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Arevalo-Perez J, Yllera-Contreras E, Peck KK, Hatzoglou V, Yildirim O, Rosenblum MK, Holodny AI. Differentiating Low-Grade from High-Grade Intracranial Ependymomas: Comparison of Dynamic Contrast-Enhanced MRI and Diffusion-Weighted Imaging. AJNR Am J Neuroradiol 2024; 45:927-933. [PMID: 38782589 DOI: 10.3174/ajnr.a8226] [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: 12/19/2023] [Accepted: 02/07/2024] [Indexed: 05/25/2024]
Abstract
BACKGROUND AND PURPOSE The aim of this study was to determine the diagnostic value of fractional plasma volume derived from dynamic contrast-enhanced perfusion MR imaging versus ADC, obtained from DWI in differentiating between grade 2 (low-grade) and grade 3 (high-grade) intracranial ependymomas. MATERIALS AND METHODS A hospital database was created for the period from January 2013 through June 2022, including patients with histologically-proved ependymoma diagnosis with available dynamic contrast-enhanced MR imaging. Both dynamic contrast-enhanced perfusion and DWI were performed on each patient using 1.5T and 3T scanners. Fractional plasma volume maps and ADC maps were calculated. ROIs were defined by a senior neuroradiologist manually by including the enhancing tumor on every section and conforming a VOI to obtain the maximum value of fractional plasma volume (Vpmax) and the minimum value of ADC (ADCmin). A Mann-Whitney U test at a significance level of corrected P = .01 was used to evaluate the differences. Additionally, receiver operating characteristic curve analysis was applied to assess the sensitivity and specificity of Vpmax and ADCmin values. RESULTS A total of 20 patients with ependymomas (10 grade 2 tumors and 10 grade 3 tumors) were included. Vpmax values for grade 3 ependymomas were significantly higher (P < .002) than those for grade 2. ADCmin values were overall lower in high-grade lesions. However, no statistically significant differences were found (P = .12114). CONCLUSIONS As a dynamic contrast-enhanced perfusion MR imaging metric, fractional plasma volume can be used as an indicator to differentiate grade 2 and grade 3 ependymomas. Dynamic contrast-enhanced perfusion MR imaging plays an important role with high diagnostic value in differentiating low- and high-grade ependymoma.
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Affiliation(s)
- Julio Arevalo-Perez
- From the Department of Radiology (J.A.-P., E.Y.-C., V.H., O.Y., A.I.H.), Memorial Sloan Kettering Cancer Center, New York, New York
| | - Elena Yllera-Contreras
- From the Department of Radiology (J.A.-P., E.Y.-C., V.H., O.Y., A.I.H.), Memorial Sloan Kettering Cancer Center, New York, New York
| | - Kyung K Peck
- Department of Medical Physics (K.K.P.), Memorial Sloan Kettering Cancer Center, New York, New York
| | - Vaios Hatzoglou
- From the Department of Radiology (J.A.-P., E.Y.-C., V.H., O.Y., A.I.H.), Memorial Sloan Kettering Cancer Center, New York, New York
| | - Onur Yildirim
- From the Department of Radiology (J.A.-P., E.Y.-C., V.H., O.Y., A.I.H.), Memorial Sloan Kettering Cancer Center, New York, New York
| | - Marc K Rosenblum
- Department of Pathology (M.K.R.), Memorial Sloan Kettering Cancer Center, New York, New York
| | - Andrei I Holodny
- From the Department of Radiology (J.A.-P., E.Y.-C., V.H., O.Y., A.I.H.), Memorial Sloan Kettering Cancer Center, New York, New York
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Arevalo-Perez J, Trang A, Yllera-Contreras E, Yildirim O, Saha A, Young R, Lyo J, Peck KK, Holodny AI. Longitudinal Evaluation of DCE-MRI as an Early Indicator of Progression after Standard Therapy in Glioblastoma. Cancers (Basel) 2024; 16:1839. [PMID: 38791921 PMCID: PMC11119591 DOI: 10.3390/cancers16101839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 05/06/2024] [Accepted: 05/10/2024] [Indexed: 05/26/2024] Open
Abstract
Background and Purpose: Distinguishing treatment-induced imaging changes from progressive disease has important implications for avoiding inappropriate discontinuation of a treatment. Our goal in this study is to evaluate the utility of dynamic contrast-enhanced (DCE) perfusion MRI as a biomarker for the early detection of progression. We hypothesize that DCE-MRI may have the potential as an early predictor for the progression of disease in GBM patients when compared to the current standard of conventional MRI. Methods: We identified 26 patients from 2011 to 2023 with newly diagnosed primary glioblastoma by histopathology and gross or subtotal resection of the tumor. Then, we classified them into two groups: patients with progression of disease (POD) confirmed by pathology or change in chemotherapy and patients with stable disease without evidence of progression or need for therapy change. Finally, at least three DCE-MRI scans were performed prior to POD for the progression cohort, and three consecutive DCE-MRI scans were performed for those with stable disease. The volume of interest (VOI) was delineated by a neuroradiologist to measure the maximum values for Ktrans and plasma volume (Vp). A Friedman test was conducted to evaluate the statistical significance of the parameter changes between scans. Results: The mean interval between subsequent scans was 57.94 days, with POD-1 representing the first scan prior to POD and POD-3 representing the third scan. The normalized maximum Vp values for POD-3, POD-2, and POD-1 are 1.40, 1.86, and 3.24, respectively (FS = 18.00, p = 0.0001). It demonstrates that Vp max values are progressively increasing in the three scans prior to POD when measured by routine MRI scans. The normalized maximum Ktrans values for POD-1, POD-2, and POD-3 are 0.51, 0.09, and 0.51, respectively (FS = 1.13, p < 0.57). Conclusions: Our analysis of the longitudinal scans leading up to POD significantly correlated with increasing plasma volume (Vp). A longitudinal study for tumor perfusion change demonstrated that DCE perfusion could be utilized as an early predictor of tumor progression.
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Affiliation(s)
- Julio Arevalo-Perez
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065, USA; (A.T.); (E.Y.-C.); (O.Y.); (A.S.); (R.Y.); (K.K.P.); (A.I.H.)
- Department of Radiology, Weill Medical College of Cornell University, 525 East 68th Street, New York, NY 10065, USA
| | - Andy Trang
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065, USA; (A.T.); (E.Y.-C.); (O.Y.); (A.S.); (R.Y.); (K.K.P.); (A.I.H.)
| | - Elena Yllera-Contreras
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065, USA; (A.T.); (E.Y.-C.); (O.Y.); (A.S.); (R.Y.); (K.K.P.); (A.I.H.)
| | - Onur Yildirim
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065, USA; (A.T.); (E.Y.-C.); (O.Y.); (A.S.); (R.Y.); (K.K.P.); (A.I.H.)
- Department of Radiology, Weill Medical College of Cornell University, 525 East 68th Street, New York, NY 10065, USA
| | - Atin Saha
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065, USA; (A.T.); (E.Y.-C.); (O.Y.); (A.S.); (R.Y.); (K.K.P.); (A.I.H.)
- Department of Radiology, Weill Medical College of Cornell University, 525 East 68th Street, New York, NY 10065, USA
| | - Robert Young
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065, USA; (A.T.); (E.Y.-C.); (O.Y.); (A.S.); (R.Y.); (K.K.P.); (A.I.H.)
- Department of Radiology, Weill Medical College of Cornell University, 525 East 68th Street, New York, NY 10065, USA
- Brain Tumor Center, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065, USA
| | - John Lyo
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065, USA; (A.T.); (E.Y.-C.); (O.Y.); (A.S.); (R.Y.); (K.K.P.); (A.I.H.)
- Department of Radiology, Weill Medical College of Cornell University, 525 East 68th Street, New York, NY 10065, USA
| | - Kyung K. Peck
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065, USA; (A.T.); (E.Y.-C.); (O.Y.); (A.S.); (R.Y.); (K.K.P.); (A.I.H.)
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065, USA
| | - Andrei I. Holodny
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065, USA; (A.T.); (E.Y.-C.); (O.Y.); (A.S.); (R.Y.); (K.K.P.); (A.I.H.)
- Department of Radiology, Weill Medical College of Cornell University, 525 East 68th Street, New York, NY 10065, USA
- Brain Tumor Center, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065, USA
- Department of Neuroscience, Weill-Cornell Graduate School of the Medical Sciences, 1300 York Ave, New York, NY 10065, USA
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Sheikh SF, Akhuj A, Raghuveer R, Saklecha A. Neurophysiotherapy in Grade II Diffuse Astrocytoma: A Case Report. Cureus 2024; 16:e53082. [PMID: 38414688 PMCID: PMC10897357 DOI: 10.7759/cureus.53082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 01/27/2024] [Indexed: 02/29/2024] Open
Abstract
Diffuse astrocytoma is a slow, progressive, and invasive tumor that develops from astrocytes and there is no discernible boundary between tumor and brain cells. We present a case of a 48-year-old woman with diffuse astrocytoma who experienced sudden left-sided weakness, multiple convulsive episodes, and vomiting. The patient underwent surgery for a left occipital mini craniotomy with complete tumor removal through a titanium burr hole. Postoperatively, the patient complained of bilateral upper and lower extremities weakness, and decreased muscular tone was found; hence, she was referred to undergo neurophysiotherapy. A four-week rehabilitative protocol was started. Physiotherapy is critical in these patients for ensuring early and rapid recovery and treating the condition's clinical manifestations. The outcome measures employed were the tone grading scale, the Brunnstrom recovery stage, and the Functional Independence Measure (FIM). This case study concludes that physiotherapy rehabilitation for an operated case of grade 2 diffuse astrocytoma led to improved lower limb strength, normal tone, and improved functional independence, which helped the patient achieve better functional activities and a greater quality of life.
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Affiliation(s)
- Simran F Sheikh
- Department of Neuro-Physiotherapy, Ravi Nair Physiotherapy College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Aditi Akhuj
- Department of Neuro-Physiotherapy, Ravi Nair Physiotherapy College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Raghumahanti Raghuveer
- Department of Neuro-Physiotherapy, Ravi Nair Physiotherapy College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Akshaya Saklecha
- Department of Neuro-Physiotherapy, Ravi Nair Physiotherapy College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
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Yaltırık Bilgin E, Ünal Ö, Çiledağ N. The relationship of T2 hypointensity and diffusion restriction of brain metastases with the presence and amount of vasogenic edema in MRI. Neuroradiol J 2023; 36:460-463. [PMID: 36598363 PMCID: PMC10588596 DOI: 10.1177/19714009221150847] [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] [Indexed: 01/05/2023] Open
Abstract
AIM Brain metastases are seen in 15-40% of patients diagnosed with cancer. We aimed to search the relationship between the T2 hypointensity, diffusion-weighted imaging characteristics, and the presence and amount of vasogenic edema of brain metastasis in magnetic resonance imaging (MRI). METHODS A total of 292 patients with brain metastasis were included in the study. T2 signals of metastatic lesions, accompanying diffusion restriction and perilesional vasogenic edema findings, were investigated. In metastases accompanied by vasogenic edema, the largest dimension of the vasogenic edema-mass complex on T2-weighted sequences and the largest dimension of the mass in contrast-enhanced T1-weighted series were measured and the edema-mass ratio (EMR) was calculated by comparing these two values. RESULTS The frequency of vasogenic edema was statistically significantly higher in T2 hypointense metastases (89.1% vs 58.8%, χ2 = 18.949, p = <.001) and metastases accompanied by diffusion restriction(81% vs 61.5%, χ2 = 6.971, p = .008). EMR values were found to be statistically significantly higher in T2 hypointense metastases (EMR→ Z = -4.507, p = <.001) and metastases with diffusion restriction(EMR→ Z = -3.819, p = .001). CONCLUSIONS The frequency of vasogenic edema and EMR rates were higher in patients in T2 hypointense metastases and metastases accompanied by diffusion restriction in MRI.
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Affiliation(s)
- Ezel Yaltırık Bilgin
- Department Of Radiology, Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, Ankara, Turkey
| | - Özkan Ünal
- Department Of Radiology, Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, Ankara, Turkey
| | - Nazan Çiledağ
- Department Of Radiology, Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, Ankara, Turkey
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Vats N, Sengupta A, Gupta RK, Patir R, Vaishya S, Ahlawat S, Saini J, Agarwal S, Singh A. Differentiation of Pilocytic Astrocytoma from Glioblastoma using a Machine-Learning framework based upon quantitative T1 perfusion MRI. Magn Reson Imaging 2023; 98:76-82. [PMID: 36572323 DOI: 10.1016/j.mri.2022.12.013] [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: 07/25/2022] [Revised: 12/22/2022] [Accepted: 12/22/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND AND PURPOSE Differentiation of pilocytic astrocytoma (PA) from glioblastoma is difficult using conventional MRI parameters. The purpose of this study was to differentiate these two similar in appearance tumors using quantitative T1 perfusion MRI parameters combined under a machine learning framework. MATERIALS AND METHODS This retrospective study included age/sex and location matched 26 PA and 33 glioblastoma patients with tumor histopathological characterization performed using WHO 2016 classification. Multi-parametric MRI data were acquired at 3 T scanner and included T1 perfusion and DWI data along with conventional MRI images. Analysis of T1 perfusion data using a leaky-tracer-kinetic-model, first-pass-model and piecewise-linear-model resulted in multiple quantitative parameters. ADC maps were also computed from DWI data. Tumors were segmented into sub-components such as enhancing and non-enhancing regions, edema and necrotic/cystic regions using T1 perfusion parameters. Enhancing and non-enhancing regions were combined and used as an ROI. A support-vector-machine classifier was developed for the classification of PA versus glioblastoma using T1 perfusion MRI parameters/features. The feature set was optimized using a random-forest based algorithm. Classification was also performed between the two tumor types using the ADC parameter. RESULTS T1 perfusion parameter values were significantly different between the two groups. The combination of T1 perfusion parameters classified tumors more accurately with a cross validated error of 9.80% against that of ADC's 17.65% error. CONCLUSION The approach of using quantitative T1 perfusion parameters based upon a support-vector-machine classifier reliably differentiated PA from glioblastoma and performed better classification than ADC.
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Affiliation(s)
- Neha Vats
- Centre for Biomedical Engineering, IIT Delhi, New Delhi, India; Clinic for Diagnostic and Interventional Radiology (DIR), Heidelberg University Hospital, Heidelberg, Germany
| | - Anirban Sengupta
- Centre for Biomedical Engineering, IIT Delhi, New Delhi, India; Vanderbilt University Institute of Imaging Science, Nashville, TN 37232, United States; Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Rakesh K Gupta
- Department of Radiology, Fortis Memorial Research Institute, Gurugram, India
| | - Rana Patir
- Department of Neurosurgery, Fortis Memorial Research Institute, Gurugram, India
| | - Sandeep Vaishya
- Department of Neurosurgery, Fortis Memorial Research Institute, Gurugram, India
| | - Sunita Ahlawat
- SRL Diagnostics, Fortis Memorial Research Institute, Gurugram, India
| | - Jitender Saini
- Department of Neuroimaging & Interventional Radiology, National Institute of Mental Health and Neuro Sciences, Bengaluru, India
| | - Sumeet Agarwal
- Department of Electrical Engineering, IIT Delhi, New Delhi, India
| | - Anup Singh
- Centre for Biomedical Engineering, IIT Delhi, New Delhi, India; Department for Biomedical Engineering, AIIMS, Delhi, New Delhi, India.
