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Gong Z, Xu T, Peng N, Cheng X, Niu C, Wiestler B, Hong F, Li HB. A Multi-Center, Multi-Parametric MRI Dataset of Primary and Secondary Brain Tumors. Sci Data 2024; 11:789. [PMID: 39019912 PMCID: PMC11255278 DOI: 10.1038/s41597-024-03634-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Accepted: 07/11/2024] [Indexed: 07/19/2024] Open
Abstract
Brain metastases (BMs) and high-grade gliomas (HGGs) are the most common and aggressive types of malignant brain tumors in adults, with often poor prognosis and short survival. As their clinical symptoms and image appearances on conventional magnetic resonance imaging (MRI) can be astonishingly similar, their accurate differentiation based solely on clinical and radiological information can be very challenging, particularly for "cancer of unknown primary", where no systemic malignancy is known or found. Non-invasive multiparametric MRI and radiomics offer the potential to identify these distinct biological properties, aiding in the characterization and differentiation of HGGs and BMs. However, there is a scarcity of publicly available multi-origin brain tumor imaging data for tumor characterization. In this paper, we introduce a multi-center, multi-origin brain tumor MRI (MOTUM) imaging dataset obtained from 67 patients: 29 with high-grade gliomas, 20 with lung metastases, 10 with breast metastases, 2 with gastric metastasis, 4 with ovarian metastasis, and 2 with melanoma metastasis. This dataset includes anonymized DICOM files alongside processed FLAIR, T1-weighted, contrast-enhanced T1-weighted, T2-weighted sequences images, segmentation masks of two tumor regions, and clinical data. Our data-sharing initiative is to support the benchmarking of automated tumor segmentation, multi-modal machine learning, and disease differentiation of multi-origin brain tumors in a multi-center setting.
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Affiliation(s)
- Zhenyu Gong
- Department of Neurosurgery, Second Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
- Department of Neurosurgery, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Tao Xu
- Department of Neurosurgery, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Nan Peng
- Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Xing Cheng
- Department of Spine Surgery, Orthopedics Center of Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Department of Spine Surgery, Orthopedic Research Institute, The First Affiliated Hospital of Sun Yat-sen University; Guangdong Provincial Key Laboratory of Orthopedics and Traumatology, Guangzhou, China
| | - Chen Niu
- PET/CT center, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Fan Hong
- Department of Neurosurgery, Second Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China.
| | - Hongwei Bran Li
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany.
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, USA.
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Liu X, Liu J. Aided Diagnosis Model Based on Deep Learning for Glioblastoma, Solitary Brain Metastases, and Primary Central Nervous System Lymphoma with Multi-Modal MRI. BIOLOGY 2024; 13:99. [PMID: 38392317 PMCID: PMC10887006 DOI: 10.3390/biology13020099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 01/26/2024] [Accepted: 01/27/2024] [Indexed: 02/24/2024]
Abstract
(1) Background: Diagnosis of glioblastoma (GBM), solitary brain metastases (SBM), and primary central nervous system lymphoma (PCNSL) plays a decisive role in the development of personalized treatment plans. Constructing a deep learning classification network to diagnose GBM, SBM, and PCNSL with multi-modal MRI is important and necessary. (2) Subjects: GBM, SBM, and PCNSL were confirmed by histopathology with the multi-modal MRI examination (study from 1225 subjects, average age 53 years, 671 males), 3.0 T T2 fluid-attenuated inversion recovery (T2-Flair), and Contrast-enhanced T1-weighted imaging (CE-T1WI). (3) Methods: This paper introduces MFFC-Net, a classification model based on the fusion of multi-modal MRIs, for the classification of GBM, SBM, and PCNSL. The network architecture consists of parallel encoders using DenseBlocks to extract features from different modalities of MRI images. Subsequently, an L1-norm feature fusion module is applied to enhance the interrelationships among tumor tissues. Then, a spatial-channel self-attention weighting operation is performed after the feature fusion. Finally, the classification results are obtained using the full convolutional layer (FC) and Soft-max. (4) Results: The ACC of MFFC-Net based on feature fusion was 0.920, better than the radiomics model (ACC of 0.829). There was no significant difference in the ACC compared to the expert radiologist (0.920 vs. 0.924, p = 0.774). (5) Conclusions: Our MFFC-Net model could distinguish GBM, SBM, and PCNSL preoperatively based on multi-modal MRI, with a higher performance than the radiomics model and was comparable to radiologists.
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Affiliation(s)
- Xiao Liu
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
| | - Jie Liu
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
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Müller SJ, Khadhraoui E, Ernst M, Rohde V, Schatlo B, Malinova V. Differentiation of multiple brain metastases and glioblastoma with multiple foci using MRI criteria. BMC Med Imaging 2024; 24:3. [PMID: 38166651 PMCID: PMC10759655 DOI: 10.1186/s12880-023-01183-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Accepted: 12/13/2023] [Indexed: 01/05/2024] Open
Abstract
OBJECTIVE Glioblastoma with multiple foci (mGBM) and multiple brain metastases share several common features on magnetic resonance imaging (MRI). A reliable preoperative diagnosis would be of clinical relevance. The aim of this study was to explore the differences and similarities between mGBM and multiple brain metastases on MRI. METHODS We performed a retrospective analysis of 50 patients with mGBM and compared them with a cohort of 50 patients with multiple brain metastases (2-10 lesions) histologically confirmed and treated at our department between 2015 and 2020. The following imaging characteristics were analyzed: lesion location, distribution, morphology, (T2-/FLAIR-weighted) connections between the lesions, patterns of contrast agent uptake, apparent diffusion coefficient (ADC)-values within the lesion, the surrounding T2-hyperintensity, and edema distribution. RESULTS A total of 210 brain metastases and 181 mGBM lesions were analyzed. An infratentorial localization was found significantly more often in patients with multiple brain metastases compared to mGBM patients (28 vs. 1.5%, p < 0.001). A T2-connection between the lesions was detected in 63% of mGBM lesions compared to 1% of brain metastases. Cortical edema was only present in mGBM. Perifocal edema with larger areas of diffusion restriction was detected in 31% of mGBM patients, but not in patients with metastases. CONCLUSION We identified a set of imaging features which improve preoperative diagnosis. The presence of T2-weighted imaging hyperintensity connection between the lesions and cortical edema with varying ADC-values was typical for mGBM.
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Affiliation(s)
- Sebastian Johannes Müller
- Department of Neuroradiology, University Medical Center, Göttingen, Germany
- Neuroradiologische Klinik, Klinikum Stuttgart, Stuttgart, Germany
| | - Eya Khadhraoui
- Department of Neuroradiology, University Medical Center, Göttingen, Germany
- Neuroradiologische Klinik, Klinikum Stuttgart, Stuttgart, Germany
| | - Marielle Ernst
- Department of Neuroradiology, University Medical Center, Göttingen, Germany
| | - Veit Rohde
- Department of Neurosurgery, University Medical Center, Göttingen, Germany
| | - Bawarjan Schatlo
- Department of Neurosurgery, University Medical Center, Göttingen, Germany
| | - Vesna Malinova
- Department of Neurosurgery, University Medical Center, Göttingen, Germany.
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Zhu FY, Sun YF, Yin XP, Zhang Y, Xing LH, Ma ZP, Xue LY, Wang JN. Using machine learning-based radiomics to differentiate between glioma and solitary brain metastasis from lung cancer and its subtypes. Discov Oncol 2023; 14:224. [PMID: 38055122 DOI: 10.1007/s12672-023-00837-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 11/22/2023] [Indexed: 12/07/2023] Open
Abstract
OBJECTIVE To establish a machine learning-based radiomics model to differentiate between glioma and solitary brain metastasis from lung cancer and its subtypes, thereby achieving accurate preoperative classification. MATERIALS AND METHODS A retrospective analysis was conducted on MRI T1WI-enhanced images of 105 patients with glioma and 172 patients with solitary brain metastasis from lung cancer, which were confirmed pathologically. The patients were divided into the training group and validation group in an 8:2 ratio for image segmentation, extraction, and filtering; multiple layer perceptron (MLP), support vector machine (SVM), random forest (RF), and logistic regression (LR) were used for modeling; fivefold cross-validation was used to train the model; the validation group was used to evaluate and assess the predictive performance of the model, ROC curve was used to calculate the accuracy, sensitivity, and specificity of the model, and the area under curve (AUC) was used to assess the predictive performance of the model. RESULTS The accuracy and AUC of the MLP differentiation model for high-grade glioma and solitary brain metastasis in the validation group was 0.992, 1.000, respectively, while the sensitivity and specificity were 1.000, 0.968, respectively. The accuracy and AUC for the MLP and SVM differentiation model for high-grade glioma and small cell lung cancer brain metastasis in the validation group was 0.966, 1.000, respectively, while the sensitivity and specificity were 1.000, 0.929, respectively. The accuracy and AUC for the MLP differentiation model for high-grade glioma and non-small cell lung cancer brain metastasis in the validation group was 0.982, 0.999, respectively, while the sensitivity and specificity were 0.958, 1.000, respectively. CONCLUSION The application of machine learning-based radiomics has a certain clinical value in differentiating glioma from solitary brain metastasis from lung cancer and its subtypes. In the HGG/SBM and HGG/NSCLC SBM validation groups, the MLP model had the best diagnostic performance, while in the HGG/SCLC SBM validation group, the MLP and SVM models had the best diagnostic performance.
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Affiliation(s)
- Feng-Ying Zhu
- Department of Radiology, Affiliated Hospital of Hebei University, No.212 of Yuhua Road, Lianchi District, Baoding, 071000, China
| | - Yu-Feng Sun
- College of Electronic Information Engineering, Hebei University, Baoding, 071002, China
| | - Xiao-Ping Yin
- Department of Radiology, Affiliated Hospital of Hebei University, No.212 of Yuhua Road, Lianchi District, Baoding, 071000, China
| | - Yu Zhang
- Department of Radiology, Affiliated Hospital of Hebei University, No.212 of Yuhua Road, Lianchi District, Baoding, 071000, China
| | - Li-Hong Xing
- Department of Radiology, Affiliated Hospital of Hebei University, No.212 of Yuhua Road, Lianchi District, Baoding, 071000, China
| | - Ze-Peng Ma
- Department of Radiology, Affiliated Hospital of Hebei University, No.212 of Yuhua Road, Lianchi District, Baoding, 071000, China
| | - Lin-Yan Xue
- College of Quality and Technical Supervision, Hebei University, No.180 of Wusi Road, Lianchi District, Baoding, 071002, China.
| | - Jia-Ning Wang
- Department of Radiology, Affiliated Hospital of Hebei University, No.212 of Yuhua Road, Lianchi District, Baoding, 071000, China.
