e., T2 hyperintensity, normal appearing white or gray matter). Similar surrounding background tissue not progressing to tumor was also selected. Two radiologists compared mean ADC values on pre-radiotherapy MR for sites which gave rise to future tumor recurrence and sites of similar background tissue. RESULTS Pre-radiotherapy mean ADC values were significantly lower in regions of future tumor recurrence than in regions of surrounding background tissue not progressing to tumor (P=0.003). There were no significant quantitative differences on T1-weighted pre contrast (P=0.50) or T2-weighted (P=0.10) sequences between sites. There was strong interobserver agreement with an intraclass correlation of 0.867 for ADC values at sites of future tumor recurrence and background tissue. CONCLUSIONS Mean ADC values may help predict sites of future gross tumor recurrence in GBM, which could be helpful in radiation therapy planning.
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Affiliation(s)
- Daniel Jeong
- 1 Department of Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA ; 2 Department of Diagnostic Radiology, Rush University Medical Center, Chicago, IL, USA ; 3 Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA
| | - Christian Malalis
- 1 Department of Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA ; 2 Department of Diagnostic Radiology, Rush University Medical Center, Chicago, IL, USA ; 3 Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA
| | - John A Arrington
- 1 Department of Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA ; 2 Department of Diagnostic Radiology, Rush University Medical Center, Chicago, IL, USA ; 3 Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA
| | - Aaron S Field
- 1 Department of Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA ; 2 Department of Diagnostic Radiology, Rush University Medical Center, Chicago, IL, USA ; 3 Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA
| | - Jung W Choi
- 1 Department of Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA ; 2 Department of Diagnostic Radiology, Rush University Medical Center, Chicago, IL, USA ; 3 Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA
| | - Mehmet Kocak
- 1 Department of Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA ; 2 Department of Diagnostic Radiology, Rush University Medical Center, Chicago, IL, USA ; 3 Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA
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