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Romano A, Palizzi S, Romano A, Moltoni G, Di Napoli A, Maccioni F, Bozzao A. Diffusion Weighted Imaging in Neuro-Oncology: Diagnosis, Post-Treatment Changes, and Advanced Sequences-An Updated Review. Cancers (Basel) 2023; 15:cancers15030618. [PMID: 36765575 PMCID: PMC9913305 DOI: 10.3390/cancers15030618] [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: 12/19/2022] [Revised: 01/15/2023] [Accepted: 01/16/2023] [Indexed: 01/20/2023] Open
Abstract
DWI is an imaging technique commonly used for the assessment of acute ischemia, inflammatory disorders, and CNS neoplasia. It has several benefits since it is a quick, easily replicable sequence that is widely used on many standard scanners. In addition to its normal clinical purpose, DWI offers crucial functional and physiological information regarding brain neoplasia and the surrounding milieu. A narrative review of the literature was conducted based on the PubMed database with the purpose of investigating the potential role of DWI in the neuro-oncology field. A total of 179 articles were included in the study.
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Affiliation(s)
- Andrea Romano
- NESMOS Department, U.O.C. Neuroradiology, “Sant’Andrea” University Hospital, 00189 Rome, Italy
| | - Serena Palizzi
- NESMOS Department, U.O.C. Neuroradiology, “Sant’Andrea” University Hospital, 00189 Rome, Italy
| | - Allegra Romano
- NESMOS Department, U.O.C. Neuroradiology, “Sant’Andrea” University Hospital, 00189 Rome, Italy
| | - Giulia Moltoni
- NESMOS Department, U.O.C. Neuroradiology, “Sant’Andrea” University Hospital, 00189 Rome, Italy
- Correspondence: ; Tel.: +39-3347906958
| | - Alberto Di Napoli
- NESMOS Department, U.O.C. Neuroradiology, “Sant’Andrea” University Hospital, 00189 Rome, Italy
- IRCCS Fondazione Santa Lucia, 00179 Rome, Italy
| | - Francesca Maccioni
- Department of Radiology, Sapienza University of Rome, Viale Regina Elena 324, 00161 Rome, Italy
| | - Alessandro Bozzao
- NESMOS Department, U.O.C. Neuroradiology, “Sant’Andrea” University Hospital, 00189 Rome, Italy
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Bilgin EY, Ünal Ö, Göç MF, Bahsi T. Differences in apparent diffusion coefficient histogram analysis according to EGFR mutation status in brain metastasis due to lung adenocarcinoma. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2023; 31:1035-1045. [PMID: 37424492 DOI: 10.3233/xst-230084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
BACKGROUND The etiology, clinicopathological features, and prognosis of cancer in cases with EGFR mutations are different from those without mutations.OBJECTİVE:This study aims to evaluate the differences in ADC histogram analysis in brain metastases with EGFR mutation status in lung adenocarcinoma cases and the relationship between ADC histogram analysis differences and overall survival. METHODS In this retrospective case-control study, 30 patients (8 EGFR+/22 EGFR-) and 51 brain metastases (15 EGFR+/36 EGFR-) were included. ROI markings are first performed from each section, including metastasis in ADC mapping using FIREVOXEL software. Next, ADC histogram parameters are calculated. Overall survival analysis after brain metastasis (OSBM) is defined as the time from initial brain metastasis diagnosis to the time of death or last follow-up. Patient-based (by evaluating the largest lesion) and lesion-based (by evaluating all measurable lesions) statistical analyses are then performed. RESULTS In the lesion-based analysis, skewness values are lower in EGFR+ patients, which is statistically significant (p = 0.012). The two groups have no significant difference regarding other ADC histogram analysis parameters, mortality, and overall survival (p > 0.05). In the ROC analysis, the most appropriate skewness cut-off value is determined as 0.321 to distinguish the EGFR mutation difference, and this value is statistically significant (sensitivity: 66.7%, specificity: 80.6%, AUC: 0.730) (p = 0.006).CONCLUSİON:The findings of this study provide valuable insights into the differences in ADC histogram analysis according to EGFR mutation status in brain metastases due to lung adenocarcinoma. The identified parameters, especially skewness, are potentially non-invasive biomarkers for predicting mutation status. Incorporating these biomarkers into routine clinical practice may aid treatment decision-making and prognostic assessment for patients. Further validation studies and prospective investigations are warranted to confirm the clinical utility of these findings and establish their potential for personalized therapeutic strategies and patient outcomes.
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Affiliation(s)
- Ezel Yaltırık Bilgin
- Department of Radiology, Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, Ankara, Turkey
| | - Özkan Ünal
- Department of Radiology, Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, Ankara, Turkey
| | - Muhammed Fatih Göç
- Department of Radiology, Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, Ankara, Turkey
| | - Taha Bahsi
- Department of Medical Genetics, Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, Ankara, Turkey
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Li Y, Qin Q, Zhang Y, Cao Y. Noninvasive Determination of the IDH Status of Gliomas Using MRI and MRI-Based Radiomics: Impact on Diagnosis and Prognosis. Curr Oncol 2022; 29:6893-6907. [PMID: 36290819 PMCID: PMC9600456 DOI: 10.3390/curroncol29100542] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 09/18/2022] [Accepted: 09/19/2022] [Indexed: 01/13/2023] Open
Abstract
Gliomas are the most common primary malignant brain tumors in adults. The fifth edition of the WHO Classification of Tumors of the Central Nervous System, published in 2021, provided molecular and practical approaches to CNS tumor taxonomy. Currently, molecular features are essential for differentiating the histological subtypes of gliomas, and recent studies have emphasized the importance of isocitrate dehydrogenase (IDH) mutations in stratifying biologically distinct subgroups of gliomas. IDH plays a significant role in gliomagenesis, and the association of IDH status with prognosis is very clear. Recently, there has been much progress in conventional MR imaging (cMRI), advanced MR imaging (aMRI), and radiomics, which are widely used in the study of gliomas. These advances have resulted in an improved correlation between MR signs and IDH mutation status, which will complement the prediction of the IDH phenotype. Although imaging cannot currently substitute for genetic tests, imaging findings have shown promising signs of diagnosing glioma subtypes and evaluating the efficacy and prognosis of individualized molecular targeted therapy. This review focuses on the correlation between MRI and MRI-based radiomics and IDH gene-phenotype prediction, discussing the value and application of these techniques in the diagnosis and evaluation of the prognosis of gliomas.
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Affiliation(s)
- Yurong Li
- Department of Radiation Oncology, Nanjing Medical University First Affiliated Hospital, Nanjing 210029, China
- The First School of Clinical Medicine, Nanjing Medical University, Nanjing 210029, China
| | - Qin Qin
- Department of Radiation Oncology, Nanjing Medical University First Affiliated Hospital, Nanjing 210029, China
| | - Yumeng Zhang
- Department of Radiation Oncology, Nanjing Medical University First Affiliated Hospital, Nanjing 210029, China
| | - Yuandong Cao
- Department of Radiation Oncology, Nanjing Medical University First Affiliated Hospital, Nanjing 210029, China
- Correspondence:
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Apparent Diffusion Coefficient-Based Radiomic Nomogram in Sinonasal Squamous Cell Carcinoma: A Preliminary Study on Histological Grade Evaluation. J Comput Assist Tomogr 2022; 46:823-829. [PMID: 35675693 DOI: 10.1097/rct.0000000000001329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE The aim of the study was to develop and validate a nomogram model combining radiomic features and clinical characteristics to preoperatively differentiate between low- and high-grade sinonasal squamous cell carcinomas. MATERIAL AND METHODS A total of 174 patients who underwent diffusion-weighted imaging were included in this study. The patients were allocated to the training and testing cohorts randomly at a ratio of 6:4. The least absolute shrinkage and selection operator regression was applied for feature selection and radiomic signature (radscore) construction. Multivariable logistic regression analysis was applied to identify independent predictors. The performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC), the calibration curve, decision curve analysis, and the clinical impact curve. RESULTS The radscore included 9 selected radiomic features. The radscore and clinical stage were independent predictors. The nomogram showed better performance (training cohort: AUC, 0.92; 95% confidence interval, 0.85-0.96; testing cohort: AUC, 0.91; 95% CI, 0.82-0.97) than either the radscore or the clinical stage in both the training and test cohorts (P < 0.050). The nomogram demonstrated good calibration and clinical usefulness. CONCLUSIONS The apparent diffusion coefficient-based radiomic nomogram model could be useful in differentiating between low- and high-grade sinonasal squamous cell carcinomas.
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Effectiveness of ADC histogram analysis in the diagnosis of focal liver lesions; is a contrast agent necessary? MARMARA MEDICAL JOURNAL 2022. [DOI: 10.5472/marumj.1121815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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The Use of 18F-FET-PET-MRI in Neuro-Oncology: The Best of Both Worlds—A Narrative Review. Diagnostics (Basel) 2022; 12:diagnostics12051202. [PMID: 35626357 PMCID: PMC9140561 DOI: 10.3390/diagnostics12051202] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 04/22/2022] [Accepted: 04/28/2022] [Indexed: 02/05/2023] Open
Abstract
Gliomas are the most frequent primary tumors of the brain. They can be divided into grade II-IV astrocytomas and grade II-III oligodendrogliomas, based on their histomolecular profile. The prognosis and treatment is highly dependent on grade and well-identified prognostic and/or predictive molecular markers. Multi-parametric MRI, including diffusion weighted imaging, perfusion, and MR spectroscopy, showed increasing value in the non-invasive characterization of specific molecular subsets of gliomas. Radiolabeled amino-acid analogues, such as 18F-FET, have also been proven valuable in glioma imaging. These tracers not only contribute in the diagnostic process by detecting areas of dedifferentiation in diffuse gliomas, but this technique is also valuable in the follow-up of gliomas, as it can differentiate pseudo-progression from real tumor progression. Since multi-parametric MRI and 18F-FET PET are complementary imaging techniques, there may be a synergistic role for PET-MRI imaging in the neuro-oncological imaging of primary brain tumors. This could be of value for both primary staging, as well as during treatment and follow-up.
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The role of apparent diffusion coefficient as a predictive factor for tumor recurrence in patients with cerebellopontine angle epidermoid tumor. Neurosurg Rev 2021; 45:1383-1392. [PMID: 34581893 DOI: 10.1007/s10143-021-01654-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 09/04/2021] [Accepted: 09/24/2021] [Indexed: 01/03/2023]
Abstract
Intracranial epidermoid tumors are slowly growing benign tumors, but due to adjacent critical neurovascular structures, surgical resection is challenging, with the risk of recurrence. The apparent diffusion coefficient (ADC) has been used to evaluate the characteristics of brain tumors, but its utility for intracranial epidermoid tumors has not been specifically explored. This study analyzed the utility of preoperative ADC values in predicting tumor recurrence for patients with intracranial epidermoid tumors. Between 2008 and 2019, 21 patients underwent surgery for cerebellopontine angle (CPA) epidermoid tumor, and their preoperative ADC data were analyzed. The patients were divided into two groups: the recurrence group, defined by regrowth of the remnant tumor or newly developed mass after gross total resection on magnetic resonance imaging (MRI); and the stable group, defined by the absence of growth or evidence of tumor on MRI. Receiver operating characteristic (ROC) analysis was used to obtain the ADC cutoff values for predicting tumor recurrence. The prognostic value of the ADC was assessed using Kaplan-Meier curves. The minimum ADC values were significantly lower in the recurrence group than in the stable tumor group (P = 0.020). ROC analysis showed that a minimum ADC value lower than 804.5 × 10-6 mm2/s could be used to predict higher recurrence risk of CPA epidermoid tumors. Non-total resection and mean and minimum ADC values lower than the respective cutoffs were negative predictors of recurrence-free survival. Minimum ADC values could be useful in predicting the recurrence of CPA epidermoid tumors.
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Neuroimaging in the Era of the Evolving WHO Classification of Brain Tumors, From the AJR Special Series on Cancer Staging. AJR Am J Roentgenol 2021; 217:3-15. [PMID: 33502214 DOI: 10.2214/ajr.20.25246] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The inclusion of molecular and genetic information with histopathologic information defines the framework for brain tumor classification and grading. This framework is reflected in the major restructuring of the WHO brain tumor classification system in 2016 and in numerous subsequent proposed updates reflecting ongoing developments in understanding the impact of tumor genotype on classification and grading. This incorporation of molecular and genetic features improves tumor diagnosis and prediction of tumor behavior and response to treatment. Neuroimaging is essential for the noninvasive assessment of pretreatment tumor grading and for identification and determination of therapeutic efficacy. Use of conventional neuroimaging and physiologic imaging techniques, such as diffusion- and perfusion-weighted MRI, can increase diagnostic confidence before and after treatment. Although the use of neuroimaging to consistently determine tumor genetics is not yet robust, promising developments are on the horizon. Given the complexity of the brain tumor microenvironment, the development and implementation of a standardized reporting system can aid in conveying to radiologists, referring providers, and patients important information about brain tumor response to treatment. The purpose of this article is to review the current state and role of neuroimaging in this continuously evolving field.
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Guo J, Ren J, Shen J, Cheng R, He Y. Do the combination of multiparametric MRI-based radiomics and selected blood inflammatory markers predict the grade and proliferation in glioma patients? Diagn Interv Radiol 2021; 27:440-449. [PMID: 33769289 PMCID: PMC8136526 DOI: 10.5152/dir.2021.20154] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 08/24/2020] [Accepted: 08/29/2020] [Indexed: 11/22/2022]
Abstract
PURPOSE We aimed to explore whether multiparametric magnetic resonance imaging (MRI)-based radiomics combined with selected blood inflammatory markers could effectively predict the grade and proliferation in glioma patients. METHODS This retrospective study included 152 patients histopathologically diagnosed with glioma. Stratified sampling was used to divide all patients into a training cohort (n=107) and a validation cohort (n=45) according to a ratio of 7:3, and five-fold repeat cross-validation was adopted in the training cohort. Multiparametric MRI and clinical parameters, including age, the neutrophil-lymphocyte ratio and red cell distribution width, were assessed. During image processing, image registration and gray normalization were conducted. A radiomics analysis was performed by extracting 1584 multiparametric MRI-based features, and the least absolute shrinkage and selection operator (LASSO) was applied to generate a radiomics signature for predicting grade and Ki-67 index in both training and validation cohorts. Statistical analysis included analysis of variance, Pearson correlation, intraclass correlation coefficient, multivariate logistic regression, Hosmer-Lemeshow test, and receiver operating characteristic (ROC) curve. RESULTS The radiomics signature demonstrated good performance in both the training and validation cohorts, with areas under the ROC curve (AUCs) of 0.92, 0.91, and 0.94 and 0.94, 0.75, and 0.82 for differentiating between low and high grade gliomas, grade III and grade IV gliomas, and low Ki-67 and high Ki-67, respectively, and was better than the clinical model; the AUCs of the combined model were 0.93, 0.91, and 0.95 and 0.94, 0.76, and 0.80, respectively. CONCLUSION Both the radiomics signature and combined model showed high diagnostic efficacy and outperformed the clinical model. The clinical factors did not provide additional improvement in the prediction of the grade and proliferation index in glioma patients, but the stability was improved.