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Hung ND, Dung LV, Vi NH, Hai Anh NT, Hong Phuong LT, Hieu ND, Duc NM. The role of 3-Tesla magnetic resonance perfusion and spectroscopy in distinguishing glioblastoma from solitary brain metastasis. J Clin Imaging Sci 2023; 13:19. [PMID: 37559877 PMCID: PMC10408633 DOI: 10.25259/jcis_49_2023] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 06/10/2023] [Indexed: 08/11/2023] Open
Abstract
OBJECTIVES This study aimed to assess the value of magnetic resonance perfusion (MR perfusion) and magnetic resonance spectroscopy (MR spectroscopy) in 3.0-Tesla magnetic resonanceimaging (MRI) for differential diagnosis of glioblastoma (GBM) and solitary brain metastasis (SBM). MATERIAL AND METHODS This retrospective study involved 36 patients, including 24 cases of GBM and 12 of SBM diagnosed using histopathology. All patients underwent a 3.0-Tesla MRI examination with pre-operative MR perfusion and MR spectroscopy. We assessed the differences in age, sex, cerebral blood volume (CBV), relative CBV (rCBV), and the metabolite ratios of choline/N-acetylaspartate (Cho/NAA) and Cho/creatine between the GBM and SBM groups using the Mann-Whitney U-test and Chi-square test. The cutoff value, area under the curve, sensitivity, specificity, positive predictive value, and negative predictive value of the significantly different parameters between these two groups were determined using the receiver operating characteristic curve. RESULTS In MR perfusion, the CBV of the peritumoral region (pCBV) had the highest preoperative predictive value in discriminating GBM from SBM (cutoff: 1.41; sensitivity: 70.83%; and specificity: 83.33%), followed by the ratio of CBV of the solid tumor component to CBV of normal white matter (rCBVt/n) and the ratio of CBV of the pCBV to CBV of normal white matter (rCBVp/n). In MR spectroscopy, the Cho/NAA ratio of the pCBV (pCho/NAA; cutoff: 1.02; sensitivity: 87.50%; and specificity: 75%) and the Cho/NAA ratio of the solid tumor component (tCho/NAA; cutoff: 2.11; sensitivity: 87.50%; and specificity: 66.67%) were significantly different between groups. Moreover, combining these remarkably different parameters increased their diagnostic utility for distinguishing between GBM and SBM. CONCLUSION pCBV, rCBVt/n, rCBVp/n, pCho/NAA, and tCho/NAA are useful indices for differentiating between GBM and SBM. Combining these indices can improve diagnostic performance in distinguishing between these two tumors.
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Affiliation(s)
- Nguyen Duy Hung
- Department of Radiology, Hanoi Medical University, Ho Chi Minh City, Hanoi, Vietnam
| | - Le Van Dung
- Department of Radiology, Hanoi Medical University, Ho Chi Minh City, Hanoi, Vietnam
| | - Nguyen Ha Vi
- Department of Radiology, Hanoi Medical University, Ho Chi Minh City, Hanoi, Vietnam
| | - Nguyen-Thi Hai Anh
- Department of Radiology, Hanoi Medical University, Ho Chi Minh City, Hanoi, Vietnam
| | | | - Nguyen Dinh Hieu
- Department of Radiology, Hanoi Medical University, Ho Chi Minh City, Hanoi, Vietnam
| | - Nguyen Minh Duc
- Department of Radiology, Pham Ngoc Thach University of Medicine, Ho Chi Minh City, Vietnam
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Chen L, Li T, Li Y, Zhang J, Li S, Zhu L, Qin J, Tang L, Zeng Z. Combining amide proton transfer-weighted and arterial spin labeling imaging to differentiate solitary brain metastases from glioblastomas. Magn Reson Imaging 2023; 102:96-102. [PMID: 37172748 DOI: 10.1016/j.mri.2023.05.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Revised: 05/03/2023] [Accepted: 05/09/2023] [Indexed: 05/15/2023]
Abstract
PURPOSE To evaluate the clinical utility of amide proton transfer-weighted imaging (APTw) and arterial spin labeling (ASL) in differentiating solitary brain metastases (SBMs) from glioblastomas (GBMs). METHODS Forty-eight patients diagnosed with brain tumors were enrolled. All patients underwent conventional MRI, APTw, and ASL scans on a 3.0 T MRI system. The mean APTw value and mean cerebral blood flow (CBF) value were measured. The differences in various parameters between GBMs and SBMs were assessed using the independent-samples t-test. The quantitative performance of these MRI parameters in distinguishing between GBMs and SBMs was evaluated using receiver operating characteristic (ROC) curve analysis. RESULTS GBMs exhibited significantly higher APTw and CBF values in peritumoral regions compared with SBMs (P < 0.05). There was no significant difference between SBMs and GBMs in tumor cores. APTw MRI had a higher diagnostic efficiency in differentiating SBMs from GBMs (area under the curve [AUC]: 0.864; 75.0% sensitivity and 81.8% specificity). Combined use of APTw and CBF value increased the AUC to 0.927. CONCLUSION APTw may be superior to ASL for distinguishing between SBMs and GBMs. Combination of APTw and ASL showed better discrimination and a superior diagnostic performance.
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Affiliation(s)
- Ling Chen
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No.6, Shuangyong Road, Nanning, Guangxi 530021, China; Department of Medical Imaging Center, The Fourth Affiliated Hospital, Guangxi Medical University, Heping Road No.156, Liunan District, Liuzhou, Guangxi 545007, China
| | - Tao Li
- Department of Medical Imaging Center, The Fourth Affiliated Hospital, Guangxi Medical University, Heping Road No.156, Liunan District, Liuzhou, Guangxi 545007, China
| | - Yao Li
- Department of Neurosurgery, The Fourth Affiliated Hospital, Guangxi Medical University, Heping Road No.156, Liunan District, Liuzhou, Guangxi 545007, China
| | - Jinhuan Zhang
- Department of Medical Imaging Center, The Fourth Affiliated Hospital, Guangxi Medical University, Heping Road No.156, Liunan District, Liuzhou, Guangxi 545007, China
| | - Shuanghong Li
- Department of Medical Imaging Center, The Fourth Affiliated Hospital, Guangxi Medical University, Heping Road No.156, Liunan District, Liuzhou, Guangxi 545007, China
| | - Li Zhu
- Department of Medical Imaging Center, The Fourth Affiliated Hospital, Guangxi Medical University, Heping Road No.156, Liunan District, Liuzhou, Guangxi 545007, China
| | - Jianli Qin
- Department of Medical Imaging Center, The Fourth Affiliated Hospital, Guangxi Medical University, Heping Road No.156, Liunan District, Liuzhou, Guangxi 545007, China
| | - Lifang Tang
- Department of Medical Imaging Center, The Fourth Affiliated Hospital, Guangxi Medical University, Heping Road No.156, Liunan District, Liuzhou, Guangxi 545007, China
| | - Zisan Zeng
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No.6, Shuangyong Road, Nanning, Guangxi 530021, China.
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Wang X, Dai Y, Lin H, Cheng J, Zhang Y, Cao M, Zhou Y. Shape and texture analyses based on conventional MRI for the preoperative prediction of the aggressiveness of pituitary adenomas. Eur Radiol 2023; 33:3312-3321. [PMID: 36738323 DOI: 10.1007/s00330-023-09412-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 12/21/2022] [Accepted: 12/29/2022] [Indexed: 02/05/2023]
Abstract
OBJECTIVES Pituitary adenomas can exhibit aggressive behavior, characterized by rapid growth, resistance to conventional treatment, and early recurrence. This study aims to evaluate the clinical value of shape-related features combined with textural features based on conventional MRI in evaluating the aggressiveness of pituitary adenomas and develop the best diagnostic model. METHODS Two hundred forty-six pituitary adenoma patients (84 aggressive, 162 non-aggressive) who underwent preoperative MRI were retrospectively reviewed. The patients were divided into training (n = 193) and testing (n = 53) sets. Clinical information, shape-related, and textural features extracted from the tumor volume on contrast-enhanced T1-weighted images (CE-T1WI), were compared between aggressive and non-aggressive groups. Variables with significant differences were enrolled into Pearson's correlation analysis to weaken multicollinearity. Logistic regression models based on the selected features were constructed to predict tumor aggressiveness under fivefold cross-validation. RESULTS Sixty-five imaging features, including five shape-related and sixty textural features, were extracted from volumetric CE-T1WI. Forty-seven features were significantly different between aggressive and non-aggressive groups (all p values < 0.05). After feature selection, four features (SHAPE_Sphericity, SHAPE_Compacity, DISCRETIZED_Q3, and DISCRETIZED_Kurtosis) were put into logistic regression analysis. Based on the combination of these features and Knosp grade, the model yielded an area under the curve value of 0.935, with a sensitivity of 94.4% and a specificity of 82.9%, to discriminate between aggressive and non-aggressive pituitary adenomas in the testing set. CONCLUSION The radiomic model based on tumor shape and textural features study from CE-T1WI might potentially assist in the preoperative aggressiveness diagnosis of pituitary adenomas. KEY POINTS • Pituitary adenomas with aggressive behavior exhibit rapid growth, resistance to conventional treatment, and early recurrence despite gross resection and may require multiline treatments. • Shape-related features and texture features based on CE-T1WI were significantly correlated with the Ki-67 labeling index, mitotic count, and p53 expression, and the proposed model achieved a favorable prediction of the aggressiveness of PAs with an AUC value of 0.935. • The prediction model might provide valuable guidance for individualized treatment in patients with PAs.