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Affiliation(s)
| | | | - Junkang Shen
- From the Department of Radiology (J.G., J.S. ), The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China; Department of Radiology (J.G., Y.H.), Shanxi Provincial People’s Hospital, Taiyuan, China; GE Healthcare China (J.R.), Beijing, China; Department of Neurosurgery (R.C.), Shanxi Provincial People’s Hospital, Taiyuan, China
| | - Rui Cheng
- From the Department of Radiology (J.G., J.S. ), The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China; Department of Radiology (J.G., Y.H.), Shanxi Provincial People’s Hospital, Taiyuan, China; GE Healthcare China (J.R.), Beijing, China; Department of Neurosurgery (R.C.), Shanxi Provincial People’s Hospital, Taiyuan, China
| | - Yexin He
- From the Department of Radiology (J.G., J.S. ), The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China; Department of Radiology (J.G., Y.H.), Shanxi Provincial People’s Hospital, Taiyuan, China; GE Healthcare China (J.R.), Beijing, China; Department of Neurosurgery (R.C.), Shanxi Provincial People’s Hospital, Taiyuan, China
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Cao H, Xiao X, Hua J, Huang G, He W, Qin J, Wu Y, Li X. The Added Value of Inflow-Based Vascular-Space-Occupancy and Diffusion-Weighted Imaging in Preoperative Grading of Gliomas. NEURODEGENER DIS 2021; 20:123-130. [PMID: 33735873 DOI: 10.1159/000512545] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Accepted: 10/26/2020] [Indexed: 11/19/2022] Open
Abstract
OBJECTIVES The present study aimed to study whether combined inflow-based vascular-space-occupancy (iVASO) MR imaging (MRI) and diffusion-weighted imaging (DWI) improve the diagnostic accuracy in the preoperative grading of gliomas. METHODS Fifty-one patients with histopathologically confirmed diffuse gliomas underwent preoperative structural MRI, iVASO, and DWI. We performed 2 qualitative consensus reviews: (1) structural MR images alone and (2) structural MR images with iVASO and DWI. Relative arteriolar cerebral blood volume (rCBVa) and minimum apparent diffusion coefficient (mADC) were compared between low-grade and high-grade gliomas. Receiver operating characteristic (ROC) curve analysis was performed to compare the tumor grading efficiency of rCBVa, mADC, and the combination of the two parameters. RESULTS Two observers diagnosed accurate tumor grade in 40 of 51 (78.4%) patients in the first review and in 46 of 51 (90.2%) in the second review. Both rCBVa and mADC showed significant differences between low-grade and high-grade gliomas. ROC analysis gave a threshold value of 1.52 for rCBVa and 0.85 × 10-3 mm2/s for mADC to provide a sensitivity and specificity of 88.0 and 81.2% and 100.0 and 68.7%, respectively. The area under the ROC curve (AUC) was 0.87 and 0.85 for rCBVa and mADC, respectively. The combination of rCBVa and mADC values increased the AUC to 0.92. CONCLUSION The combined application of iVASO and DWI may improve the diagnostic accuracy of glioma grading.
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Affiliation(s)
- Haimei Cao
- Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Xiang Xiao
- Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Jun Hua
- Neurosection, Division of MRI Research, Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Meghalaya, USA.,Department of Radiology, F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Johns Hopkins University School of Medicine, Baltimore, Meghalaya, USA
| | - Guanglong Huang
- Department of Neurosurgery, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Wenle He
- Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Jie Qin
- Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yuankui Wu
- Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, China,
| | - Xiaodan Li
- Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, China
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Sawlani V, Patel MD, Davies N, Flintham R, Wesolowski R, Ughratdar I, Pohl U, Nagaraju S, Petrik V, Kay A, Jacob S, Sanghera P, Wykes V, Watts C, Poptani H. Multiparametric MRI: practical approach and pictorial review of a useful tool in the evaluation of brain tumours and tumour-like lesions. Insights Imaging 2020; 11:84. [PMID: 32681296 PMCID: PMC7367972 DOI: 10.1186/s13244-020-00888-1] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 06/24/2020] [Indexed: 12/17/2022] Open
Abstract
MRI has a vital role in the assessment of intracranial lesions. Conventional MRI has limited specificity and multiparametric MRI using diffusion-weighted imaging, perfusion-weighted imaging and magnetic resonance spectroscopy allows more accurate assessment of the tissue microenvironment. The purpose of this educational pictorial review is to demonstrate the role of multiparametric MRI for diagnosis, treatment planning and for assessing treatment response, as well as providing a practical approach for performing and interpreting multiparametric MRI in the clinical setting. A variety of cases are presented to demonstrate how multiparametric MRI can help differentiate neoplastic from non-neoplastic lesions compared to conventional MRI alone.
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Affiliation(s)
- Vijay Sawlani
- University Hospitals Birmingham NHS Foundation Trust, Queen Elizabeth Hospital Birmingham, Mindelsohn Way, Edgbaston, Birmingham, B15 2TH, UK.
- University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
| | - Markand Dipankumar Patel
- University Hospitals Birmingham NHS Foundation Trust, Queen Elizabeth Hospital Birmingham, Mindelsohn Way, Edgbaston, Birmingham, B15 2TH, UK
- University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Nigel Davies
- University Hospitals Birmingham NHS Foundation Trust, Queen Elizabeth Hospital Birmingham, Mindelsohn Way, Edgbaston, Birmingham, B15 2TH, UK
| | - Robert Flintham
- University Hospitals Birmingham NHS Foundation Trust, Queen Elizabeth Hospital Birmingham, Mindelsohn Way, Edgbaston, Birmingham, B15 2TH, UK
| | - Roman Wesolowski
- University Hospitals Birmingham NHS Foundation Trust, Queen Elizabeth Hospital Birmingham, Mindelsohn Way, Edgbaston, Birmingham, B15 2TH, UK
| | - Ismail Ughratdar
- University Hospitals Birmingham NHS Foundation Trust, Queen Elizabeth Hospital Birmingham, Mindelsohn Way, Edgbaston, Birmingham, B15 2TH, UK
| | - Ute Pohl
- University Hospitals Birmingham NHS Foundation Trust, Queen Elizabeth Hospital Birmingham, Mindelsohn Way, Edgbaston, Birmingham, B15 2TH, UK
| | - Santhosh Nagaraju
- University Hospitals Birmingham NHS Foundation Trust, Queen Elizabeth Hospital Birmingham, Mindelsohn Way, Edgbaston, Birmingham, B15 2TH, UK
| | - Vladimir Petrik
- University Hospitals Birmingham NHS Foundation Trust, Queen Elizabeth Hospital Birmingham, Mindelsohn Way, Edgbaston, Birmingham, B15 2TH, UK
| | - Andrew Kay
- University Hospitals Birmingham NHS Foundation Trust, Queen Elizabeth Hospital Birmingham, Mindelsohn Way, Edgbaston, Birmingham, B15 2TH, UK
| | - Saiju Jacob
- University Hospitals Birmingham NHS Foundation Trust, Queen Elizabeth Hospital Birmingham, Mindelsohn Way, Edgbaston, Birmingham, B15 2TH, UK
- University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Paul Sanghera
- University Hospitals Birmingham NHS Foundation Trust, Queen Elizabeth Hospital Birmingham, Mindelsohn Way, Edgbaston, Birmingham, B15 2TH, UK
| | - Victoria Wykes
- University Hospitals Birmingham NHS Foundation Trust, Queen Elizabeth Hospital Birmingham, Mindelsohn Way, Edgbaston, Birmingham, B15 2TH, UK
- University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Colin Watts
- University Hospitals Birmingham NHS Foundation Trust, Queen Elizabeth Hospital Birmingham, Mindelsohn Way, Edgbaston, Birmingham, B15 2TH, UK
- University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Harish Poptani
- Centre for Pre-Clinical Imaging, Department of Cellular and Molecular Physiology, University of Liverpool, Crown Street, Liverpool, L69 3BX, UK
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Park YW, Choi YS, Ahn SS, Chang JH, Kim SH, Lee SK. Radiomics MRI Phenotyping with Machine Learning to Predict the Grade of Lower-Grade Gliomas: A Study Focused on Nonenhancing Tumors. Korean J Radiol 2020; 20:1381-1389. [PMID: 31464116 PMCID: PMC6715562 DOI: 10.3348/kjr.2018.0814] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Accepted: 04/21/2019] [Indexed: 12/28/2022] Open
Abstract
Objective To assess whether radiomics features derived from multiparametric MRI can predict the tumor grade of lower-grade gliomas (LGGs; World Health Organization grade II and grade III) and the nonenhancing LGG subgroup. Materials and Methods Two-hundred four patients with LGGs from our institutional cohort were allocated to training (n = 136) and test (n = 68) sets. Postcontrast T1-weighted images, T2-weighted images, and fluid-attenuated inversion recovery images were analyzed to extract 250 radiomics features. Various machine learning classifiers were trained using the radiomics features to predict the glioma grade. The trained classifiers were internally validated on the institutional test set and externally validated on a separate cohort (n = 99) from The Cancer Genome Atlas (TCGA). Classifier performance was assessed by determining the area under the curve (AUC) from receiver operating characteristic curve analysis. An identical process was performed in the nonenhancing LGG subgroup (institutional training set, n = 73; institutional test set, n = 37; and TCGA cohort, n = 37) to predict the glioma grade. Results The performance of the best classifier was good in the internal validation set (AUC, 0.85) and fair in the external validation set (AUC, 0.72) to predict the LGG grade. For the nonenhancing LGG subgroup, the performance of the best classifier was good in the internal validation set (AUC, 0.82), but poor in the external validation set (AUC, 0.68). Conclusion Radiomics feature-based classifiers may be useful to predict LGG grades. However, radiomics classifiers may have a limited value when applied to the nonenhancing LGG subgroup in a TCGA cohort.
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Affiliation(s)
- Yae Won Park
- Department of Radiology, Ewha Womans University College of Medicine, Seoul, Korea.,Department of Radiology and Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Korea
| | - Yoon Seong Choi
- Department of Radiology and Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Korea.
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Korea
| | - Jong Hee Chang
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea
| | - Se Hoon Kim
- Department of Pathology, Yonsei University College of Medicine, Seoul, Korea
| | - Seung Koo Lee
- Department of Radiology and Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Korea
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Differentiation of high-grade and low-grade intra-axial brain tumors by time-dependent diffusion MRI. Magn Reson Imaging 2020; 72:34-41. [PMID: 32599021 DOI: 10.1016/j.mri.2020.06.018] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 04/27/2020] [Accepted: 06/24/2020] [Indexed: 12/11/2022]
Abstract
INTRODUCTION Oscillating gradient spin-echo (OGSE) sequences enable acquisitions with shorter diffusion times. There is growing interest in the effect of diffusion time on apparent diffusion coefficient (ADC) values in patients with cancer. However, little evidence exists regarding its usefulness for differentiating between high-grade and low-grade brain tumors. The purpose of this study is to investigate the utility of changes in the ADC value between short and long diffusion times in distinguishing low-grade and high-grade brain tumors. MATERIAL AND METHODS Eleven patients with high-grade brain tumors and ten patients with low-grade brain tumors were scanned using a 3 T magnetic resonance imaging with diffusion-weighted imaging (DWI) using OGSE and PGSE (effective diffusion time [Δeff]: 6.5 ms and 35.2 ms) and b-values of 0 and 1000 s/mm2. Using a region of interest (ROI) analysis of the brain tumors, we measured the ADC for two Δeff (ADCΔeff) values and computed the subtraction ADC (ΔADC = ADC6.5 ms - ADC35.2 ms) and the relative ADC (ΔADC = (ADC6.5 ms - ADC35.2 ms) / ADC35.2 ms × 100). The maximum values for the subtraction ADC (ΔADCmax) and the relative ADC (rADCmax) on the ROI were compared between low-grade and high-grade tumors using the Wilcoxon rank-sum test. A P-value <.05 was considered significant. The ROIs were also placed in the normal white matter of patients with high- and low-grade brain tumors, and ΔADCmax values were determined. RESULTS High-grade tumors had significantly higher ΔADCmax and rADCmax than low-grade tumors. The ΔADCmax values of the normal white matter were lower than the ΔADCmax of high- and low-grade brain tumors. CONCLUSION The dependence of ADC values on diffusion time between 6.5 ms and 35.2 ms was stronger in high-grade tumors than in low-grade tumors, suggesting differences in internal tissue structure. This finding highlights the importance of reporting diffusion times in ADC evaluations and might contribute to the grading of brain tumors using DWI.
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Differentiation of high-grade from low-grade diffuse gliomas using diffusion-weighted imaging: a comparative study of mono-, bi-, and stretched-exponential diffusion models. Neuroradiology 2020; 62:815-823. [PMID: 32424712 PMCID: PMC7311374 DOI: 10.1007/s00234-020-02456-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2020] [Accepted: 05/05/2020] [Indexed: 12/13/2022]
Abstract
Purpose Diffusion-weighted imaging (DWI) plays an important role in the preoperative assessment of gliomas; however, the diagnostic performance of histogram-derived parameters from mono-, bi-, and stretched-exponential DWI models in the grading of gliomas has not been fully investigated. Therefore, we compared these models’ ability to differentiate between high-grade and low-grade gliomas. Methods This retrospective study included 22 patients with diffuse gliomas (age, 23–74 years; 12 males; 11 high-grade and 11 low-grade gliomas) who underwent preoperative 3 T-magnetic resonance imaging from October 2014 to August 2019. The apparent diffusion coefficient was calculated from the mono-exponential model. Using 13 b-values, the true-diffusion coefficient, pseudo-diffusion coefficient, and perfusion fraction were obtained from the bi-exponential model, and the distributed-diffusion coefficient and heterogeneity index were obtained from the stretched-exponential model. Region-of-interests were drawn on each imaging parameter map for subsequent histogram analyses. Results The skewness of the apparent diffusion, true-diffusion, and distributed-diffusion coefficients was significantly higher in high-grade than in low-grade gliomas (0.67 ± 0.67 vs. − 0.18 ± 0.63, 0.68 ± 0.74 vs. − 0.08 ± 0.66, 0.63 ± 0.72 vs. − 0.15 ± 0.73; P = 0.0066, 0.0192, and 0.0128, respectively). The 10th percentile of the heterogeneity index was significantly lower (0.77 ± 0.08 vs. 0.88 ± 0.04; P = 0.0004), and the 90th percentile of the perfusion fraction was significantly higher (12.64 ± 3.44 vs. 7.14 ± 1.70%: P < 0.0001), in high-grade than in low-grade gliomas. The combination of the 10th percentile of the true-diffusion coefficient and 90th percentile of the perfusion fraction showed the best area under the receiver operating characteristic curve (0.96). Conclusion The bi-exponential model exhibited the best diagnostic performance for differentiating high-grade from low-grade gliomas. Electronic supplementary material The online version of this article (10.1007/s00234-020-02456-2) contains supplementary material, which is available to authorized users.