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Affiliation(s)
- Xiaoqing Wang
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yongming Dai
- Central Research Institute, United Imaging Healthcare, Shanghai, China
| | - Hai Lin
- Central Research Institute, United Imaging Healthcare, Shanghai, China
| | - Jiahui Cheng
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yiming Zhang
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Mengqiu Cao
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
| | - Yan Zhou
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
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Radiomics can differentiate high-grade glioma from brain metastasis: a systematic review and meta-analysis. Eur Radiol 2022; 32:8039-8051. [PMID: 35587827 DOI: 10.1007/s00330-022-08828-x] [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: 12/10/2021] [Revised: 04/05/2022] [Accepted: 04/18/2022] [Indexed: 01/03/2023]
Abstract
OBJECTIVE (1) To evaluate the diagnostic performance of radiomics in differentiating high-grade glioma from brain metastasis and how to improve the model. (2) To assess the methodological quality of radiomics studies and explore ways of embracing the clinical application of radiomics. METHODS Studies using radiomics to differentiate high-grade glioma from brain metastasis published by 26 July 2021 were systematically reviewed. Methodological quality and risk of bias were assessed using the Radiomics Quality Score (RQS) system and Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool, respectively. Pooled sensitivity and specificity of the radiomics model were also calculated. RESULTS Seventeen studies combining 1,717 patients were included in the systematic review, of which 10 studies without data leakage suspicion were employed for the quantitative statistical analysis. The average RQS was 5.13 (14.25% of total), with substantial or almost perfect inter-rater agreements. The inclusion of clinical features in the radiomics model was only reported in one study, as was the case for publicly available algorithm code. The pooled sensitivity and specificity were 84% (95% CI, 80-88%) and 84% (95% CI, 81-87%), respectively. The performances of feature extraction from the volume of interest (VOI) or (semi) automatic segmentation in the radiomics models were superior to those of protocols employing region of interest (ROI) or manual segmentation. CONCLUSION Radiomics can accurately differentiate high-grade glioma from brain metastasis. The adoption of standardized workflow to avoid potential data leakage as well as the integration of clinical features and radiomics are advised to consider in future studies. KEY POINTS • The pooled sensitivity and specificity of radiomics for differentiating high-grade gliomas from brain metastasis were 84% and 84%, respectively. • Avoiding potential data leakage by adopting an intensive and standardized workflow is essential to improve the quality and generalizability of the radiomics model. • The application of radiomics in combination with clinical features in differentiating high-grade gliomas from brain metastasis needs further validation.
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Li J, Ek F, Olsson R, Belting M, Bengzon J. Glioblastoma CD105 + cells define a SOX2 - cancer stem cell-like subpopulation in the pre-invasive niche. Acta Neuropathol Commun 2022; 10:126. [PMID: 36038950 PMCID: PMC9426031 DOI: 10.1186/s40478-022-01422-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 08/04/2022] [Indexed: 11/10/2022] Open
Abstract
Glioblastoma (GBM) is the most common and most aggressive primary brain tumor in adults. Glioma stem like cells (GSC) represent the highest cellular hierarchy in GBM and have a determining role in tumor growth, recurrence and patient prognosis. However, a better definition of GSC subpopulations, especially at the surgical resection margin, is warranted for improved oncological treatment options. The present study interrogated cells expressing CD105 (CD105+) specifically within the tumor front and the pre-invasive niche as a potential GSC subpopulation. GBM primary cell lines were generated from patients (n = 18) and CD105+ cells were isolated and assessed for stem-like characteristics. In vitro, CD105+ cells proliferated and enriched in serum-containing medium but not in serum-free conditions. CD105+ cells were characterized by Nestin+, Vimentin+ and SOX2-, clearly distinguishing them from SOX2+ GCS. GBM CD105+ cells differentiated into osteocytes and adipocytes but not chondrocytes. Exome sequencing revealed that GBM CD105+ cells matched 83% of somatic mutations in the Cancer cell line encyclopedia, indicating a malignant phenotype and in vivo xenotransplantation assays verified their tumorigenic potential. Cytokine assays showed that immunosuppressive and protumorigenic cytokines such as IL6, IL8, CCL2, CXCL-1 were produced by CD105+ cells. Finally, screening for 88 clinical drugs revealed that GBM CD105+ cells are resistant to most chemotherapeutics except Doxorubicin, Idarubicin, Fludarabine and ABT-751. Our study provides a rationale for targeting tumoral CD105+ cells in order to reshape the tumor microenvironment and block GBM progression.
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Affiliation(s)
- Jiaxin Li
- Stem Cell Center, Lund University, Lund, Sweden.
- Division of Neurosurgery, Department of Clinical Sciences, Lund University, Lund, Sweden.
| | - Fredrik Ek
- Chemical Biology and Therapeutics, Department of Experimental Medical Science, Lund University, Lund, Sweden
| | - Roger Olsson
- Chemical Biology and Therapeutics, Department of Experimental Medical Science, Lund University, Lund, Sweden
| | - Mattias Belting
- Section of Oncology, Department of Clinical Sciences, Lund University, Lund, Sweden
- Department of Hematology, Oncology and Radiophysics, Skane University Hospital, Lund, Sweden
- Science for Life Laboratory, Department of Immunology, Genetics, and Pathology, Uppsala University, Uppsala, Sweden
| | - Johan Bengzon
- Stem Cell Center, Lund University, Lund, Sweden
- Division of Neurosurgery, Department of Clinical Sciences, Lund University, Lund, Sweden
- Department of Neurosurgery, Skane University Hospital, Lund, Sweden
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Wang P, Gao E, Qi J, Ma X, Zhao K, Bai J, Zhang Y, Zhang H, Yang G, Cheng J, Zhao G. Quantitative analysis of mean apparent propagator-magnetic resonance imaging for distinguishing glioblastoma from solitary brain metastasis. Eur J Radiol 2022; 154:110430. [DOI: 10.1016/j.ejrad.2022.110430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 06/29/2022] [Indexed: 11/27/2022]
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Machine Learning Applications for Differentiation of Glioma from Brain Metastasis-A Systematic Review. Cancers (Basel) 2022; 14:cancers14061369. [PMID: 35326526 PMCID: PMC8946855 DOI: 10.3390/cancers14061369] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 02/22/2022] [Accepted: 03/01/2022] [Indexed: 12/19/2022] Open
Abstract
Simple Summary We present a systematic review of published reports on machine learning (ML) applications for the differentiation of gliomas from brain metastases by summarizing study characteristics, strengths, and pitfalls. Based on these findings, we present recommendations for future research in this field. Abstract Glioma and brain metastasis can be difficult to distinguish on conventional magnetic resonance imaging (MRI) due to the similarity of imaging features in specific clinical circumstances. Multiple studies have investigated the use of machine learning (ML) models for non-invasive differentiation of glioma from brain metastasis. Many of the studies report promising classification results, however, to date, none have been implemented into clinical practice. After a screening of 12,470 studies, we included 29 eligible studies in our systematic review. From each study, we aggregated data on model design, development, and best classifiers, as well as quality of reporting according to the TRIPOD statement. In a subset of eligible studies, we conducted a meta-analysis of the reported AUC. It was found that data predominantly originated from single-center institutions (n = 25/29) and only two studies performed external validation. The median TRIPOD adherence was 0.48, indicating insufficient quality of reporting among surveyed studies. Our findings illustrate that despite promising classification results, reliable model assessment is limited by poor reporting of study design and lack of algorithm validation and generalizability. Therefore, adherence to quality guidelines and validation on outside datasets is critical for the clinical translation of ML for the differentiation of glioma and brain metastasis.
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Mărginean L, Ștefan PA, Lebovici A, Opincariu I, Csutak C, Lupean RA, Coroian PA, Suciu BA. CT in the Differentiation of Gliomas from Brain Metastases: The Radiomics Analysis of the Peritumoral Zone. Brain Sci 2022; 12:brainsci12010109. [PMID: 35053852 PMCID: PMC8774238 DOI: 10.3390/brainsci12010109] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 12/20/2021] [Accepted: 12/21/2021] [Indexed: 02/06/2023] Open
Abstract
Due to their similar imaging features, high-grade gliomas (HGGs) and solitary brain metastases (BMs) can be easily misclassified. The peritumoral zone (PZ) of HGGs develops neoplastic cell infiltration, while in BMs the PZ contains pure vasogenic edema. As the two PZs cannot be differentiated macroscopically, this study investigated whether computed tomography (CT)-based texture analysis (TA) of the PZ can reflect the histological difference between the two entities. Thirty-six patients with solitary brain tumors (HGGs, n = 17; BMs, n = 19) that underwent CT examinations were retrospectively included in this pilot study. TA of the PZ was analyzed using dedicated software (MaZda version 5). Univariate, multivariate, and receiver operating characteristics analyses were used to identify the best-suited parameters for distinguishing between the two groups. Seven texture parameters were able to differentiate between HGGs and BMs with variable sensitivity (56.67–96.67%) and specificity (69.23–100%) rates. Their combined ability successfully identified HGGs with 77.9–99.2% sensitivity and 75.3–100% specificity. In conclusion, the CT-based TA can be a useful tool for differentiating between primary and secondary malignancies. The TA features indicate a more heterogenous content of the HGGs’ PZ, possibly due to the local infiltration of neoplastic cells.
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Affiliation(s)
- Lucian Mărginean
- Radiology and Medical Imaging, Clinical Sciences Department, “George Emil Palade” University of Medicine, Pharmacy, Science, and Technology, 540139 Targu Mures, Romania;
- Interventional Radiology Department, Târgu Mureș County Emergency Clinical Hospital, 540136 Targu Mures, Romania
| | - Paul Andrei Ștefan
- Interventional Radiology Department, Târgu Mureș County Emergency Clinical Hospital, 540136 Targu Mures, Romania
- Department of Biomedical Imaging and Image-Guided Therapy, General Hospital of Vienna (AKH), Medical University of Vienna, 1090 Vienna, Austria
- Anatomy and Embriology, Morphological Sciences Department, “Iuliu Hațieganu” University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania;
- Radiology and Imaging Department, Cluj County Emergency Clinical Hospital, 400006 Cluj-Napoca, Romania; (A.L.); (C.C.); (P.A.C.)
- Correspondence:
| | - Andrei Lebovici
- Radiology and Imaging Department, Cluj County Emergency Clinical Hospital, 400006 Cluj-Napoca, Romania; (A.L.); (C.C.); (P.A.C.)
- Radiology, Surgical Specialties Department, “Iuliu Hațieganu” University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania
| | - Iulian Opincariu
- Anatomy and Embriology, Morphological Sciences Department, “Iuliu Hațieganu” University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania;
| | - Csaba Csutak
- Radiology and Imaging Department, Cluj County Emergency Clinical Hospital, 400006 Cluj-Napoca, Romania; (A.L.); (C.C.); (P.A.C.)
- Radiology, Surgical Specialties Department, “Iuliu Hațieganu” University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania
| | - Roxana Adelina Lupean
- Histology, Morphological Sciences Department, “Iuliu Hațieganu” University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania;
- Obstetrics and Gynecology Clinic “Dominic Stanca”, Cluj County Emergency Clinical Hospital, 400006 Cluj-Napoca, Romania
| | - Paul Alexandru Coroian
- Radiology and Imaging Department, Cluj County Emergency Clinical Hospital, 400006 Cluj-Napoca, Romania; (A.L.); (C.C.); (P.A.C.)
| | - Bogdan Andrei Suciu
- The First Surgical Clinic, Târgu Mureș County Emergency Clinical Hospital, 540136 Targu Mures, Romania;
- Anatomy, Morphological Sciences Department, “George Emil Palade” University of Medicine, Pharmacy, Science, and Technology, 540139 Targu Mures, Romania
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Treatment of Radiation-Induced Brain Necrosis. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2022; 2021:4793517. [PMID: 34976300 PMCID: PMC8720020 DOI: 10.1155/2021/4793517] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Revised: 11/25/2021] [Accepted: 12/08/2021] [Indexed: 02/07/2023]
Abstract
Radiation-induced brain necrosis (RBN) is a serious complication of intracranial as well as skull base tumors after radiotherapy. In the past, due to the lack of effective treatment, radiation brain necrosis was considered to be progressive and irreversible. With better understanding in histopathology and neuroimaging, the occurrence and development of RBN have been gradually clarified, and new treatment methods are constantly emerging. In recent years, some scholars have tried to treat RBN with bevacizumab, nerve growth factor, and gangliosides and have achieved similar results. Some cases of brain necrosis can be repairable and reversible. We aimed to summarize the incidence, pathogenesis, and treatment of RBN.
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Zhang L, Yao R, Gao J, Tan D, Yang X, Wen M, Wang J, Xie X, Liao R, Tang Y, Chen S, Li Y. An Integrated Radiomics Model Incorporating Diffusion-Weighted Imaging and 18F-FDG PET Imaging Improves the Performance of Differentiating Glioblastoma From Solitary Brain Metastases. Front Oncol 2021; 11:732704. [PMID: 34527594 PMCID: PMC8435895 DOI: 10.3389/fonc.2021.732704] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 08/06/2021] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND The effectiveness of conventional MRI (cMRI)-based radiomics in differentiating glioblastoma (GBM) from solitary brain metastases (SBM) is not satisfactory enough. Therefore, we aimed to develop an integrated radiomics model to improve the performance of differentiating GBM from SBM. METHODS One hundred patients with solitary brain tumors (50 with GBM, 50 with SBM) were retrospectively enrolled and randomly assigned to the training set (n = 80) or validation set (n = 20). A total of 4,424 radiomic features were obtained from contrast-enhanced T1-weighted imaging (CE-T1WI) with the contrast-enhancing and peri-enhancing edema region, T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI)-derived apparent diffusion coefficient (ADC), and 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) images. The partial least squares (PLS) regression with fivefold cross-validation is used to analyze the correlation between different radiomic features and different modalities. The cross-validity analysis was performed to judge whether a new principal component or a new feature dimension can significantly improve the final prediction effect. The principal components with effective interpretation in all radiomic features were projected to a low-dimensional space (2D in this study). The effective features of the new projection mapping were then sent to the random forest classifier to predict the results. The performance of differentiating GBM from SBM was compared between the integrated radiomics model and other radiomics models or nonradiomics methods using the area under the receiver operating characteristics curve (AUC). RESULTS Through the cross-validity analysis of partial least squares, hundreds of radiomic features were projected into a new two-dimensional space to complete the construction of radiomics model. Compared with the combined radiomics model using DWI + 18F-FDG PET (AUC = 0.93, p = 0.014), cMRI + DWI (AUC = 0.89, p = 0.011), cMRI + 8F-FDG PET (AUC = 0.91, p = 0.015), and single radiomics model using cMRI (AUC = 0.85, p = 0.018), DWI (AUC = 0.84, p = 0.017), and 18F-FDG PET (AUC = 0.85, p = 0.421), the integrated radiomics model (AUC = 0.98) showed more efficient diagnostic performance. The integrated radiomics model (AUC = 0.98) also showed significantly better performance than any single ADC, SUV, or TBR parameter (AUC = 0.57-0.71, p < 0.05). The integrated radiomics model showed better performance in the training (AUC = 0.98) and validation (AUC = 0.93) sets than any other models and methods, demonstrating robustness. CONCLUSIONS We developed an integrated radiomics model incorporating DWI and 18F-FDG PET, which improved the performance of differentiating GBM from SBM greatly.
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Affiliation(s)
- Liqiang Zhang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Rui Yao
- College of Computer & Information Science, Southwest University, Chongqing, China
| | - Jueni Gao
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Duo Tan
- College of Computer & Information Science, Southwest University, Chongqing, China
| | - Xinyi Yang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Ming Wen
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jie Wang
- Department of Nuclear Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiangxian Xie
- Department of Radiology, Chongqing United Medical Imaging Center, Chongqing, China
| | - Ruikun Liao
- Department of Radiology, Chongqing General Hospital, Chongqing, China
| | - Yao Tang
- Department of Oncology, People’s Hospital of Chongqing Hechuan, Chongqing, China
| | - Shanxiong Chen
- College of Computer & Information Science, Southwest University, Chongqing, China
| | - Yongmei Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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Martín-Noguerol T, Mohan S, Santos-Armentia E, Cabrera-Zubizarreta A, Luna A. Advanced MRI assessment of non-enhancing peritumoral signal abnormality in brain lesions. Eur J Radiol 2021; 143:109900. [PMID: 34412007 DOI: 10.1016/j.ejrad.2021.109900] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 07/24/2021] [Accepted: 08/03/2021] [Indexed: 12/30/2022]
Abstract
Evaluation of Central Nervous System (CNS) focal lesions has been classically made focusing on the assessment solid or enhancing component. However, the assessment of solitary peripherally enhancing lesions where the differential diagnosis includes High-Grade Gliomas (HGG) and metastasis, is usually challenging. Several studies have tried to address the characteristics of peritumoral non-enhancing areas, for better characterization of these lesions. Peritumoral hyperintense T2/FLAIR signal abnormality predominantly contains infiltrating tumor cells in HGG whereas CNS metastasis induce pure vasogenic edema. In addition, the accurate determination of the real extension of HGG is critical for treatment selection and outcome. Conventional MRI sequences are limited in distinguishing infiltrating neoplasm from vasogenic edema. Advanced MRI sequences like Diffusion Weighted Imaging (DWI), Diffusion Tensor Imaging (DTI), Perfusion Weighted Imaging (PWI) and MR spectroscopy (MRS) have all been utilized for this aim with acceptable results. Other advanced MRI approaches, less explored for this task such as Arterial Spin Labelling (ASL), Diffusion Kurtosis Imaging (DKI), T2 relaxometry or Amide Proton Transfer (APT) are also showning promising results in this scenario. In this article, we will discuss the physiopathological basis of peritumoral T2/FLAIR signal abnormality and review potential applications of advanced MRI sequences for its evaluation.
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Affiliation(s)
| | - Suyash Mohan
- Division of Neuroradiology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA.
| | | | | | - Antonio Luna
- MRI Unit, Radiology Department, HT Medica, Jaén, Spain.
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Su CQ, Chen XT, Duan SF, Zhang JX, You YP, Lu SS, Hong XN. A radiomics-based model to differentiate glioblastoma from solitary brain metastases. Clin Radiol 2021; 76:629.e11-629.e18. [PMID: 34092362 DOI: 10.1016/j.crad.2021.04.012] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 04/21/2021] [Indexed: 11/18/2022]
Abstract
AIM To differentiate glioblastoma (GBM) from solitary brain metastases (MET) using radiomic analysis. MATERIALS AND METHODS Two hundred and fifty-three patients with solitary brain tumours (157 GBM and 98 solitary brain MET) were split into a training cohort (n=178) and a validation cohort (n=77) by stratified sampling using computer-generated random numbers at a ratio of 7:3. After feature extraction, minimum redundancy maximum relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO) were used to build the radiomics signature on the training cohort and validation cohort. Performance was assessed by radiomics score (Rad-score), receiver operating characteristic (ROC) curve, calibration, and clinical usefulness. RESULTS Eleven radiomic features were selected as significant features in the training cohort. The Rad-score was significantly associated with the differentiation between GBM and solitary brain MET (p<0.001) both in the training and validation cohorts. The radiomics signature yielded area under the curve (AUC) values of 0.82 and 0.81 in the training and validation cohorts to distinguish between GBM and solitary brain MET. CONCLUSIONS The radiomics model might be a useful supporting tool for the preoperative differentiation of GBM from solitary brain MET, which could aid pretreatment decision-making.
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Affiliation(s)
- C-Q Su
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210029, China
| | - X-T Chen
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210029, China
| | - S-F Duan
- GE Healthcare China, NO.1, Huatuo Road, Pudong New Town, Shanghai 210000, China
| | - J-X Zhang
- Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210029, China
| | - Y-P You
- Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210029, China
| | - S-S Lu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210029, China.
| | - X-N Hong
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210029, China.
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Priya S, Liu Y, Ward C, Le NH, Soni N, Pillenahalli Maheshwarappa R, Monga V, Zhang H, Sonka M, Bathla G. Machine learning based differentiation of glioblastoma from brain metastasis using MRI derived radiomics. Sci Rep 2021; 11:10478. [PMID: 34006893 PMCID: PMC8131619 DOI: 10.1038/s41598-021-90032-w] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 05/05/2021] [Indexed: 01/19/2023] Open
Abstract
Few studies have addressed radiomics based differentiation of Glioblastoma (GBM) and intracranial metastatic disease (IMD). However, the effect of different tumor masks, comparison of single versus multiparametric MRI (mp-MRI) or select combination of sequences remains undefined. We cross-compared multiple radiomics based machine learning (ML) models using mp-MRI to determine optimized configurations. Our retrospective study included 60 GBM and 60 IMD patients. Forty-five combinations of ML models and feature reduction strategies were assessed for features extracted from whole tumor and edema masks using mp-MRI [T1W, T2W, T1-contrast enhanced (T1-CE), ADC, FLAIR], individual MRI sequences and combined T1-CE and FLAIR sequences. Model performance was assessed using receiver operating characteristic curve. For mp-MRI, the best model was LASSO model fit using full feature set (AUC 0.953). FLAIR was the best individual sequence (LASSO-full feature set, AUC 0.951). For combined T1-CE/FLAIR sequence, adaBoost-full feature set was the best performer (AUC 0.951). No significant difference was seen between top models across all scenarios, including models using FLAIR only, mp-MRI and combined T1-CE/FLAIR sequence. Top features were extracted from both the whole tumor and edema masks. Shape sphericity is an important discriminating feature.