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Correlation analysis of apparent diffusion coefficient value and P53 and Ki-67 expression in esophageal squamous cell carcinoma. Magn Reson Imaging 2020; 68:183-189. [DOI: 10.1016/j.mri.2020.01.011] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Revised: 01/21/2020] [Accepted: 01/22/2020] [Indexed: 12/20/2022]
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Wang QP, Lei DQ, Yuan Y, Xiong NX. Accuracy of ADC derived from DWI for differentiating high-grade from low-grade gliomas: Systematic review and meta-analysis. Medicine (Baltimore) 2020; 99:e19254. [PMID: 32080132 PMCID: PMC7034741 DOI: 10.1097/md.0000000000019254] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
OBJECTIVE Quantitative apparent diffusion coefficient (ADC) values of diffusion weighted imaging (DWI) could be applied to grade gliomas. This meta-analysis was conducted to assess the accuracy of ADC analysis in differentiating high-grade (HGGs) from low-grade gliomas (LGGs). METHODS PubMed, Cochrane library, Science Direct, and Embase were searched to identify suitable studies up to September 1, 2018. The quality of studies was evaluated by the quality assessment of diagnostic accuracy studies (QUADAS 2). We estimated the pooled sensitivity, specificity, positive and negative likelihood ratios (LR), diagnostic accuracy ratio (DOR) with 95% confidence intervals (CI), and determined the accuracy of the data by using the summary receiver operating characteristic (SROC) and calculating the area under the curve (AUC) to identity the accuracy of ADC analysis in grading gliomas. RESULTS Eighteen studies including 1172 patients were included and analyzed. The pooled sensitivity, specificity, PLR, NLR, DOR, and AUC with 95% CIs of DWI with b values of 1000 s/mm for separating HGGs from LGGs were 0.81 (95% CI 0.75-0.86), 0.87 (95% CI 0.81-0.91), 6.1 (95% CI 4.2-8.9), 0.22 (95% CI 0.17-0.29), 28 (95% CI 17-45), and 0.91 (95% CI 0.88-0.93), respectively. DWI with b values of 3000 s/mm showed slightly higher accuracy than that of 1000 (sensitivity 0.80, specificity 0.90 and AUC 0.92). Meta-regression analyses showed that field strengths and b values had significant impacts on diagnostic efficacy. Deeks testing confirmed no significant publication bias in all studies. CONCLUSIONS This meta-analysis suggested that ADC analysis of DWI have high accuracy in differentiating HGGs from LGGs. Standardized methodology is warranted to guide the use of this technique for clinical decision-making.
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Phuttharak W, Thammaroj J, Wara-Asawapati S, Panpeng K. Grading Gliomas Capability: Comparison between Visual Assessment and Apparent Diffusion Coefficient (ADC) Value Measurement on Diffusion-Weighted Imaging (DWI). Asian Pac J Cancer Prev 2020; 21:385-390. [PMID: 32102515 PMCID: PMC7332154 DOI: 10.31557/apjcp.2020.21.2.385] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Indexed: 11/25/2022] Open
Abstract
Background: To compare diagnostic accuracy between DWI visual scale assessment and ADC value measurement of solid portion of the tumor in grading gliomas. Methods: This retrospective study included 38 patients who had pathologically proven gliomas between January 2013 and August 2018 with 18 low grade and 20 high grade tumors. All patients underwent MRI and biopsy. Two readers reviewed DWI visual scale independently. Disagreement was resolved by consensus. One reviewer measured ADC value of entire solid part of the tumor in single axial slice with greatest dimension of tumor which was chosen by consensus. Two data sets of visual scale and ADC value were analyzed and comparison of diagnostic accuracy in glioma grading was done by using area under the curve (AUC) of receiver operating characteristic curve (ROC). Results: Visual scale and ADC value could be used to distinguish between low and high grade gliomas with a statistically significant difference. (P-value 0.002 and <0.001). Almost all high grade gliomas had visual scale 5. The sensitivity, specificity, PPV NPV and accuracy were 50%, 100%, 100% , 64.3%,73.68% respectively. The cutoff level for the ADC value was determined to be 1119.48 x10-6 mm2/s in differentiation between low and high grade gliomas with the sensitivity, specificity, PPV, NPV, accuracy of 90%, 88.89% , 90%, 88.9% and 89.47% respectively. There was no statistically significant difference(P-value = 0.163). Conclusion: Both Visual scale and ADC value were capable of differentiating between low and high grade gliomas. Although visual scale may not replace ADC measurement, larger scale prospective study is needed for validate this initial result.
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Affiliation(s)
- Warinthorn Phuttharak
- Department of Radiology,Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
| | - Jureerat Thammaroj
- Department of Radiology,Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
| | - Sakda Wara-Asawapati
- Department of Pathology, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
| | - Kobporn Panpeng
- Department of Radiology,Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
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Abstract
Non-invasive magnetic resonance imaging (MRI) techniques are increasingly applied in the clinic with a fast growing body of evidence regarding its value for clinical decision making. In contrast to biochemical or histological markers, the key advantages of imaging biomarkers are the non-invasive nature and the spatial and temporal resolution of these approaches. The following chapter focuses on clinical applications of novel MR biomarkers in humans with a strong focus on oncologic diseases. These include both clinically established biomarkers (part 1-4) and novel MRI techniques that recently demonstrated high potential for clinical utility (part 5-7).
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Affiliation(s)
- Daniel Paech
- Division of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
| | - Heinz-Peter Schlemmer
- Division of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.
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Li SH, Jiang RF, Zhang J, Su CL, Chen XW, Zhang JX, Jiang JJ, Zhu WZ. Application of Neurite Orientation Dispersion and Density Imaging in Assessing Glioma Grades and Cellular Proliferation. World Neurosurg 2019; 131:e247-e254. [DOI: 10.1016/j.wneu.2019.07.121] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Accepted: 07/15/2019] [Indexed: 11/24/2022]
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Almasian M, Wilk LS, Bloemen PR, van Leeuwen TG, ter Laan M, Aalders MCG. Pilot feasibility study of in vivo intraoperative quantitative optical coherence tomography of human brain tissue during glioma resection. JOURNAL OF BIOPHOTONICS 2019; 12:e201900037. [PMID: 31245913 PMCID: PMC7065626 DOI: 10.1002/jbio.201900037] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Revised: 06/21/2019] [Accepted: 06/23/2019] [Indexed: 05/21/2023]
Abstract
This study investigates the feasibility of in vivo quantitative optical coherence tomography (OCT) of human brain tissue during glioma resection surgery in six patients. High-resolution detection of glioma tissue may allow precise and thorough tumor resection while preserving functional brain areas, and improving overall survival. In this study, in vivo 3D OCT datasets were collected during standard surgical procedure, before and after partial resection of the tumor, both from glioma tissue and normal parenchyma. Subsequently, the attenuation coefficient was extracted from the OCT datasets using an automated and validated algorithm. The cortical measurements yield a mean attenuation coefficient of 3.8 ± 1.2 mm-1 for normal brain tissue and 3.6 ± 1.1 mm-1 for glioma tissue. The subcortical measurements yield a mean attenuation coefficient of 5.7 ± 2.1 and 4.5 ± 1.6 mm-1 for, respectively, normal brain tissue and glioma. Although the results are inconclusive with respect to trends in attenuation coefficient between normal and glioma tissue due to the small sample size, the results are in the range of previously reported values. Therefore, we conclude that the proposed method for quantitative in vivo OCT of human brain tissue is feasible during glioma resection surgery.
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Affiliation(s)
- Mitra Almasian
- Department of Biomedical Engineering & PhysicsAmsterdam UMC, University of Amsterdam, Amsterdam Cardiovascular Sciences, Cancer Center AmsterdamAmsterdamThe Netherlands
| | - Leah S. Wilk
- Department of Biomedical Engineering & PhysicsAmsterdam UMC, University of Amsterdam, Amsterdam Cardiovascular Sciences, Cancer Center AmsterdamAmsterdamThe Netherlands
| | - Paul R. Bloemen
- Department of Biomedical Engineering & PhysicsAmsterdam UMC, University of Amsterdam, Amsterdam Cardiovascular Sciences, Cancer Center AmsterdamAmsterdamThe Netherlands
| | - Ton G van Leeuwen
- Department of Biomedical Engineering & PhysicsAmsterdam UMC, University of Amsterdam, Amsterdam Cardiovascular Sciences, Cancer Center AmsterdamAmsterdamThe Netherlands
| | - Mark ter Laan
- Department of NeurosurgeryRadboud University Medical CenterNijmegenthe Netherlands
| | - Maurice C. G. Aalders
- Department of Biomedical Engineering & PhysicsAmsterdam UMC, University of Amsterdam, Amsterdam Cardiovascular Sciences, Cancer Center AmsterdamAmsterdamThe Netherlands
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Qin J, Zhang H, Wang X, Tan Y, Wu X. Combination value of diffusion‑weighted imaging and dynamic susceptibility contrast‑enhanced MRI in astrocytoma grading and correlation with GFAP, Topoisomerase IIα and MGMT. Oncol Lett 2019; 18:2763-2770. [PMID: 31452754 PMCID: PMC6704283 DOI: 10.3892/ol.2019.10656] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2016] [Accepted: 01/19/2017] [Indexed: 11/06/2022] Open
Abstract
The present study aimed to investigate the value of diffusion-weighted imaging (DWI) combined with dynamic susceptibility contrast-enhanced (DSC) magnetic resonance imaging (MRI) scans in astrocytoma grading, and correlated MRI scan parameters of values of apparent diffusion coefficient (ADC) and relative cereberal blood volume (rCBV) with the immunohistochemical (IHC) indices of glial fibrillary acidic protein (GFAP), topoisomerase IIα (Topo IIα) and O 6-methylguanine-DNA methyltransferase (MGMT). A total of 123 patients with pathologically confirmed astrocytomas of differing grades underwent DWI and DSC scans. The values of the ADC and relative cerebral blood volume rCBV were compared with the grade II–IV astrocytomas. Receiver operating characteristic curves were used to compare astrocytoma grading efficiency of ADC, rCBV and the combination of the two values. The parameters of ADC and rCBV with GFAP, Topo IIα and MGMT indices were then correlated. The differences in ADC values were significant between the grades II, III and IV astrocytomas, and the rCBV values for grades II, III and IV were also significant. The combination of DWI and DSC demonstrated the highest values for area under curve in identifying grades II and III, and identifying grades III and IV, respectively. GFAP displayed a positive correlation with ADC and a negative correlation with rCBV. Topo IIα exhibited a negative correlation with ADC, and a positive correlation with rCBV. No correlation was observed between MGMT and ADC or rCBV. The combined application of DWI and DSC may increase astrocytoma grading accuracy. Values of ADC and rCBV exhibit certain correlations with IHC indices, and may predict degree of malignancy of astrocytoma.
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Affiliation(s)
- Jiang‑Bo Qin
- Department of Radiology, The First Clinical Medical College of Shanxi Medical University, Taiyuan, Shanxi 030001, P.R. China
| | - Hui Zhang
- Department of Radiology, The First Clinical Medical College of Shanxi Medical University, Taiyuan, Shanxi 030001, P.R. China
| | - Xiao‑Chun Wang
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, Shanxi 030001, P.R. China
| | - Yan Tan
- Department of Radiology, The First Clinical Medical College of Shanxi Medical University, Taiyuan, Shanxi 030001, P.R. China
| | - Xiao‑Feng Wu
- Department of Radiology, The First Clinical Medical College of Shanxi Medical University, Taiyuan, Shanxi 030001, P.R. China
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MR imaging phenotype correlates with extent of genome-wide copy number abundance in IDH mutant gliomas. Neuroradiology 2019; 61:1023-1031. [PMID: 31134296 DOI: 10.1007/s00234-019-02219-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2019] [Accepted: 04/29/2019] [Indexed: 12/15/2022]
Abstract
PURPOSE There is variability in survival within IDH mutant gliomas determined by chromosomal events. Copy number variation (CNV) abundance associated with survival in low-grade and IDH mutant astrocytoma has been reported. Our purpose was to correlate the extent of genome-wide CNV abundance in IDH mutant astrocytomas with MRI features. METHODS Presurgical MRI and CNV plots derived from Illumina 850k EPIC DNA methylation arrays of 18 cases of WHO grade II-IV IDH mutant astrocytomas were reviewed. IDH mutant astrocytomas were divided into CNV stable group (CNV-S) with ≤ 3 chromosomal gains or losses and lack of focal gene amplifications and CNV unstable group (CNV-U) with > 3 large chromosomal gains/losses and/or focal amplifications. The associations between MR features, relative cerebral blood volume (rCBV), CNV abundance, and time to progression were assessed. Tumor rCBV estimates were obtained using DSC T2* perfusion analysis. RESULTS There were nine (50%) CNV-S and nine (50%) CNV-U IDH mutant astrocytomas. CNV-U tumors showed larger mean tumor size (P = 0.004) and maximum diameter on FLAIR (P = 0.004) and also demonstrated significantly higher median rCBV than CNV-S tumors (2.62 vs 0.78, P = 0.019). CNV-U tumors tended to have shorter time to progression although without statistical significance (P = 0.393). CONCLUSIONS Larger size/diameter and higher rCBVs were seen associated CNV-U astrocytomas, suggesting a correlation of aggressive imaging phenotype with unstable and aggressive genotype in IDH mutant astrocytomas.