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Affiliation(s)
- Sarv Priya
- Department of Radiology, University of Iowa Hospital and Clinics, 200 Hawkins Drive, Iowa City, IA, 52242, USA.
| | - Yanan Liu
- College of Engineering, University of Iowa, Iowa City, IA, USA
| | - Caitlin Ward
- Department of Biostatistics, University of Iowa, Iowa City, IA, USA
| | - Nam H Le
- College of Engineering, University of Iowa, Iowa City, IA, USA
| | - Neetu Soni
- Department of Radiology, University of Iowa Hospital and Clinics, 200 Hawkins Drive, Iowa City, IA, 52242, USA
| | | | - Varun Monga
- Department of Medicine, University of Iowa Hospitals and Clinics, Iowa City, IA, USA
| | - Honghai Zhang
- College of Engineering, University of Iowa, Iowa City, IA, USA
| | - Milan Sonka
- College of Engineering, University of Iowa, Iowa City, IA, USA
| | - Girish Bathla
- Department of Radiology, University of Iowa Hospital and Clinics, 200 Hawkins Drive, Iowa City, IA, 52242, USA
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Shin I, Kim H, Ahn SS, Sohn B, Bae S, Park JE, Kim HS, Lee SK. Development and Validation of a Deep Learning-Based Model to Distinguish Glioblastoma from Solitary Brain Metastasis Using Conventional MR Images. AJNR Am J Neuroradiol 2021; 42:838-844. [PMID: 33737268 DOI: 10.3174/ajnr.a7003] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 11/13/2020] [Indexed: 12/22/2022]
Abstract
BACKGROUND AND PURPOSE Differentiating glioblastoma from solitary brain metastasis preoperatively using conventional MR images is challenging. Deep learning models have shown promise in performing classification tasks. The diagnostic performance of a deep learning-based model in discriminating glioblastoma from solitary brain metastasis using preoperative conventional MR images was evaluated. MATERIALS AND METHODS Records of 598 patients with histologically confirmed glioblastoma or solitary brain metastasis at our institution between February 2006 and December 2017 were retrospectively reviewed. Preoperative contrast-enhanced T1WI and T2WI were preprocessed and roughly segmented with rectangular regions of interest. A deep neural network was trained and validated using MR images from 498 patients. The MR images of the remaining 100 were used as an internal test set. An additional 143 patients from another tertiary hospital were used as an external test set. The classifications of ResNet-50 and 2 neuroradiologists were compared for their accuracy, precision, recall, F1 score, and area under the curve. RESULTS The areas under the curve of ResNet-50 were 0.889 and 0.835 in the internal and external test sets, respectively. The area under the curve of neuroradiologists 1 and 2 were 0.889 and 0.768 in the internal test set and 0.857 and 0.708 in the external test set, respectively. CONCLUSIONS A deep learning-based model may be a supportive tool for preoperative discrimination between glioblastoma and solitary brain metastasis using conventional MR images.
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Affiliation(s)
- I Shin
- From the Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science (I.S., H.K., S.S.A., B.S., S.-K.L.), Yonsei University College of Medicine, Seoul, Korea
| | - H Kim
- From the Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science (I.S., H.K., S.S.A., B.S., S.-K.L.), Yonsei University College of Medicine, Seoul, Korea
| | - S S Ahn
- From the Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science (I.S., H.K., S.S.A., B.S., S.-K.L.), Yonsei University College of Medicine, Seoul, Korea
| | - B Sohn
- From the Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science (I.S., H.K., S.S.A., B.S., S.-K.L.), Yonsei University College of Medicine, Seoul, Korea
| | - S Bae
- Department of Radiology (S.B.), National Health Insurance Corporation Ilsan Hospital, Goyang, Korea
| | - J E Park
- Department of Radiology and Research Institute of Radiology (J.E.P., H.S.K.), Asan Medical Center, University of Ulsan College of Medicine
| | - H S Kim
- Department of Radiology and Research Institute of Radiology (J.E.P., H.S.K.), Asan Medical Center, University of Ulsan College of Medicine
| | - S-K Lee
- From the Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science (I.S., H.K., S.S.A., B.S., S.-K.L.), Yonsei University College of Medicine, Seoul, Korea
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Meier R, Pahud de Mortanges A, Wiest R, Knecht U. Exploratory Analysis of Qualitative MR Imaging Features for the Differentiation of Glioblastoma and Brain Metastases. Front Oncol 2020; 10:581037. [PMID: 33425734 PMCID: PMC7793795 DOI: 10.3389/fonc.2020.581037] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 11/09/2020] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVES To identify qualitative VASARI (Visually AcceSIble Rembrandt Images) Magnetic Resonance (MR) Imaging features for differentiation of glioblastoma (GBM) and brain metastasis (BM) of different primary tumors. MATERIALS AND METHODS T1-weighted pre- and post-contrast, T2-weighted, and T2-weighted, fluid attenuated inversion recovery (FLAIR) MR images of a total of 239 lesions from 109 patients with either GBM or BM (breast cancer, non-small cell (NSCLC) adenocarcinoma, NSCLC squamous cell carcinoma, small-cell lung cancer (SCLC)) were included. A set of adapted, qualitative VASARI MR features describing tumor appearance and location was scored (binary; 1 = presence of feature, 0 = absence of feature). Exploratory data analysis was performed on binary scores using a combination of descriptive statistics (proportions with 95% binomial confidence intervals), unsupervised methods and supervised methods including multivariate feature ranking using either repeated fitting or recursive feature elimination with Support Vector Machines (SVMs). RESULTS GBMs were found to involve all lobes of the cerebrum with a fronto-occipital gradient, often affected the corpus callosum (32.4%, 95% CI 19.1-49.2), and showed a strong preference for the right hemisphere (79.4%, 95% CI 63.2-89.7). BMs occurred most frequently in the frontal lobe (35.1%, 95% CI 28.9-41.9) and cerebellum (28.3%, 95% CI 22.6-34.8). The appearance of GBMs was characterized by preference for well-defined non-enhancing tumor margin (100%, 89.8-100), ependymal extension (52.9%, 36.7-68.5) and substantially less enhancing foci than BMs (44.1%, 28.9-60.6 vs. 75.1%, 68.8-80.5). Unsupervised and supervised analyses showed that GBMs are distinctively different from BMs and that this difference is driven by definition of non-enhancing tumor margin, ependymal extension and features describing laterality. Differentiation of histological subtypes of BMs was driven by the presence of well-defined enhancing and non-enhancing tumor margins and localization in the vision center. SVM models with optimal hyperparameters led to weighted F1-score of 0.865 for differentiation of GBMs from BMs and weighted F1-score of 0.326 for differentiation of BM subtypes. CONCLUSION VASARI MR imaging features related to definition of non-enhancing margin, ependymal extension, and tumor localization may serve as potential imaging biomarkers to differentiate GBMs from BMs.
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Affiliation(s)
- Raphael Meier
- University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
- Support Center for Advanced Neuroimaging, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Aurélie Pahud de Mortanges
- University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Roland Wiest
- University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
- Support Center for Advanced Neuroimaging, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Urspeter Knecht
- ARTORG Center for Biomedical Research, University of Bern, Bern, Switzerland
- Department of Diagnostic Radiology and Neuroradiology, Regional Hospital Emmental, Burgdorf, Switzerland
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Mao J, Zeng W, Zhang Q, Yang Z, Yan X, Zhang H, Wang M, Yang G, Zhou M, Shen J. Differentiation between high-grade gliomas and solitary brain metastases: a comparison of five diffusion-weighted MRI models. BMC Med Imaging 2020; 20:124. [PMID: 33228564 PMCID: PMC7684933 DOI: 10.1186/s12880-020-00524-w] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Accepted: 11/16/2020] [Indexed: 12/25/2022] Open
Abstract
Background To compare the diagnostic performance of neurite orientation dispersion and density imaging (NODDI), mean apparent propagator magnetic resonance imaging (MAP-MRI), diffusion kurtosis imaging (DKI), diffusion tensor imaging (DTI) and diffusion-weighted imaging (DWI) in distinguishing high-grade gliomas (HGGs) from solitary brain metastases (SBMs). Methods Patients with previously untreated, histopathologically confirmed HGGs (n = 20) or SBMs (n = 21) appearing as a solitary and contrast-enhancing lesion on structural MRI were prospectively recruited to undergo diffusion-weighted MRI. DWI data were obtained using a q-space Cartesian grid sampling procedure and were processed to generate parametric maps by fitting the NODDI, MAP-MRI, DKI, DTI and DWI models. The diffusion metrics of the contrast-enhancing tumor and peritumoral edema were measured. Differences in the diffusion metrics were compared between HGGs and SBMs, followed by receiver operating characteristic (ROC) analysis and the Hanley and McNeill test to determine their diagnostic performances. Results NODDI-based isotropic volume fraction (Viso) and orientation dispersion index (ODI); MAP-MRI-based mean-squared displacement (MSD) and q-space inverse variance (QIV); DKI-generated radial, mean diffusivity and fractional anisotropy (RDk, MDk and FAk); and DTI-generated radial, mean diffusivity and fractional anisotropy (RD, MD and FA) of the contrast-enhancing tumor were significantly different between HGGs and SBMs (p < 0.05). The best single discriminative parameters of each model were Viso, MSD, RDk and RD for NODDI, MAP-MRI, DKI and DTI, respectively. The AUC of Viso (0.871) was significantly higher than that of MSD (0.736), RDk (0.760) and RD (0.733) (p < 0.05). Conclusion NODDI outperforms MAP-MRI, DKI, DTI and DWI in differentiating between HGGs and SBMs. NODDI-based Viso has the highest performance.