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Chen H, Hu W, He H, Yang Y, Wen G, Lv X. Noninvasive assessment of H3 K27M mutational status in diffuse midline gliomas by using apparent diffusion coefficient measurements. Eur J Radiol 2019; 114:152-159. [DOI: 10.1016/j.ejrad.2019.03.006] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2018] [Revised: 03/06/2019] [Accepted: 03/13/2019] [Indexed: 12/21/2022]
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Sengupta A, Ramaniharan AK, Gupta RK, Agarwal S, Singh A. Glioma grading using a machine-learning framework based on optimized features obtained from T 1 perfusion MRI and volumes of tumor components. J Magn Reson Imaging 2019; 50:1295-1306. [PMID: 30895704 DOI: 10.1002/jmri.26704] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Revised: 02/16/2019] [Accepted: 02/19/2019] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Glioma grading between intermediate grades (Grade II vs. III and Grade III vs. IV) as well as multiclass grades (Grade II vs. III vs. IV) is challenging and needs to be addressed. PURPOSE To develop an artificial intelligence-based methodology for glioma grading using T1 perfusion parameters and volume of tumor components, and validate the efficacy of the methodology by grading on a cohort of glioma patients. STUDY TYPE Retrospective. POPULATION The development set consisted of 53 glioma patients and validation consisted of 13 glioma patients. FIELD STRENGTH/SEQUENCE Conventional MRI images (2D T1 -W, dual PD-T2 -W, and 3D FLAIR) and 3D T1 perfusion MRI data obtained at 3 T. ASSESSMENT Enhancing and nonenhancing components of glioma were segmented out and combined to form the region of interest (ROI) for glioma grading. Prominent vessels were removed from the selected ROI. Different T1 perfusion parameters from the ROI were combined with volume of tumor components to form the feature set for glioma grading. Optimization was carried out for selection of the statistic of the T1 perfusion parameters and the features to be used for glioma grading using sequential feature selection and random forest-based feature selection method. An optimized support vector machine (SVM) classifier was used for glioma grading. STATISTICAL TESTS Mean ± SD, analysis of variance (ANOVA) followed by the Tukey-Kramer test, ROC analysis. RESULTS Classification error for Grade II vs. III was 3.7%, for Grade III vs. IV was 5.26%, and for Grade II vs. III vs. IV was 9.43% using the proposed methodology. The mean of the values above the 90th percentile value of T1 perfusion parameters provided a maximum area under the curve (AUC) for intermediate grade differentiation. Random forest obtained optimal feature set provided better grading results than other methods using the SVM classifier. DATA CONCLUSION It was feasible to achieve low classification error for intermediate as well as multiclass glioma grading using an SVM classifier based on optimized features obtained from T1 perfusion MRI and volumes of tumor components. LEVEL OF EVIDENCE 4 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;50:1295-1306.
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Affiliation(s)
- Anirban Sengupta
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India
| | | | - Rakesh K Gupta
- Department of Radiology, Fortis Memorial Research Institute, Gurgaon, India
| | - Sumeet Agarwal
- Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, India
| | - Anup Singh
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India.,Department for Biomedical Engineering, AIIMS Delhi, New Delhi, India
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Song B, Wang H, Chen Y, Liu W, Wei R, Ding Y. Efficacy of apparent diffusion coefficient in predicting aggressive histological features of papillary thyroid carcinoma. ACTA ACUST UNITED AC 2019; 24:348-356. [PMID: 30373722 DOI: 10.5152/dir.2018.18130] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
PURPOSE We aimed to evaluate preoperative diffusion-weighted magnetic resonance imaging (DWI) for predicting aggressive histological features in papillary thyroid cancer (PTC). METHODS This prospective study included 141 PTC patients, who underwent DWI prior to thyroidectomy; 88 patients with 88 PTC lesions were finally analyzed. Multiple comparisons of mean and minimum apparent diffusion coefficient (ADC) values (ADCmean and ADCmin) and ADC of the solid component (ADCsolid) between the lowly aggressive PTC, highly aggressive PTC without hobnail, and hobnail variant PTC groups were performed by one-way ANOVA or the Welch test. The nonparametric Kruskal-Wallis H-test was used to assess lesion size differences. Receiver-operating characteristic (ROC) curve analysis was also performed. RESULTS ADC values in the lowly aggressive PTC group were found to be significantly higher than those in the highly aggressive PTC without hobnail group (ADCmean: 1.35±0.20×10-3 mm2/s vs. 1.16±0.17×10-3 mm2/s, P = 0.003; ADCmin: 1.10±0.17×10-3 mm2/s vs. 0.88±0.16×10-3 mm2/s, P < 0.001; ADCsolid: 1.26±0.23×10-3 mm2/s vs. 1.04±0.17×10-3 mm2/s, P < 0.001). No significant differences for the ADCmean, ADCmin, and ADCsolid were observed between the lowly aggressive and hobnail variant PTC groups (all P > 0.05). Lesion sizes in the hobnail variant PTC group was significantly elevated compared with the lowly aggressive PTC group (2.19±1.21 cm vs. 0.93±0.37 cm, P < 0.001). Areas under the curves (AUCs) for ADCmean, ADCmin, and ADCsolid between the lowly aggressive PTC and highly aggressive PTC group without hobnail were 0.758, 0.851, and 0.787, respectively. The AUC for size between the lowly aggressive and hobnail variant PTC group was 0.896. CONCLUSION ADCmin from DWI could potentially provide quantitative information to differentiate lowly aggressive PTC from highly aggressive PTC lesions without hobnail variants.
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Affiliation(s)
- Bin Song
- Department of Radiology Minhang Branch, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Hao Wang
- Department of Radiology Minhang Branch, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yongqi Chen
- Department of Pathology Minhang Branch, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Weiyan Liu
- Department of General Surgery, Minhang Branch, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Ran Wei
- Department of Radiology Minhang Branch, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yi Ding
- Department of Radiology Minhang Branch, Zhongshan Hospital, Fudan University, Shanghai, China
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Hales PW, d'Arco F, Cooper J, Pfeuffer J, Hargrave D, Mankad K, Clark C. Arterial spin labelling and diffusion-weighted imaging in paediatric brain tumours. NEUROIMAGE-CLINICAL 2019; 22:101696. [PMID: 30735859 PMCID: PMC6365981 DOI: 10.1016/j.nicl.2019.101696] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2018] [Revised: 01/16/2019] [Accepted: 01/27/2019] [Indexed: 01/27/2023]
Abstract
BACKGROUND Diffusion- and perfusion-weighted MRI are valuable tools for measuring the cellular and vascular properties of brain tumours. This has been well studied in adult patients, however, the biological features of childhood brain tumours are unique, and paediatric-focused studies are less common. We aimed to assess the diagnostic utility of apparent diffusion coefficient (ADC) values derived from diffusion-weighted imaging (DWI) and cerebral blood flow (CBF) values derived from arterial spin labelling (ASL) in paediatric brain tumours. METHODS We performed a meta-analysis of published studies reporting ADC and ASL-derived CBF values in paediatric brain tumours. Data were combined using a random effects model in order to define typical parameter ranges for different histological tumour subtypes and WHO grades. New data were also acquired in a 'validation cohort' at our institution, in which ADC and CBF values in treatment naïve paediatric brain tumour patients were measured, in order to test the validity of the findings from the literature in an un-seen cohort. ADC and CBF quantification was performed by two radiologists via manual placement of tumour regions of interest (ROIs), in addition to an automated approach to tumour ROI placement. RESULTS A total of 14 studies met the inclusion criteria for the meta-analysis, constituting data acquired in 542 paediatric patients. Parameters of interest were based on measurements from ROIs placed within the tumour, including mean and minimum ADC values (ADCROI-mean, ADCROI-min) and the maximum CBF value normalised to grey matter (nCBFROI-max). After combination of the literature data, a number of histological tumour subtype groups showed significant differences in ADC values, which were confirmed, where possible, in our validation cohort of 32 patients. In both the meta-analysis and our cohort, diffuse midline glioma was found to be an outlier among high-grade tumour subtypes, with ADC and CBF values more similar to the low-grade tumours. After grouping patients by WHO grade, significant differences in grade groups were found in ADCROI-mean, ADCROI-min, and nCBFROI-max, in both the meta-analysis and our validation cohort. After excluding diffuse midline glioma, optimum thresholds (derived from ROC analysis) for separating low/high-grade tumours were 0.95 × 10-3 mm2/s (ADCROI-mean), 0.82 × 10-3 mm2/s (ADCROI-min) and 1.45 (nCBFROI-max). These thresholds were able to identify low/high-grade tumours with 96%, 83%, and 83% accuracy respectively in our validation cohort, and agreed well with the results from the meta-analysis. Diagnostic power was improved by combining ADC and CBF measurements from the same tumour, after which 100% of tumours in our cohort were correctly classified as either low- or high-grade (excluding diffuse midline glioma). CONCLUSION ADC and CBF values are useful for differentiating certain histological subtypes, and separating low- and high-grade paediatric brain tumours. The threshold values presented here are in agreement with previously published studies, as well as a new patient cohort. If ADC and CBF values acquired in the same tumour are combined, the diagnostic accuracy is optimised.
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Affiliation(s)
- Patrick W Hales
- Developmental Imaging & Biophysics Section, UCL Great Ormond Street Institute of Child Health, London WC1N 1EH, United Kingdom.
| | - Felice d'Arco
- Great Ormond Street Children's Hospital, Great Ormond St, London WC1N 3JH, United Kingdom
| | - Jessica Cooper
- Great Ormond Street Children's Hospital, Great Ormond St, London WC1N 3JH, United Kingdom
| | - Josef Pfeuffer
- Siemens Healthcare GmbH, MR Application Development, Erlangen, Germany
| | - Darren Hargrave
- Great Ormond Street Children's Hospital, Great Ormond St, London WC1N 3JH, United Kingdom
| | - Kshitij Mankad
- Great Ormond Street Children's Hospital, Great Ormond St, London WC1N 3JH, United Kingdom
| | - Chris Clark
- Developmental Imaging & Biophysics Section, UCL Great Ormond Street Institute of Child Health, London WC1N 1EH, United Kingdom
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Al-Sharydah AM, Al-Arfaj HK, Saleh Al-Muhaish H, Al-Suhaibani SS, Al-Aftan MS, Almedallah DK, Al-Abdulwahhab AH, Al-Hedaithy AA, Al-Jubran SA. Can apparent diffusion coefficient values help distinguish between different types of pediatric brain tumors? Eur J Radiol Open 2019; 6:49-55. [PMID: 30627595 PMCID: PMC6321863 DOI: 10.1016/j.ejro.2018.12.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Accepted: 12/17/2018] [Indexed: 11/29/2022] Open
Abstract
Rationale and objectives Classifying brain tumors is challenging, but recently developed imaging techniques offer the opportunity for neuroradiologists and neurosurgeons to diagnose, differentiate, and manage different types of brain tumors. Such advances will be reflected in improvements in patients’ life expectancy and quality of life. Among the newest techniques, the apparent diffusion coefficient (ADC), which tracks the rate of microscopic water diffusion within tissues, has become a focus of investigation. Recently, ADC has been used as a preoperative diffusion-weighted magnetic resonance imaging (MRI) parameter that facilitates tumor diagnosis and grading. Here, we aimed to determine the ADC cutoff values for pediatric brain tumors (PBTs) categorized according to the World Health Organization (WHO) classification of brain tumors. Materials and methods We retrospectively reviewed 80 cases, and assessed them based on their MRI-derived ADC. These results were compared with those of WHO classification-based histopathology. Results Whole-lesion ADC values ranged 0.225–1.240 × 10−3 mm2/s for ependymal tumors, 0.107–1.571 × 10−3 mm2/s for embryonal tumors, 0.1065–2.37801 × 10−3 mm2/s for diffuse astrocytic and oligodendroglial tumors, 0.5220–0.7840 × 10−3 mm2/s for other astrocytic tumors, and 0.1530–0.8160 × 10−3 mm2/s for meningiomas. These findings revealed the usefulness of ADC in the differential diagnosis of PBT, as it was able to discriminate between five types of PBTs. Conclusion The application of an ADC diagnostic criterion would reduce the need for spectroscopic analysis. However, further research is needed to implement ADC in the differential diagnosis of PBT.
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Affiliation(s)
- Abdulaziz Mohammad Al-Sharydah
- Radiology Department, Imam Abdulrahman Bin Faisal University, King Fahd Hospital of the University, Al-Khobar City, Eastern Province, Saudi Arabia
| | - Hussain Khalid Al-Arfaj
- Medical Imaging Department, King Fahad Specialist Hospital, Dammam City, Eastern Province, Saudi Arabia
| | - Husam Saleh Al-Muhaish
- Medical Imaging Department, King Fahad Specialist Hospital, Dammam City, Eastern Province, Saudi Arabia
| | - Sari Saleh Al-Suhaibani
- Radiology Department, Imam Abdulrahman Bin Faisal University, King Fahd Hospital of the University, Al-Khobar City, Eastern Province, Saudi Arabia
| | - Mohammad Saad Al-Aftan
- Radiology Department, Imam Abdulrahman Bin Faisal University, King Fahd Hospital of the University, Al-Khobar City, Eastern Province, Saudi Arabia
| | - Dana Khaled Almedallah
- College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam City, Eastern Province, Saudi Arabia
| | - Abdulrhman Hamad Al-Abdulwahhab
- Radiology Department, Imam Abdulrahman Bin Faisal University, King Fahd Hospital of the University, Al-Khobar City, Eastern Province, Saudi Arabia
| | | | - Saeed Ahmad Al-Jubran
- Radiology Department, Imam Abdulrahman Bin Faisal University, King Fahd Hospital of the University, Al-Khobar City, Eastern Province, Saudi Arabia
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Wu CC, Jain R, Radmanesh A, Poisson LM, Guo WY, Zagzag D, Snuderl M, Placantonakis DG, Golfinos J, Chi AS. Predicting Genotype and Survival in Glioma Using Standard Clinical MR Imaging Apparent Diffusion Coefficient Images: A Pilot Study from The Cancer Genome Atlas. AJNR Am J Neuroradiol 2018; 39:1814-1820. [PMID: 30190259 DOI: 10.3174/ajnr.a5794] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2018] [Accepted: 07/02/2018] [Indexed: 12/26/2022]
Abstract
BACKGROUND AND PURPOSE Few studies have shown MR imaging features and ADC correlating with molecular markers and survival in patients with glioma. Our purpose was to correlate MR imaging features and ADC with molecular subtyping and survival in adult diffuse gliomas. MATERIALS AND METHODS Presurgical MRIs and ADC maps of 131 patients with diffuse gliomas and available molecular and survival data from The Cancer Genome Atlas were reviewed. MR imaging features, ADC (obtained by ROIs within the lowest ADC area), and mean relative ADC values were evaluated to predict isocitrate dehydrogenase (IDH) mutation, 1p/19q codeletion status, MGMT promoter methylation, and overall survival. RESULTS IDH wild-type gliomas tended to exhibit enhancement, necrosis, and edema; >50% enhancing area (P < .001); absence of a cystic area (P = .013); and lower mean relative ADC (median, 1.1 versus 1.6; P < .001) than IDH-mutant gliomas. By means of a cutoff value of 1.08 for mean relative ADC, IDH-mutant and IDH wild-type gliomas with lower mean relative ADC (<1.08) had poorer survival than those with higher mean relative ADC (median survival time, 24.2 months; 95% CI, 0.0-54.9 months versus 62.0 months; P = .003; and median survival time, 10.4 months; 95% CI, 4.4-16.4 months versus 17.7 months; 95% CI, 11.6-23.7 months; P = .041, respectively), regardless of World Health Organization grade. Median survival of those with IDH-mutant glioma with low mean relative ADC was not significantly different from that in those with IDH wild-type glioma. Other MR imaging features were not statistically significant predictors of survival. CONCLUSIONS IDH wild-type glioma showed lower ADC values, which also correlated with poor survival in both IDH-mutant and IDH wild-type gliomas, irrespective of histologic grade. A subgroup with IDH-mutant gliomas with lower ADC had dismal survival similar to that of those with IDH wild-type gliomas.
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Affiliation(s)
- C-C Wu
- From the Department of Radiology (C.-C.W., W.-Y.G.), Taipei Veterans General Hospital, Taipei, Taiwan, Republic of China
- School of Medicine (C.-C.W., W.-Y.G.), National Yang-Ming University, Taipei, Taiwan, Republic of China
- Departments of Radiology (C.-C.W., R.J., A.R.)
| | - R Jain
- Departments of Radiology (C.-C.W., R.J., A.R.)
- Neurosurgery (R.J., D.P., J.G.)
| | - A Radmanesh
- Departments of Radiology (C.-C.W., R.J., A.R.)
| | - L M Poisson
- Department of Public Health Sciences and Hermelin Brain Tumor Center (L.M.P.), Henry Ford Hospital, Detroit, Michigan
| | - W-Y Guo
- From the Department of Radiology (C.-C.W., W.-Y.G.), Taipei Veterans General Hospital, Taipei, Taiwan, Republic of China
- School of Medicine (C.-C.W., W.-Y.G.), National Yang-Ming University, Taipei, Taiwan, Republic of China
| | - D Zagzag
- Pathology (D.Z., M.S.), NYU School of Medicine, New York, New York
| | - M Snuderl
- Pathology (D.Z., M.S.), NYU School of Medicine, New York, New York
| | | | | | - A S Chi
- Neuro-Oncology Program (A.S.C.), Laura and Isaac Perlmutter Cancer Center, NYU School of Medicine and Langone Health, New York, New York
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Chen X, Jiang J, Shen N, Zhao L, Zhang J, Qin Y, Zhang S, Li L, Zhu W. Stretched-exponential model diffusion-weighted imaging as a potential imaging marker in preoperative grading and assessment of proliferative activity of gliomas. Am J Transl Res 2018; 10:2659-2668. [PMID: 30210702 PMCID: PMC6129521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2018] [Accepted: 07/08/2018] [Indexed: 06/08/2023]
Abstract
PURPOSE To assess the feasibility of using diffusion-weighted imaging (DWI) with the stretched-exponential model (SEM) for glioma grading and determining the correlations among parameters and proliferating cell nuclear antigen and Ki-67 expression. MATERIALS AND METHODS Mono-exponential model-DWI (MEM-DWI) and SEM-DWI were performed in 104 patients with pathologically proven gliomas. The patients were divided into the training set (n = 72) and test set (n = 32). Apparent diffusion coefficient (ADC), solid tumor distributed diffusion coefficient (DDC), and whole tumor α values were measured. These parameters were applied as cut-off values to determine the predictive accuracy. Proliferating cell nuclear antigen and Ki-67 expression correlated with all parameters. RESULTS Significant differences between low-grade gliomas (LGG) and high-grade gliomas (HGG) were observed for all parameters (P < 0.05), and significant differences in the ability of DDC to distinguish between any two glioma grades (P < 0.05) were also evident. DDC showed the highest sensitivity and specificity for glioma grading and was negatively correlated with Ki-67 and proliferating cell nuclear antigen expression. DDC also showed greater predictive accuracy than ADC and α. CONCLUSION SEM-DWI offers a better approach for glioma grading than MEM-DWI, and DDC may be a better imaging biomarker for grading and evaluating the proliferative activity of brain gliomas.
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Affiliation(s)
- Xiaowei Chen
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology Wuhan, China
| | - Jingjing Jiang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology Wuhan, China
| | - Nanxi Shen
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology Wuhan, China
| | - Lingyun Zhao
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology Wuhan, China
| | - Jiaxuan Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology Wuhan, China
| | - Yuanyuan Qin
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology Wuhan, China
| | - Shun Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology Wuhan, China
| | - Li Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology Wuhan, China
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology Wuhan, China
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Yazdani M, Rumboldt Z, Tabesh A, Giglio P, Schiarelli C, Morgan PS, Spampinato MV. Perilesional apparent diffusion coefficient in the preoperative evaluation of glioma grade. Clin Imaging 2018; 52:88-94. [PMID: 30032069 DOI: 10.1016/j.clinimag.2018.07.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2017] [Revised: 06/15/2018] [Accepted: 07/04/2018] [Indexed: 01/22/2023]
Abstract
Preoperative identification of high-grade gliomas is critical to optimize treatment strategy and to predict prognosis. To determine whether perilesional apparent diffusion coefficient (ADC) values differ between high- and low-grade tumors, we assessed water diffusivity within normal-appearing brain parenchyma (NABP) surrounding gliomas in twenty-one treatment-naïve patients. This showed significantly lower mean and 25th percentile (Q1) ADC values in high- grade compared to low-grade gliomas respectively in the range of 10-25 and 10-30 mm away from combined tumor and surrounding T2 signal. Thus, perilesional ADC measurement may reflect the extent of tumor infiltration beyond the abnormality seen on conventional MRI.
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Affiliation(s)
- Milad Yazdani
- Department of Radiology and Radiological Science, Medical University of South Carolina, 96 Jonathan Lucas Street, Charleston, SC, USA.
| | - Zoran Rumboldt
- Department of Radiology and Radiological Science, Medical University of South Carolina, 96 Jonathan Lucas Street, Charleston, SC, USA
| | - Ali Tabesh
- Department of Radiology and Radiological Science, Medical University of South Carolina, 96 Jonathan Lucas Street, Charleston, SC, USA
| | - Pierre Giglio
- Department of Neurology, Ohio State University, Wexner Medical College, Columbus, OH, USA
| | - Chiara Schiarelli
- Department of Radiology and Radiological Science, Medical University of South Carolina, 96 Jonathan Lucas Street, Charleston, SC, USA
| | - Paul S Morgan
- Medical Physics & Clinical Engineering, QMC Campus, University of Nottingham, Nottingham, UK
| | - Maria V Spampinato
- Department of Radiology and Radiological Science, Medical University of South Carolina, 96 Jonathan Lucas Street, Charleston, SC, USA
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Apparent diffusion coefficient histogram in breast cancer brain metastases may predict their biological subtype and progression. Sci Rep 2018; 8:9947. [PMID: 29967409 PMCID: PMC6028481 DOI: 10.1038/s41598-018-28315-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2018] [Accepted: 06/19/2018] [Indexed: 01/07/2023] Open
Abstract
Our aims for this study were to investigate the relationship between diffusion weighted image (DWI) parameters of brain metastases (BMs) and biological markers of breast cancer, and moreover, to assess whether DWI parameters accurately predict patient outcomes. DWI data for 34 patients with BMs from breast cancer were retrospectively reviewed. Apparent diffusion coefficient (ADC) histogram parameters were calculated from all measurable BMs. Two region of interest (ROI) methods are used for the analysis: from the largest BM or from all measurable BMs per one patient. ADC histogram parameters were compared between positive and negative groups depending on ER/PR and HER2 statuses. Overall survival analysis after BM (OSBM) and BM-specific progression-free survival (BMPFS) was analyzed with ADC parameters. Regardless of ROI methods, 25th percentile of ADC histogram was significantly lower in the ER/PR-positive group than in the ER/PR-negative group (P < 0.05). Using ROIs from all measurable BMs, Peak location, 50th percentile, 75th percentile, and mean value of ADC histogram were also significantly lower in the ER/PR-positive group than in the ER/PR-negative group (P < 0.05). However, there was no significant difference between HER2-postive and negative group. On univariate analysis, using ROIs from all measurable BMs, lower 25th percentile, 50th percentile and mean of ADC were significant predictors for poor BMPFS. ADC histogram analysis may have a prognostic value over ER/PR status as well as BMPFS.
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Diagnostic performance of apparent diffusion coefficient parameters for glioma grading. J Neurooncol 2018; 139:61-68. [PMID: 29574566 DOI: 10.1007/s11060-018-2841-5] [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: 12/12/2017] [Accepted: 03/20/2018] [Indexed: 10/17/2022]
Abstract
This study was to evaluate the diagnostic performance of fractional anisotropy (FA) and apparent diffusion coefficient (ADC) parameters derived from diffusion tensor imaging in the differentiation between grade II and III gliomas. The records of 60 patients (30 women, 30 men; mean age, 45.4 years) suspected of having gliomas who underwent an ADC image-guided stereotactic biopsy were retrospectively reviewed. The values of FA and ADC were measured, and the sensitivity, specificity, accuracy and area under the curve (AUC) of those parameters were calculated based on the receiver operating characteristic curve analysis. A predictive diagnostic equation was also constructed and evaluated. Significant differences in minimum ADC values were found in the quantitative analysis between the grade III and II glioma groups. The sensitivity, specificity, positive predictive values (PPV), negative predictive values (NPV), accuracy and AUC for identifying grade III and II gliomas at the optimum cut-off value of 0.895 × 10-3 mm2/s of minimum ADC were 81.0, 89.1, 77.3, 91.1, 86.6 and 0.87, respectively. The predictive diagnostic equation was superior to the single minimum ADC indicator with a sensitivity of 90.5%, a specificity of 84.8%, a PPV of 73.1%, an NPV of 95.1%, and an accuracy of 86.6%, respectively. The study provides evidence that minimum ADC values have a superior diagnostic performance in differentiating grade III and II gliomas, and the predictive diagnostic equation may be helpful in the differentiation.
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Takano K, Kinoshita M, Arita H, Okita Y, Chiba Y, Kagawa N, Watanabe Y, Shimosegawa E, Hatazawa J, Hashimoto N, Fujimoto Y, Kishima H. Influence of region-of-interest designs on quantitative measurement of multimodal imaging of MR non-enhancing gliomas. Oncol Lett 2018; 15:7934-7940. [PMID: 29725480 PMCID: PMC5920197 DOI: 10.3892/ol.2018.8319] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Accepted: 03/16/2018] [Indexed: 11/06/2022] Open
Abstract
A number of studies have revealed the usefulness of multimodal imaging in gliomas. Although the results have been heavily affected by the method used for region of interest (ROI) design, the most discriminatory method for setting the ROI remains unclear. The aim of the present study was to determine the most suitable ROI design for 18F-fluorodeoxyglucose (FDG) and 11C-methionine (MET) positron emission tomography (PET), apparent diffusion coefficient (ADC), and fractional anisotropy (FA) obtained by diffusion tensor imaging (DTI) from the viewpoint of grades of non-enhancing gliomas. A total of 31 consecutive patients with newly diagnosed, histologically confirmed magnetic resonance (MR) non-enhancing gliomas who underwent FDG-PET, MET-PET and DTI were retrospectively investigated. Quantitative measurements were performed using four different ROIs; hotspot/tumor center and whole tumor, constructed in either two-dimensional (2D) or three-dimensional (3D). Histopathological grading of the tumor was considered as empirical truth and the quantitative measurements obtained from each ROI was correlated with the grade of the tumor. The most discriminating ROI for non-enhancing glioma grading was different according to the different imaging modalities. 2D-hotspot/center ROI was most discriminating for FDG-PET (P=0.087), ADC map (P=0.0083), and FA map (P=0.25), whereas 3D-whole tumor ROI was best for MET-PET (P=0.0050). In the majority of scenarios, 2D-ROIs performed better than 3D-ROIs. Results from the image analysis using FDG-PET, MET-PET, ADC and FA may be affected by ROI design and the most discriminating ROI for non-enhancing glioma grading was different according to the imaging modality.
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Affiliation(s)
- Koji Takano
- Department of Neurosurgery, Osaka International Cancer Institute, Osaka 541-8567, Japan.,Department of Neurosurgery, Toyonaka Municipal Hospital, Toyonaka, Osaka 560-8565, Japan
| | - Manabu Kinoshita
- Department of Neurosurgery, Osaka International Cancer Institute, Osaka 541-8567, Japan.,Department of Neurosurgery, Osaka University Graduate School of Medicine, Suita, Osaka 565-0871, Japan
| | - Hideyuki Arita
- Department of Neurosurgery, Osaka University Graduate School of Medicine, Suita, Osaka 565-0871, Japan
| | - Yoshiko Okita
- Department of Neurosurgery, Osaka National Hospital, National Hospital Organization, Osaka 540-0006, Japan
| | - Yasuyoshi Chiba
- Department of Neurosurgery, Osaka University Graduate School of Medicine, Suita, Osaka 565-0871, Japan.,Department of Neurosurgery, Osaka Women's and Children's Hospital, Izumi, Osaka 594-1101, Japan
| | - Naoki Kagawa
- Department of Neurosurgery, Osaka University Graduate School of Medicine, Suita, Osaka 565-0871, Japan
| | - Yoshiyuki Watanabe
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka 565-0871, Japan
| | - Eku Shimosegawa
- Department of Nuclear Medicine and Tracer Kinetics, Osaka University Graduate School of Medicine, Suita, Osaka 565-0871, Japan
| | - Jun Hatazawa
- Department of Nuclear Medicine and Tracer Kinetics, Osaka University Graduate School of Medicine, Suita, Osaka 565-0871, Japan
| | - Naoya Hashimoto
- Department of Neurosurgery, Kyoto Prefectural University Graduate School of Medical Science, Kyoto 602-8566, Japan
| | - Yasunori Fujimoto
- Department of Neurosurgery, Osaka University Graduate School of Medicine, Suita, Osaka 565-0871, Japan
| | - Haruhiko Kishima
- Department of Neurosurgery, Osaka University Graduate School of Medicine, Suita, Osaka 565-0871, Japan
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Darbar A, Waqas M, Enam SF, Mahmood SD. Use of Preoperative Apparent Diffusion Coefficients to Predict Brain Tumor Grade. Cureus 2018; 10:e2284. [PMID: 29740523 PMCID: PMC5938001 DOI: 10.7759/cureus.2284] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Introduction The apparent diffusion coefficient (ADC) sequence is based on the diffusion properties of water molecules within tissues and correlates with tissue cellularity. ADC may have a role in predicting tumor grade for gliomas, and may in turn assist in identifying tumor biopsy sites. The purpose of this investigation was to assess the competence of preoperative ADC values in predicting tumor grades. Methods This was a retrospective investigation. We calculated the ADC values in the areas of greatest restriction in solid tumor components, and we recorded the pattern of contrast enhancement. Pathology reports masked to the imaging results were reviewed independently. We calculated the differences in the mean values of different tumor grades and high-grade and low-grade gliomas. A receiver operator curve (ROC) analysis assessed the predictive potential of ADC values for low-grade gliomas. Results Forty-eight cases of glioma were included in our study. We noted a statistically significant difference in the lowest mean ADC values for the tumor regions of Grade IV lesions (333.83 ± 295.47) compared with Grade I lesions (653.20 ± 145.07). On ROC analysis, we noted an area under the curve (AUC) of 0.80 for the lowest ADC value in the whole tumor region, which was a predictor of low-grade glioma with 95 % confidence interval (CI) of 0.675-0.926. The sensitivity of the lowest ADC value was 84.5% for high-grade lesions. Conclusion Given our findings that the means of the lowest ADC value are significantly different between low and high-grade gliomas with an AUC of 0.80 for ADC as a predictor of low-grade lesions and a sensitivity of 84.5% for high-grade lesions, ADC values contain some predictive properties of tumor grading. ADC values may be a valuable parameter in the assessment and treatment of tumors.