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Affiliation(s)
- Jiaji Mao
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, China.,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, China
| | - Weike Zeng
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, China
| | - Qinyuan Zhang
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, China.,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, China
| | - Zehong Yang
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, China
| | - Xu Yan
- MR Scientific Marketing, Siemens Healthcare, No. 278 Zhouzhu Road, Shanghai, 201318, China
| | - Huiting Zhang
- MR Scientific Marketing, Siemens Healthcare, No. 278 Zhouzhu Road, Shanghai, 201318, China
| | - Mengzhu Wang
- MR Scientific Marketing, Siemens Healthcare, No. 278 Zhouzhu Road, Shanghai, 201318, China
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance, Institute of Physics and Electronics Science, East China Normal University, No. 3663 North Zhongshan Road, Shanghai, 200062, China
| | - Minxiong Zhou
- College of Medical Imaging, Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, No. 279 Zhouzhu Road, Shanghai, 201318, China
| | - Jun Shen
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, China. .,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, China.
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Csutak C, Ștefan PA, Lenghel LM, Moroșanu CO, Lupean RA, Șimonca L, Mihu CM, Lebovici A. Differentiating High-Grade Gliomas from Brain Metastases at Magnetic Resonance: The Role of Texture Analysis of the Peritumoral Zone. Brain Sci 2020; 10:brainsci10090638. [PMID: 32947822 PMCID: PMC7565295 DOI: 10.3390/brainsci10090638] [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: 08/12/2020] [Revised: 09/03/2020] [Accepted: 09/14/2020] [Indexed: 11/16/2022] Open
Abstract
High-grade gliomas (HGGs) and solitary brain metastases (BMs) have similar imaging appearances, which often leads to misclassification. In HGGs, the surrounding tissues show malignant invasion, while BMs tend to displace the adjacent area. The surrounding edema produced by the two cannot be differentiated by conventional magnetic resonance (MRI) examinations. Forty-two patients with pathology-proven brain tumors who underwent conventional pretreatment MRIs were retrospectively included (HGGs, n = 16; BMs, n = 26). Texture analysis of the peritumoral zone was performed on the T2-weighted sequence using dedicated software. The most discriminative texture features were selected using the Fisher and the probability of classification error and average correlation coefficients. The ability of texture parameters to distinguish between HGGs and BMs was evaluated through univariate, receiver operating, and multivariate analyses. The first percentile and wavelet energy texture parameters were independent predictors of HGGs (75–87.5% sensitivity, 53.85–88.46% specificity). The prediction model consisting of all parameters that showed statistically significant results at the univariate analysis was able to identify HGGs with 100% sensitivity and 66.7% specificity. Texture analysis can provide a quantitative description of the peritumoral zone encountered in solitary brain tumors, that can provide adequate differentiation between HGGs and BMs.
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Affiliation(s)
- Csaba Csutak
- Radiology and Imaging Department, County Emergency Hospital, Cluj-Napoca, Clinicilor Street, Number 5, Cluj-Napoca, 400006 Cluj, Romania; (C.C.); (L.M.L.); (C.M.M.); (A.L.)
- Radiology, Surgical Specialties Department, “Iuliu Haţieganu” University of Medicine and Pharmacy, Clinicilor Street, number 3–5, Cluj-Napoca, 400006 Cluj, Romania
| | - Paul-Andrei Ștefan
- Radiology and Imaging Department, County Emergency Hospital, Cluj-Napoca, Clinicilor Street, Number 5, Cluj-Napoca, 400006 Cluj, Romania; (C.C.); (L.M.L.); (C.M.M.); (A.L.)
- Anatomy and Embryology, Morphological Sciences Department, “Iuliu Haţieganu” University of Medicine and Pharmacy, Victor Babeș Street, number 8, Cluj-Napoca, 400012 Cluj, Romania
- Correspondence: ; Tel.: +40-743-957-206
| | - Lavinia Manuela Lenghel
- Radiology and Imaging Department, County Emergency Hospital, Cluj-Napoca, Clinicilor Street, Number 5, Cluj-Napoca, 400006 Cluj, Romania; (C.C.); (L.M.L.); (C.M.M.); (A.L.)
- Radiology, Surgical Specialties Department, “Iuliu Haţieganu” University of Medicine and Pharmacy, Clinicilor Street, number 3–5, Cluj-Napoca, 400006 Cluj, Romania
| | - Cezar Octavian Moroșanu
- Department of Neurosurgery, North Bristol Trust, Southmead Hospital, Southmead Road, Westbury on Trym, Bristol BS2 8BJ, UK;
| | - Roxana-Adelina Lupean
- Histology, Morphological Sciences Department, “Iuliu Hațieganu” University of Medicine and Pharmacy, Louis Pasteur Street, number 4, Cluj-Napoca, 400349 Cluj, Romania;
| | - Larisa Șimonca
- Department of Paediatric Surgery, Bristol Royal Hospital for Children, Upper Maudlin Street, Bristol BS2 8BJ, UK;
| | - Carmen Mihaela Mihu
- Radiology and Imaging Department, County Emergency Hospital, Cluj-Napoca, Clinicilor Street, Number 5, Cluj-Napoca, 400006 Cluj, Romania; (C.C.); (L.M.L.); (C.M.M.); (A.L.)
- Histology, Morphological Sciences Department, “Iuliu Hațieganu” University of Medicine and Pharmacy, Louis Pasteur Street, number 4, Cluj-Napoca, 400349 Cluj, Romania;
| | - Andrei Lebovici
- Radiology and Imaging Department, County Emergency Hospital, Cluj-Napoca, Clinicilor Street, Number 5, Cluj-Napoca, 400006 Cluj, Romania; (C.C.); (L.M.L.); (C.M.M.); (A.L.)
- Radiology, Surgical Specialties Department, “Iuliu Haţieganu” University of Medicine and Pharmacy, Clinicilor Street, number 3–5, Cluj-Napoca, 400006 Cluj, Romania
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22
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Bae S, An C, Ahn SS, Kim H, Han K, Kim SW, Park JE, Kim HS, Lee SK. Robust performance of deep learning for distinguishing glioblastoma from single brain metastasis using radiomic features: model development and validation. Sci Rep 2020; 10:12110. [PMID: 32694637 PMCID: PMC7374174 DOI: 10.1038/s41598-020-68980-6] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2020] [Accepted: 07/02/2020] [Indexed: 11/09/2022] Open
Abstract
We evaluated the diagnostic performance and generalizability of traditional machine learning and deep learning models for distinguishing glioblastoma from single brain metastasis using radiomics. The training and external validation cohorts comprised 166 (109 glioblastomas and 57 metastases) and 82 (50 glioblastomas and 32 metastases) patients, respectively. Two-hundred-and-sixty-five radiomic features were extracted from semiautomatically segmented regions on contrast-enhancing and peritumoral T2 hyperintense masks and used as input data. For each of a deep neural network (DNN) and seven traditional machine learning classifiers combined with one of five feature selection methods, hyperparameters were optimized through tenfold cross-validation in the training cohort. The diagnostic performance of the optimized models and two neuroradiologists was tested in the validation cohort for distinguishing glioblastoma from metastasis. In the external validation, DNN showed the highest diagnostic performance, with an area under receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy of 0.956 (95% confidence interval [CI], 0.918–0.990), 90.6% (95% CI, 80.5–100), 88.0% (95% CI, 79.0–97.0), and 89.0% (95% CI, 82.3–95.8), respectively, compared to the best-performing traditional machine learning model (adaptive boosting combined with tree-based feature selection; AUC, 0.890 (95% CI, 0.823–0.947)) and human readers (AUC, 0.774 [95% CI, 0.685–0.852] and 0.904 [95% CI, 0.852–0.951]). The results demonstrated deep learning using radiomic features can be useful for distinguishing glioblastoma from metastasis with good generalizability.
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Affiliation(s)
- Sohi Bae
- Department of Radiology, National Health Insurance Service Ilsan Hospital, Goyang, 10444, Korea
| | - Chansik An
- Department of Radiology, National Health Insurance Service Ilsan Hospital, Goyang, 10444, Korea.,Research and Analysis Team, National Health Insurance Service Ilsan Hospital, Goyang, 10444, Korea
| | - Sung Soo Ahn
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.
| | - Hwiyoung Kim
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.
| | - Kyunghwa Han
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Sang Wook Kim
- School of Biomedical Engineering, Korea University College of Health Science, Seoul, 02841, Korea
| | - Ji Eun Park
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Korea
| | - Ho Sung Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Korea
| | - Seung-Koo Lee
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
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23
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Glioblastomas and brain metastases differentiation following an MRI texture analysis-based radiomics approach. Phys Med 2020; 76:44-54. [PMID: 32593138 DOI: 10.1016/j.ejmp.2020.06.016] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 06/11/2020] [Accepted: 06/15/2020] [Indexed: 12/12/2022] Open
Abstract
PURPOSE To evaluate the potential of 2D texture features extracted from magnetic resonance (MR) images for differentiating brain metastasis (BM) and glioblastomas (GBM) following a radiomics approach. METHODS This retrospective study included 50 patients with BM and 50 with GBM who underwent T1-weighted MRI between December 2010 and January 2017. Eighty-eight rotation-invariant texture features were computed for each segmented lesion using six texture analysis methods. These features were also extracted from the four images obtained after applying the discrete wavelet transform (88 features × 4 images). Three feature selection methods and five predictive models were evaluated. A 5-fold cross-validation scheme was used to randomly split the study group into training (80 patients) and testing (20 patients), repeating the process ten times. Classification was evaluated computing the average area under the receiver operating characteristic curve. Sensibility, specificity and accuracy were also computed. The whole process was tested quantizing the images with different gray-level values to evaluate their influence in the final results. RESULTS Highest classification accuracy was obtained using the original images quantized with 128 gray-levels and a feature selection method based on the p-value. The best overall performance was achieved using a support vector machine model with a subset of 32 features (AUC = 0.896 ± 0.067, sensitivity of 82% and specificity of 80%). Naïve Bayes and k-nearest neighbors models showed also valuable results (AUC ≈ 0.8) with a lower number of features (<13), thus suggesting that these models may be more generalizable when using external validations. CONCLUSION The proposed radiomics MRI approach is able to discriminate between GBM and BM with high accuracy employing a set of 2D texture features, thus helping in the diagnosis of brain lesions in a fast and non-invasive way.