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Jung WS, Park CH, Hong CK, Suh SH, Ahn SJ. Diffusion-Weighted Imaging of Brain Metastasis from Lung Cancer: Correlation of MRI Parameters with the Histologic Type and Gene Mutation Status. AJNR Am J Neuroradiol 2018; 39:273-279. [PMID: 29301782 DOI: 10.3174/ajnr.a5516] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2017] [Accepted: 09/07/2017] [Indexed: 12/30/2022]
Abstract
BACKGROUND AND PURPOSE Development of noninvasive imaging biomarkers indicating the histology and the gene mutation status of brain metastasis from lung cancer is important. We aimed to investigate diffusion-weighted imaging parameters as predictors of the histology and gene mutations of brain metastasis from lung cancer. MATERIALS AND METHODS DWI data for 74 patients with brain metastasis from lung cancer were retrospectively reviewed. The patients were first grouped according to the primary tumor histology (adenocarcinoma, small-cell lung cancer, squamous cell carcinoma), and those with adenocarcinoma were further divided into epidermal growth factor receptor (EFGR) mutation-positive and wild type groups. Sex; age; number, size, and location of brain metastasis; DWI visual scores; the minimum ADC; and the normalized ADC ratio were compared among groups using χ2 and ANOVA. Multiple logistic regression analysis was performed to determine independent predictors of the EGFR mutation. RESULTS The minimum ADC was lower in the small-cell lung cancer group than in the other 2 groups, though the difference was not significant. Furthermore, minimum ADC and the normalized ADC ratio were significantly lower in the EGFR mutation-positive group than in the wild type group (P = .021 and .014, respectively). Multivariate analysis revealed that minimum ADC and the normalized ADC ratio were independently associated with the EGFR mutation status (P = .028 and .021, respectively). CONCLUSIONS Our results suggest that DWI parameters (minimum ADC and normalized ADC ratio) for the solid components of brain metastasis from lung cancer are not correlated with their histology, whereas they can predict the EGFR mutation status in brain metastasis from lung adenocarcinoma.
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Affiliation(s)
- W S Jung
- From the Departments of Radiology (W.S.J., C.H.P., S.H.S., S.J.A.).,Department of Radiology (W.S.J.), Ajou University School of Medicine, Suwon, Korea
| | - C H Park
- From the Departments of Radiology (W.S.J., C.H.P., S.H.S., S.J.A.)
| | - C-K Hong
- Neurosurgery (C.-K.H.), Gangnam Severance Hospital, Yonsei University, College of Medicine, Seoul, Korea
| | - S H Suh
- From the Departments of Radiology (W.S.J., C.H.P., S.H.S., S.J.A.)
| | - S J Ahn
- From the Departments of Radiology (W.S.J., C.H.P., S.H.S., S.J.A.)
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Iv M, Yoon BC, Heit JJ, Fischbein N, Wintermark M. Current Clinical State of Advanced Magnetic Resonance Imaging for Brain Tumor Diagnosis and Follow Up. Semin Roentgenol 2018; 53:45-61. [DOI: 10.1053/j.ro.2017.11.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Lewis S, Besa C, Wagner M, Jhaveri K, Kihira S, Zhu H, Sadoughi N, Fischer S, Srivastava A, Yee E, Mortele K, Babb J, Thung S, Taouli B. Prediction of the histopathologic findings of intrahepatic cholangiocarcinoma: qualitative and quantitative assessment of diffusion-weighted imaging. Eur Radiol 2017; 28:2047-2057. [PMID: 29234913 DOI: 10.1007/s00330-017-5156-6] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2017] [Accepted: 10/26/2017] [Indexed: 12/21/2022]
Abstract
OBJECTIVE To correlate qualitative and quantitative diffusion weighted imaging (DWI) characteristics of intrahepatic cholangiocarcinoma (ICC) with histopathologic tumour grade and fibrosis content. METHODS Fifty-one patients (21M/30F; mean age 61y) with ICC and MRI including DWI were included in this IRB-approved multicentre retrospective study. Qualitative tumour features were assessed. Tumour apparent diffusion coefficient (ADC) mean, minimum, and normalized (nADCliver) values were computed. Tumour grade [well(G1), moderately(G2), or poorly differentiated(G3)] and tumour fibrosis content [minimal(1), moderate(2), or abundant(3)] were categorized pathologically. Imaging findings and ADC values were compared with pathologic measures. Utility of ADC values for predicting tumour grade was assessed using ROC analysis. RESULTS 51 ICCs (mean size 6.5±1.1 cm) were assessed. 33/51(64%) of ICCs demonstrated diffuse hyperintensity and 15/51(29%) demonstrated target appearance on DWI. Infiltrative morphology (p=0.02) and tumour size (p=0.04) were associated with G3. ADCmean and nADCmean of G3 (1.32±0.47x10-3 mm2/sec and 0.97±0.95) were lower than G1+G2 (1.57±0.39x10-3 mm2/sec and 1.24±0.49; p=0.03 and p=0.04). ADCmean and nADCmean were inversely correlated with tumour grade (p<0.025). No correlation was found between ADC and tumour fibrosis content. AUROC, sensitivity and specificity of nADCmean for G3 versus G1+G2 were 0.71, 89.5% and 55.5%. CONCLUSION ADC quantification has reasonable accuracy for predicting ICC grade. KEY POINTS • ADC quantification was useful for predicting ICC tumour grade. • Infiltrative tumour morphology and size were associated with poorly differentiated ICCs. • ADC values depended more on ICC tumour grade than fibrosis content. • Ability to predict ICC tumour grade non-invasively could impact patient management.
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Affiliation(s)
- Sara Lewis
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Translational and Molecular Imaging Institute (TMII), Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Cecilia Besa
- Translational and Molecular Imaging Institute (TMII), Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Radiology, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Mathilde Wagner
- Translational and Molecular Imaging Institute (TMII), Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Radiology, Sorbonne Universités, UPMC, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Kartik Jhaveri
- Department of Radiology, University of Toronto, Toronto, Ontario, Canada
| | - Shingo Kihira
- Translational and Molecular Imaging Institute (TMII), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Hongfa Zhu
- Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nima Sadoughi
- Department of Radiology, University of Toronto, Toronto, Ontario, Canada
- Department of Radiology, University of Ottawa and The Ottawa Hospital, Ottawa, Canada
| | - Sandra Fischer
- Department of Pathology, University of Toronto, Ontario, Canada
| | - Amogh Srivastava
- Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Eric Yee
- Department of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- Department of Pathology, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Koenraad Mortele
- Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - James Babb
- Department of Radiology, New York University Langone Medical Center, New York, NY, USA
| | - Swan Thung
- Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bachir Taouli
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Translational and Molecular Imaging Institute (TMII), Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Zhao J, Li X, Zhang K, Yin X, Meng X, Han L, Zhang X. Prediction of microvascular invasion of hepatocellular carcinoma with preoperative diffusion-weighted imaging: A comparison of mean and minimum apparent diffusion coefficient values. Medicine (Baltimore) 2017; 96:e7754. [PMID: 28816952 PMCID: PMC5571689 DOI: 10.1097/md.0000000000007754] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
The aim of the study was to investigate the value of preoperative diffusion-weighted imaging (DWI) in predicting microvascular invasion (MVI) of hepatocellular carcinoma (HCC), using and comparing mean and minimum apparent diffusion coefficient (ADC) values.Preoperative MR images of 318 patients with HCC confirmed by surgical pathology were retrospectively analyzed. All patients underwent preoperative DWI on a 1.5 Tesla MRI scanner. The mean and minimum ADC values of the tumors were measured. Interobserver agreements were assessed by the intraclass correlation coefficient (ICC). The ADC values were compared in HCCs between with and without MVI. ROC curves of ADC values were obtained and then compared in distinguishing HCCs with MVI from those without MVI.There were 211 HCCs with MVI and 107 HCCs without MVI. ICC for the measurements of the mean and minimum ADC values between both observers was 0.88 (95% CI 0.85 - 0.90) and 0.88 (95% CI 0.85 - 0.90), respectively. The mean and minimum ADC values of HCCs with MVI were lower than those of HCCs without MVI (P = .00, .00, respectively). With a cut-off value of 0.98 × 10 mm/s, the minimum ADC (MinADC) showed a sensitivity of 62.56% and a specificity of 65.42% in predicting MVI, whereas the mean ADC provided a sensitivity of 79.15% and a specificity of 50.47% with a cut-off value of 1.19 × 10 mm/s. No significant difference existed between MinADC and mean ADC for their diagnostic performances in the prediction of MVI (P = .48).DWI could preoperatively provide quantitative parameters for predicting MVI of HCC.
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Affiliation(s)
- Jinkun Zhao
- Department of Radiology, The Second Hospital of Tianjin Medical University
| | - Xubin Li
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Huan-hu-xi Road, Hexi District, Tianjin
| | - Kun Zhang
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Huan-hu-xi Road, Hexi District, Tianjin
| | - Xiaoyu Yin
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Huan-hu-xi Road, Hexi District, Tianjin
| | - Xiangfu Meng
- Department of Radiology, Linyi Traditional Chinese Medicine Hospital, Shandong, China
| | - Lizhu Han
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Huan-hu-xi Road, Hexi District, Tianjin
| | - Xuening Zhang
- Department of Radiology, The Second Hospital of Tianjin Medical University
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Xing Z, Yang X, She D, Lin Y, Zhang Y, Cao D. Noninvasive Assessment of IDH Mutational Status in World Health Organization Grade II and III Astrocytomas Using DWI and DSC-PWI Combined with Conventional MR Imaging. AJNR Am J Neuroradiol 2017; 38:1138-1144. [PMID: 28450436 DOI: 10.3174/ajnr.a5171] [Citation(s) in RCA: 87] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2016] [Accepted: 02/06/2017] [Indexed: 12/17/2022]
Abstract
BACKGROUND AND PURPOSE Isocitrate dehydrogenase (IDH) has been shown to have both diagnostic and prognostic implications in gliomas. The purpose of this study was to examine whether DWI and DSC-PWI combined with conventional MR imaging could noninvasively predict IDH mutational status in World Health Organization grade II and III astrocytomas. MATERIALS AND METHODS We retrospectively reviewed DWI, DSC-PWI, and conventional MR imaging in 42 patients with World Health Organization grade II and III astrocytomas. Minimum ADC, relative ADC, and relative maximum CBV values were compared between IDH-mutant and wild-type tumors by using the Mann-Whitney U test. Receiver operating characteristic curve and logistic regression were used to assess their diagnostic performances. RESULTS Minimum ADC and relative ADC were significantly higher in IDH-mutated grade II and III astrocytomas than in IDH wild-type tumors (P < .05). Minimum ADC with the cutoff value of ≥1.01 × 10-3 mm2/s could differentiate the mutational status with a sensitivity, specificity, positive predictive value, and negative predictive value of 76.9%, 82.6%, 91.2%, and 60.5%, respectively. The threshold value of <2.35 for relative maximum CBV in the prediction of IDH mutation provided a sensitivity, specificity, positive predictive value, and negative predictive value of 100.0%, 60.9%, 85.6%, and 100.0%, respectively. A combination of DWI, DSC-PWI, and conventional MR imaging for the identification of IDH mutations resulted in a sensitivity, specificity, positive predictive value, and negative predictive value of 92.3%, 91.3%, 96.1%, and 83.6%. CONCLUSIONS A combination of conventional MR imaging, DWI, and DSC-PWI techniques produces a high sensitivity, specificity, positive predictive value, and negative predictive value for predicting IDH mutations in grade II and III astrocytomas. The strategy of using advanced, semiquantitative MR imaging techniques may provide an important, noninvasive, surrogate marker that should be studied further in larger, prospective trials.
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Affiliation(s)
- Z Xing
- From the Department of Radiology, First Affiliated Hospital of Fujian Medical University, Fuzhou, P.R. China
| | - X Yang
- From the Department of Radiology, First Affiliated Hospital of Fujian Medical University, Fuzhou, P.R. China
| | - D She
- From the Department of Radiology, First Affiliated Hospital of Fujian Medical University, Fuzhou, P.R. China
| | - Y Lin
- From the Department of Radiology, First Affiliated Hospital of Fujian Medical University, Fuzhou, P.R. China
| | - Y Zhang
- From the Department of Radiology, First Affiliated Hospital of Fujian Medical University, Fuzhou, P.R. China
| | - D Cao
- From the Department of Radiology, First Affiliated Hospital of Fujian Medical University, Fuzhou, P.R. China.
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Preoperative grading of supratentorial nonenhancing gliomas by high b-value diffusion-weighted 3 T magnetic resonance imaging. J Neurooncol 2017; 133:147-154. [PMID: 28439776 DOI: 10.1007/s11060-017-2423-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2016] [Accepted: 04/11/2017] [Indexed: 10/19/2022]
Abstract
The purpose of this study was to determine the difference in discrimination between high- and low-grade supratentorial nonenhancing gliomas (HGGs and LGGs, respectively) when using apparent diffusion coefficient (ADC) values with high or standard b-value. Thirty-nine patients underwent conventional magnetic resonance imaging and diffusion-weighted imaging (DWI) with standard and high b-values (b = 1000 and 3000 s/mm2, respectively). Minimum, maximum, and mean ADC values (ADCMIN, ADCMAX, and ADCMEAN, respectively) were measured from ADC maps with both b-values. Receiver operating curve analysis was used to determine the cutoff ADC values for distinguishing between nonenhancing HGGs and LGGs. ADCMIN, ADCMAX, and ADCMEAN values for the nonenhancing HGGs were lower than those for LGGs. These differences were much larger when a high b-value was used (all P < 0.0001) than when a standard b-value was used (P = 0.0001, <0.0001, and <0.0001, respectively). Discriminant analysis indicated that the greatest likelihood for discriminating HGGs and LGGs when ADCMEAN was obtained with a high b-value, with cutoff value of 0.814 × 10-3 mm2/s. ADC values obtained with a high b-value can be useful for grading and surgical management of nonenhancing HGGs and LGGs. The lowest degree of overlap was obtained when ADCMEAN was determined with a b-value of 3000 s/mm2.
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Sakata A, Fushimi Y, Okada T, Arakawa Y, Kunieda T, Minamiguchi S, Kido A, Sakashita N, Miyamoto S, Togashi K. Diagnostic performance between contrast enhancement, proton MR spectroscopy, and amide proton transfer imaging in patients with brain tumors. J Magn Reson Imaging 2017; 46:732-739. [PMID: 28252822 DOI: 10.1002/jmri.25597] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2016] [Accepted: 11/28/2016] [Indexed: 11/11/2022] Open
Abstract
PURPOSE To explore the relationship among parameters of magnetic resonance spectroscopy (MRS) and amide proton transfer (APT) imaging, and to assess the diagnostic performance of MRS and APT imaging for grading brain tumors in comparison with contrast enhancement of conventional MRI for preoperative grading in patients with brain tumor. MATERIALS AND METHODS Institutional Review Board approval and written informed consent were obtained. Forty-one patients with suspected brain tumors were enrolled in the study. Single-voxel MRS and 2D APT imaging of the same slice level were conducted using a 3T MRI scanner. Positive or negative contrast enhancement on T1 -weighted images was assessed by two neuroradiologists. Correlations among metabolite concentrations, metabolite ratios, and calculated histogram parameters, including mean APT (APTmean ) and the 90th percentile of APT (APT90 ) were assessed using Spearman's correlation coefficient. Diagnostic performance was evaluated with receiver operating characteristic (ROC) curve analysis for contrast enhancement and MRS and APT imaging. Values of P < 0.05 were considered statistically significant. RESULTS Positive correlations with statistical significance were found between total concentration of choline (Cho) and APT90 (r = 0.49), and between Cho/creatine (Cr) and APTmean (r = 0.65) as well as APT90 (r = 0.49). A negative correlation with statistical significance was observed between NAA/Cr and APTmean (r = -0.52). According to ROC curves, Cho/Cr, APTmean , APT90 , demonstrated higher area under the curve (AUC) values than that of contrast enhancement in grading gliomas. CONCLUSION Significant correlations were observed between metabolite concentrations and ratios on MRS and APT values. MRS and APT imaging showed comparable diagnostic capability for grading brain tumors, suggesting that both MRS and APT imaging offer potential for quantitatively assessing similar biological characteristics in brain tumors on noncontrast MRI. LEVEL OF EVIDENCE 2 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:732-739.
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Affiliation(s)
- Akihiko Sakata
- Kyoto University Graduate School of Medicine, Department of Diagnostic Imaging and Nuclear Medicine, Kyoto, Japan
| | - Yasutaka Fushimi
- Kyoto University Graduate School of Medicine, Department of Diagnostic Imaging and Nuclear Medicine, Kyoto, Japan
| | - Tomohisa Okada
- Kyoto University Graduate School of Medicine, Human Brain Research Center, Kyoto, Japan
| | - Yoshiki Arakawa
- Kyoto University Graduate School of Medicine, Department of Neurosurgery, Kyoto, Japan
| | - Takeharu Kunieda
- Kyoto University Graduate School of Medicine, Department of Neurosurgery, Kyoto, Japan
| | - Sachiko Minamiguchi
- Kyoto University Graduate School of Medicine, Department of Diagnostic Pathology, Kyoto, Japan
| | - Aki Kido
- Kyoto University Graduate School of Medicine, Department of Diagnostic Imaging and Nuclear Medicine, Kyoto, Japan
| | - Naotaka Sakashita
- Toshiba Medical Systems Corporations, MRI Systems Development Department, Otawara, Japan
| | - Susumu Miyamoto
- Kyoto University Graduate School of Medicine, Department of Neurosurgery, Kyoto, Japan
| | - Kaori Togashi
- Kyoto University Graduate School of Medicine, Department of Diagnostic Imaging and Nuclear Medicine, Kyoto, Japan
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The utility of diffusion MRI with quantitative ADC measurements for differentiating high-grade from low-grade cerebral gliomas: Evidence from a meta-analysis. J Neurol Sci 2016; 373:9-15. [PMID: 28131237 DOI: 10.1016/j.jns.2016.12.008] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2016] [Revised: 11/14/2016] [Accepted: 12/07/2016] [Indexed: 01/03/2023]
Abstract
OBJECTIVE The aim of this meta-analysis was to predict the grades of cerebral gliomas using quantitative apparent diffusion coefficient (ADC) values. MATERIALS AND METHODS A comprehensive search of the PubMed, EMBASE, Web of Science, and Cochrane Library databases was performed up to 8, 2016. The quality assessment of diagnostic accuracy studies (QUADAS 2) was used to evaluate the quality of studies. Statistical analyses included pooling of sensitivity and specificity, positive likelihood ratio (PLR), negative likelihood ratio' (NLR), diagnostic odds ratio (DOR), and diagnostic accuracy values of the included studies using the summary receiver operating characteristic (SROC). All analyses were conducted using STATA (version 12.0), RevMan (version 5.3), and Meta-Disc 1.4 software programs. RESULTS Fifteen studies were analyzed and included a total of 821 patients and 821 lesions. In regards to the diagnostic accuracy of ADC maps, the pooled SEN, SPE, PLR, NLR, and DOR with 95%CIs were 0.82 [95%CI: 0.76, 0.87] and 0.75 [95%CI: 0.67, 0.81], 3.24 [95%CI: 2.48, 4.24], 0.24 [95%CI: 0.17, 0.33], and 13.60 [95%CI: 8.37, 22.07], respectively. The SROC curve showed an AUC of 0.85. Deeks testing confirmed no significant publication bias in all studies. CONCLUSION Our findings indicate that quantitative ADC values have high accuracy in separating high-grade from low-grade cerebral gliomas. Further studies using a standardized methodology may help guide the use of ADC values for clinical decision-making.
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Raja R, Sinha N, Saini J, Mahadevan A, Rao KN, Swaminathan A. Assessment of tissue heterogeneity using diffusion tensor and diffusion kurtosis imaging for grading gliomas. Neuroradiology 2016; 58:1217-1231. [PMID: 27796448 DOI: 10.1007/s00234-016-1758-y] [Citation(s) in RCA: 49] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2016] [Accepted: 10/18/2016] [Indexed: 11/25/2022]
Abstract
INTRODUCTION In this work, we aim to assess the significance of diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) parameters in grading gliomas. METHODS Retrospective studies were performed on 53 subjects with gliomas belonging to WHO grade II (n = 19), grade III (n = 20) and grade IV (n = 14). Expert marked regions of interest (ROIs) covering the tumour on T2-weighted images. Statistical texture measures such as entropy and busyness calculated over ROIs on diffusion parametric maps were used to assess the tumour heterogeneity. Additionally, we propose a volume heterogeneity index derived from cross correlation (CC) analysis as a tool for grading gliomas. The texture measures were compared between grades by performing the Mann-Whitney test followed by receiver operating characteristic (ROC) analysis for evaluating diagnostic accuracy. RESULTS Entropy, busyness and volume heterogeneity index for all diffusion parameters except fractional anisotropy and anisotropy of kurtosis showed significant differences between grades. The Mann-Whitney test on mean diffusivity (MD), among DTI parameters, resulted in the highest discriminability with values of P = 0.029 (0.0421) for grade II vs. III and P = 0.0312 (0.0415) for III vs. IV for entropy (busyness). In DKI, mean kurtosis (MK) showed the highest discriminability, P = 0.018 (0.038) for grade II vs. III and P = 0.022 (0.04) for III vs. IV for entropy (busyness). Results of CC analysis illustrate the existence of homogeneity in volume (uniformity across slices) for lower grades, as compared to higher grades. Hypothesis testing performed on volume heterogeneity index showed P values of 0.0002 (0.0001) and 0.0003 (0.0003) between grades II vs. III and III vs. IV, respectively, for MD (MK). CONCLUSION In summary, the studies demonstrated great potential towards automating grading gliomas by employing tumour heterogeneity measures on DTI and DKI parameters.
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Affiliation(s)
- Rajikha Raja
- International Institute of Information Technology-Bangalore, 26/C, Electronics City, Hosur Road, Bangalore, India.
| | - Neelam Sinha
- International Institute of Information Technology-Bangalore, 26/C, Electronics City, Hosur Road, Bangalore, India.
| | - Jitender Saini
- National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Anita Mahadevan
- National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Kvl Narasinga Rao
- National Institute of Mental Health and Neurosciences, Bangalore, India
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Kosztyla R, Reinsberg SA, Moiseenko V, Toyota B, Nichol A. Interhemispheric Difference Images from Postoperative Diffusion Tensor Imaging of Gliomas. Cureus 2016; 8:e817. [PMID: 27843735 PMCID: PMC5096944 DOI: 10.7759/cureus.817] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Introduction Determining the full extent of gliomas during radiotherapy planning can be challenging with conventional T1 and T2 magnetic resonance imaging (MRI). The purpose of this study was to develop a method to automatically calculate differences in the fractional anisotropy (FA) and mean diffusivity (MD) values in target volumes obtained with diffusion tensor imaging (DTI) by comparing with values from anatomically homologous voxels on the contralateral side of the brain. Methods Seven patients with a histologically confirmed glioma underwent postoperative radiotherapy planning with 1.5 T MRI and computed tomography. DTI was acquired using echo planar imaging for 20 noncolinear directions with b = 1000 s/mm2 and one additional image with b = 0, repeated four times for signal averaging. The distribution of FA and MD was calculated in the gross tumor volume (GTV), shells 0-5 mm, 5-10 mm, 10-15 mm, 15-20 mm, and 20-25 mm outside the GTV, and the GTV mirrored in the left-right direction (mirGTV). All images were aligned to a template image, and FA and MD interhemispheric difference images were calculated. The difference in mean FA and MD between the regions of interest was statistically tested using two-sided paired t-tests with α = 0.05. Results The mean FA in mirGTV was 0.20 ± 0.04, which was larger than the FA in the GTV (0.12 ± 0.03) and shells 0-5 mm (0.15 ± 0.03) and 5-10 mm (0.17 ± 0.03) outside the GTV. The mean MD (×10-3 mm2/s) in mirGTV was 0.93 ± 0.09, which was smaller than the MD in the GTV (1.48 ± 0.19) and the peritumoral shells. The distribution of FA and MD interhemispheric differences followed the same trends as FA and MD values. Conclusions This study successfully implemented a method for calculation of FA and MD differences by comparison of voxel values with anatomically homologous voxels on the contralateral side of the brain. Further research is warranted to determine if radiotherapy planning using these images can be used to improve target delineation.
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Affiliation(s)
- Robert Kosztyla
- Department of Physics and Astronomy, University of British Columbia ; Department of Medical Physics, BC Cancer Agency
| | | | - Vitali Moiseenko
- Department of Radiation Medicine and Applied Sciences, University of California, San Diego
| | - Brian Toyota
- Division of Neurosurgery, University of British Columbia
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Wu X, Reinikainen P, Vanhanen A, Kapanen M, Vierikko T, Ryymin P, Hyödynmaa S, Kellokumpu-Lehtinen PL. Correlation between apparent diffusion coefficient value on diffusion-weighted MR imaging and Gleason score in prostate cancer. Diagn Interv Imaging 2016; 98:63-71. [PMID: 27687831 DOI: 10.1016/j.diii.2016.08.009] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2016] [Revised: 07/25/2016] [Accepted: 08/23/2016] [Indexed: 02/04/2023]
Abstract
OBJECTIVES To investigate whether diffusion-weighted imaging (DWI) apparent diffusion coefficient (ADC) correlates with prostate cancer aggressiveness and further to compare the diagnostic performance of ADC and normalized ADC (nADC: normalized to non-tumor tissue). PATIENTS AND METHODS Thirty pre-treatment patients (mean age, 69years; range: 59-78years) with prostate cancer underwent magnetic resonance imaging (MRI) examination, including DWI with three b values: 50, 400, and 800s/mm2. Both ADC and nADC were correlated with the Gleason score obtained through transrectal ultrasound-guided biopsy. RESULTS The tumor minimum ADC (ADCmin: the lowest ADC value within tumor) had an inverse correlation with the Gleason score (r=-0.43, P<0.05), and it was lower in patients with Gleason score 3+4 than in those with Gleason score 3+3 (0.54±0.11×103mm2/s vs. 0.64±0.12×10-3mm2/s, P<0.05). Both the nADCmin and nADCmean correlated with the Gleason score (r=-0.52 and r=-0.55, P<0.01; respectively), and they were lower in patients with Gleason score 3+4 than those with Gleason score 3+3 (P<0.01; respectively). Receiver operating characteristic (ROC) analysis showed that the area under the ROC curve was 0.765, 0.818, or 0.833 for the ADCmin, nADCmin, or nADCmean; respectively, in differentiating between Gleason score 3+4 and 3+3 tumors. CONCLUSION Tumor ADCmin, nADCmin, and nADCmean are useful markers to predict the aggressiveness of prostate cancer.
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Affiliation(s)
- X Wu
- Department of Oncology, Tampere University Hospital, Tampere, Finland; Medical Imaging Centre, Department of Radiology, Tampere University Hospital, Tampere, Finland; School of Medicine, University of Tampere, Tampere, Finland.
| | - P Reinikainen
- Department of Oncology, Tampere University Hospital, Tampere, Finland
| | - A Vanhanen
- Department of Oncology, Tampere University Hospital, Tampere, Finland; Medical Imaging Centre, Department of Medical Physics, Tampere University Hospital, Tampere, Finland
| | - M Kapanen
- Department of Oncology, Tampere University Hospital, Tampere, Finland; Medical Imaging Centre, Department of Medical Physics, Tampere University Hospital, Tampere, Finland
| | - T Vierikko
- Medical Imaging Centre, Department of Radiology, Tampere University Hospital, Tampere, Finland
| | - P Ryymin
- Medical Imaging Centre, Department of Radiology, Tampere University Hospital, Tampere, Finland
| | - S Hyödynmaa
- Medical Imaging Centre, Department of Medical Physics, Tampere University Hospital, Tampere, Finland
| | - P-L Kellokumpu-Lehtinen
- Department of Oncology, Tampere University Hospital, Tampere, Finland; School of Medicine, University of Tampere, Tampere, Finland
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