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Differentiation of supratentorial single brain metastasis and glioblastoma by using peri-enhancing oedema region-derived radiomic features and multiple classifiers. Eur Radiol 2020; 30:3015-3022. [PMID: 32006166 DOI: 10.1007/s00330-019-06460-w] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Revised: 08/11/2019] [Accepted: 09/13/2019] [Indexed: 12/15/2022]
Abstract
OBJECTIVE To differentiate supratentorial single brain metastasis (MET) from glioblastoma (GBM) by using radiomic features derived from the peri-enhancing oedema region and multiple classifiers. METHODS One hundred and twenty single brain METs and GBMs were retrospectively reviewed and then randomly divided into a training data set (70%) and validation data set (30%). Quantitative radiomic features of each case were extracted from the peri-enhancing oedema region of conventional MR images. After feature selection, five classifiers were built. Additionally, the combined use of the classifiers was studied. Accuracy, sensitivity, and specificity were used to evaluate the classification performance. RESULTS A total of 321 features were extracted, and 3 features were selected for each case. The 5 classifiers showed an accuracy of 0.70 to 0.76, sensitivity of 0.57 to 0.98, and specificity of 0.43 to 0.93 for the training data set, with an accuracy of 0.56 to 0.64, sensitivity of 0.39 to 0.78, and specificity of 0.50 to 0.89 for the validation data set. When combining the classifiers, the classification performance differed according to the combined mode and the agreement pattern of classifiers, and the greatest benefit was obtained when all the classifiers reached agreement using the same weight and simple majority vote method. CONCLUSIONS Three features derived from the peri-enhancing oedema region had moderate value in differentiating supratentorial single brain MET from GBM with five single classifiers. Combined use of classifiers, like multi-disciplinary team (MDT) consultation, could confer extra benefits, especially for those cases when all classifiers reach agreement. KEY POINTS • Radiomics provides a way to differentiate single brain MET between GBM by using conventional MR images. • The results of classifiers or algorithms themselves are also data, the transformation of the primary data. • Like MDT consultation, the combined use of multiple classifiers may confer extra benefits.
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Discrimination Between Solitary Brain Metastasis and Glioblastoma Multiforme by Using ADC-Based Texture Analysis: A Comparison of Two Different ROI Placements. Acad Radiol 2019; 26:1466-1472. [PMID: 30770161 DOI: 10.1016/j.acra.2019.01.010] [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: 10/15/2018] [Revised: 01/05/2019] [Accepted: 01/15/2019] [Indexed: 12/22/2022]
Abstract
RATIONALE AND OBJECTIVES To explore the value of texture analysis based on the apparent diffusion coefficient (ADC) value and the effect of region of interest (ROI) placements in distinguishing glioblastoma multiforme (GBM) from solitary brain metastasis (sMET). MATERIALS AND METHODS Sixty-two patients with pathologically confirmed GBM (n = 36) and sMET (n = 26) were retrospectively included. All patients underwent diffusion-weighted imaging with b values of 0 and 1000 s/mm2, and the ADC maps were generated automatically. ROIs were placed on the largest whole single-slice tumor (ROI1) and the enhanced solid portion (ROI2) of the ADC maps, respectively. The texture feature metrics of the histogram and gray-level co-occurrence matrix were then extracted by using in-house software. The parameters of the texture analysis were compared between GBM and sMET, using the Mann-Whitney U test. A receiver operating characteristic (ROC) curve analysis was performed to determine the best parameters for distinguishing between GBM from sMET. RESULTS Homogeneity and the inverse difference moment (IDM) of GBM were significantly higher than those of sMET in both ROIs (ROI1, p = 0.014 for homogeneity and p = 0.048 for IDM; ROI2, p< 0.001 for homogeneity and p = 0.029 for IDM). According to the ROC curve analysis, the area under the ROC curve (AUC) of homogeneity in ROI1 (AUC, 0.682, sensitivity, 72.2%, specificity, 61.5%) was significantly lower than that of ROI2 (AUC, 0.886, sensitivity, 83.3%, specificity, 76.9%; p= 0.012), whereas the IDM showed no statistical significance between two ROIs (p> 0.05). CONCLUSION The ADC-based texture analysis can help differentiate GBM from sMET, and the ROI on the solid portion would be recommended to calculate the ADC-based texture metrics.
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26
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Petrujkić K, Milošević N, Rajković N, Stanisavljević D, Gavrilović S, Dželebdžić D, Ilić R, Di Ieva A, Maksimović R. Computational quantitative MR image features - a potential useful tool in differentiating glioblastoma from solitary brain metastasis. Eur J Radiol 2019; 119:108634. [PMID: 31473463 DOI: 10.1016/j.ejrad.2019.08.003] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Revised: 07/28/2019] [Accepted: 08/05/2019] [Indexed: 01/31/2023]
Abstract
PURPOSE Glioblastomas (GBM) and metastases are the most frequent malignant brain tumors in the adult population. Their presentation on conventional MRI is quite similar, but treatment strategy and prognosis are substantially different. Even with advanced MR techniques, in some cases diagnostic uncertainty remains. The main objective of this study was to determine whether fractal, texture, or both MR image analyses could aid in differentiating glioblastoma from solitary brain metastasis. METHOD In a retrospective study of 55 patients (30 glioblastomas and 25 solitary metastases) who underwent T2W/SWI/CET1 MRI, quantitative parameters of fractal and texture analysis were estimated, using box-counting and gray level co-occurrence matrix (GLCM) methods. RESULTS All five GLCM parameters obtained from T2W images showed significant difference between glioblastomas and solitary metastases, as well as on CET1 images except correlation (SCOR), contrary to SWI images which showed different values of two parameters (angular second moment-SASM and contrast-SCON). Only three fractal features (binary box dimension-Dbin, normalized box dimension-Dnorm and lacunarity-λ) measured on T2W and Dnorm measured on CET1 images significantly differed GBMs from solitary metastases. The highest sensitivity and specificity were obtained from inverse difference moment (SIDM) on T2W and SIDM on CET1 images, respectively. Combination of several GLCM parameters yielded better results. The processing of T2W images provided the most significantly different parameters between the groups, followed by CET1 and SWI images. CONCLUSIONS Computational-aided quantitative image analysis may potentially improve diagnostic accuracy. According to our results texture features are more significant than fractal-based features in differentiation glioblastoma from solitary metastasis.
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Affiliation(s)
- Katarina Petrujkić
- Clinical Centre of Serbia, Centre for Radiology and Magnetic Resonance, Pasterova 2, Belgrade 11000, Serbia.
| | - Nebojša Milošević
- Department of Biophysics, School of Medicine, University of Belgrade, Višegradska 26/2, Belgrade 11000, Serbia
| | - Nemanja Rajković
- Department of Biophysics, School of Medicine, University of Belgrade, Višegradska 26/2, Belgrade 11000, Serbia
| | - Dejana Stanisavljević
- Department for Medical Statistics, School of Medicine, University of Belgrade, Dr Subotića 8, Belgrade 11000, Serbia
| | - Svetlana Gavrilović
- Clinical Centre of Serbia, Centre for Radiology and Magnetic Resonance, Pasterova 2, Belgrade 11000, Serbia
| | - Dragana Dželebdžić
- Clinical Centre of Serbia, Centre for Radiology and Magnetic Resonance, Pasterova 2, Belgrade 11000, Serbia
| | - Rosanda Ilić
- Department of Neurosurgery, School of Medicine, University of Belgrade, Dr Subotića 8, Belgrade 11000, Serbia; Clinical Centre of Serbia, Clinical for Neurosurgery, Dr Koste Todorovića 54, 11000 Belgrade, Serbia
| | - Antonio Di Ieva
- Department of Clinical Medicine, Faculty of Medicine and Health Science, Neurosurgery Unit, Macquarie University, 2 Technology Place, Macquarie University, Sydney, NSW 2109, Australia
| | - Ružica Maksimović
- Clinical Centre of Serbia, Centre for Radiology and Magnetic Resonance, Pasterova 2, Belgrade 11000, Serbia; Department of Radiology, School of Medicine, University of Belgrade, Dr Subotića 8, Belgrade 11000, Serbia
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Differentiating Glioblastomas from Solitary Brain Metastases Using Arterial Spin Labeling Perfusion− and Diffusion Tensor Imaging−Derived Metrics. World Neurosurg 2019; 127:e593-e598. [DOI: 10.1016/j.wneu.2019.03.213] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2018] [Revised: 03/19/2019] [Accepted: 03/20/2019] [Indexed: 12/20/2022]
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Cerebral Radiation Necrosis: Incidence, Pathogenesis, Diagnostic Challenges, and Future Opportunities. Curr Oncol Rep 2019; 21:66. [PMID: 31218455 DOI: 10.1007/s11912-019-0818-y] [Citation(s) in RCA: 69] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
PURPOSE OF REVIEW Cerebral radiation necrosis (CRN) is a major dose-limiting adverse event of radiotherapy. The incidence rate of RN varies with the radiotherapy modality, total dose, dose fractionation, and the nature of the lesion being targeted. In addition to these known and controllable features, there is a stochastic component to the occurrence of CRN-the genetic profile of the host or the lesion and their role in the development of CRN. RECENT FINDINGS Recent studies provide some insight into the genetic mechanisms underlying radiation-induced brain injury. In addition to these incompletely understood host factors, the diagnostic criteria for CRN using structural and functional imaging are also not clear, though multiple structural and functional imaging modalities exist, a combination of which may prove to be the ideal diagnostic imaging approach. As the utilization of novel molecular therapies and immunotherapy increases, the incidence of CNR is expected to increase and its diagnosis will become more challenging. Tissue biopsies can be insensitive and suffer from sampling biases and procedural risks. Liquid biopsies represent a promising, accurate, and non-invasive diagnostic strategy, though this modality is currently in its infancy. A better understanding of the pathogenesis of CRN will expand and optimize the diagnosis and management of CRN by better utilizing existing treatment options including bevacizumab, pentoxifylline, hyperbaric oxygen therapy, and laser interstitial thermal therapy.
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Finneran MM, Georges J, Kakareka M, Moncman R, Enriquez M, Siegal T, Kubicek G, Turtz A, Yocom S, Goldman HW, Barrese J. Epithelioid glioblastoma presenting as aphasia in a young adult with ovarian cancer: A case report. Clin Case Rep 2019; 7:821-825. [PMID: 30997093 PMCID: PMC6452522 DOI: 10.1002/ccr3.2088] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Revised: 12/11/2018] [Accepted: 12/18/2018] [Indexed: 12/23/2022] Open
Abstract
Our patient's clinical history and preoperative radiographic evaluation suggested central nervous system (CNS) metastatic disease. Ultimately, final pathology revealed epithelioid glioblastoma (eGBM), a newly classified CNS primary tumor. This reinforces the importance of direct tissue sampling and including eGBM on the differential for young patients with histories of systemic cancer presenting with new CNS lesions.
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Affiliation(s)
- Megan M. Finneran
- Department of Clinical MedicineChicago College of Osteopathic MedicineChicagoIllinois
| | - Joseph Georges
- Department of NeurosurgeryPhiladelphia College of Osteopathic MedicinePhiladelphiaPennsylvania
- Department of NeurosurgeryCooper University HospitalCamdenNew Jersey
| | - Michael Kakareka
- Department of NeurosurgeryPhiladelphia College of Osteopathic MedicinePhiladelphiaPennsylvania
- Department of NeurosurgeryCooper University HospitalCamdenNew Jersey
| | - Ryan Moncman
- Department of NeurosurgeryPhiladelphia College of Osteopathic MedicinePhiladelphiaPennsylvania
- Department of NeurosurgeryCooper University HospitalCamdenNew Jersey
| | - Miriam Enriquez
- Department of PathologyCooper University HospitalCamdenNew Jersey
| | - Todd Siegal
- Department of RadiologyCooper University HospitalCamdenNew Jersey
| | - Gregory Kubicek
- Department of Radiation OncologyCooper University HospitalCamdenNew Jersey
| | - Alan Turtz
- Department of NeurosurgeryCooper University HospitalCamdenNew Jersey
| | - Steven Yocom
- Department of NeurosurgeryPhiladelphia College of Osteopathic MedicinePhiladelphiaPennsylvania
- Department of NeurosurgeryCooper University HospitalCamdenNew Jersey
| | - H. Warren Goldman
- Department of NeurosurgeryCooper University HospitalCamdenNew Jersey
| | - James Barrese
- Department of NeurosurgeryCooper University HospitalCamdenNew Jersey
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Solid stress in brain tumours causes neuronal loss and neurological dysfunction and can be reversed by lithium. Nat Biomed Eng 2019; 3:230-245. [PMID: 30948807 PMCID: PMC6452896 DOI: 10.1038/s41551-018-0334-7] [Citation(s) in RCA: 95] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2017] [Accepted: 11/25/2018] [Indexed: 12/12/2022]
Abstract
The compression of brain tissue by a tumour mass is believed to be a major cause of the clinical symptoms seen in patients with brain cancer. However, the biological consequences of these physical stresses on brain tissue are unknown. Here, via imaging studies in patients and by using mouse models of human brain tumours, we show that a subgroup of primary and metastatic brain tumours, classified as nodular on the basis of their growth pattern, exert solid stress on the surrounding brain tissue, causing a decrease in local vascular perfusion as well as neuronal death and impaired function. We demonstrate a causal link between solid stress and neurological dysfunction by applying and removing cerebral compression, which respectively mimic the mechanics of tumour growth and of surgical resection. We also show that, in mice, treatment with lithium reduces solid-stress-induced neuronal death and improves motor coordination. Our findings indicate that brain-tumour-generated solid stress impairs neurological function in patients, and that lithium as a therapeutic intervention could counter these effects.
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Yamamoto J, Kakeda S, Shimajiri S, Nakano Y, Saito T, Ide S, Moriya J, Korogi Y, Nishizawa S. Evaluation of Peritumoral Brain Parenchyma Using Contrast-Enhanced 3D Fast Imaging Employing Steady-State Acquisition at 3T for Differentiating Metastatic Brain Tumors and Glioblastomas. World Neurosurg 2018; 120:e719-e729. [PMID: 30165229 DOI: 10.1016/j.wneu.2018.08.147] [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: 01/17/2018] [Revised: 08/17/2018] [Accepted: 08/18/2018] [Indexed: 10/28/2022]
Abstract
BACKGROUND Metastatic brain tumors and glioblastomas are the 2 of the most common brain neoplasms in adults. However, distinguishing solitary metastatic brain tumors from glioblastomas on conventional magnetic resonance imaging remains particularly challenging. Thus, we aimed to retrospectively assess the role of contrast-enhanced fast imaging employing steady-state acquisition (CE-FIESTA) imaging in distinguishing between metastatic brain tumors and glioblastomas. MATERIALS AND METHODS Forty-three patients with metastatic brain tumors and 14 patients with glioblastomas underwent conventional magnetic resonance imaging and CE-FIESTA before surgery. First, 1 neuroradiologist and 1 neurosurgeon classified the CE-FIESTA findings for the peritumoral brain parenchyma by consensus. Next, the 2 neuroradiologists performed an observer performance study comparing tumor shape classification (smooth or irregular margins), a classic imaging finding, with the CE-FIESTA classification of the peritumoral brain parenchyma. RESULTS The CE-FIESTA findings for the peritumoral brain parenchyma were classified as follows: type A, no hyperintense rim; type B, partial hyperintense rim; and type C, extended hyperintense rim. With regard to the diagnosis of metastatic brain tumors, the observer performance study demonstrated that the mean sensitivity, specificity, and accuracy of an extended hyperintense rim classification (type C) on CE-FIESTA images were 95.3%, 85.7%, and 93.0%, respectively. The accuracy of the CE-FIESTA classification was significantly higher than that of the tumor shape classification. CONCLUSIONS CE-FIESTA images may provide useful information for distinguishing metastatic brain tumors from glioblastomas, especially when focusing on differences in the peritumoral brain parenchyma.
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Affiliation(s)
- Junkoh Yamamoto
- Department of Neurosurgery, University of Occupational and Environmental Health, Kitakyushu, Japan.
| | - Shingo Kakeda
- Department of Radiology, University of Occupational and Environmental Health, Kitakyushu, Japan
| | - Shohei Shimajiri
- Department of Surgical Pathology, University of Occupational and Environmental Health, Kitakyushu, Japan
| | - Yoshiteru Nakano
- Department of Neurosurgery, University of Occupational and Environmental Health, Kitakyushu, Japan
| | - Takeshi Saito
- Department of Neurosurgery, University of Occupational and Environmental Health, Kitakyushu, Japan
| | - Satoru Ide
- Department of Radiology, University of Occupational and Environmental Health, Kitakyushu, Japan
| | - Junji Moriya
- Department of Radiology, University of Occupational and Environmental Health, Kitakyushu, Japan
| | - Yukunori Korogi
- Department of Radiology, University of Occupational and Environmental Health, Kitakyushu, Japan
| | - Shigeru Nishizawa
- Department of Neurosurgery, University of Occupational and Environmental Health, Kitakyushu, Japan
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Evolutionary intelligence for brain tumor recognition from MRI images: a critical study and review. EVOLUTIONARY INTELLIGENCE 2018. [DOI: 10.1007/s12065-018-0156-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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Vamvakas A, Tsougos I, Arikidis N, Kapsalaki E, Fountas K, Fezoulidis I, Costaridou L. Exploiting morphology and texture of 3D tumor models in DTI for differentiating glioblastoma multiforme from solitary metastasis. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2018.02.014] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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Lin L, Xue Y, Duan Q, Sun B, Lin H, Huang X, Chen X. The role of cerebral blood flow gradient in peritumoral edema for differentiation of glioblastomas from solitary metastatic lesions. Oncotarget 2018; 7:69051-69059. [PMID: 27655705 PMCID: PMC5356611 DOI: 10.18632/oncotarget.12053] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2016] [Accepted: 09/02/2016] [Indexed: 11/25/2022] Open
Abstract
OBJECTIVE Differentiation of glioblastomas from solitary brain metastases using conventional MRI remains an important unsolved problem. In this study, we introduced the conception of the cerebral blood flow (CBF) gradient in peritumoral edema-the difference in CBF values from the proximity of the enhancing tumor to the normal-appearing white matter, and investigated the contribution of perfusion metrics on the discrimination of glioblastoma from a metastatic lesion. MATERIALS AND METHODS Fifty-two consecutive patients with glioblastoma or a solitary metastatic lesion underwent three-dimensional arterial spin labeling (3D-ASL) before surgical resection. The CBF values were measured in the peritumoral edema (near: G1; Intermediate: G2; Far: G3). The CBF gradient was calculated as the subtractions CBFG1 -CBFG3, CBFG1 - CBFG2 and CBFG2 - CBFG3. A receiver operating characteristic (ROC) curve analysis was used to seek for the best cutoff value permitting discrimination between these two tumors. RESULTS The absolute/related CBF values and the CBF gradient in the peritumoral regions of glioblastomas were significantly higher than those in metastases(P < 0.038). ROC curve analysis reveals, a cutoff value of 1.92 ml/100g for the CBF gradient of CBFG1 -CBFG3 generated the best combination of sensitivity (92.86%) and specificity (100.00%) for distinguishing between a glioblastoma and metastasis. CONCLUSION The CBF gradient in peritumoral edema appears to be a more promising ASL perfusion metrics in differentiating high grade glioma from a solitary metastasis.
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Affiliation(s)
- Lin Lin
- Department of Radiology, Union Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Yunjing Xue
- Department of Radiology, Union Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Qing Duan
- Department of Radiology, Union Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Bin Sun
- Department of Radiology, Union Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Hailong Lin
- Department of Radiology, Union Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Xinming Huang
- Department of Radiology, Union Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Xiaodan Chen
- Department of Radiology, Fujian Provincial Cancer Hospital, Fuzhou, Fujian, China
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Feng R, Loewenstern J, Aggarwal A, Pawha P, Gilani A, Iloreta AM, Bakst R, Miles B, Bederson J, Costa A, Gupta V, Shrivastava RK. Cerebral Radiation Necrosis: An Analysis of Clinical and Quantitative Imaging and Volumetric Features. World Neurosurg 2018; 111:e485-e494. [DOI: 10.1016/j.wneu.2017.12.104] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2017] [Revised: 12/14/2017] [Accepted: 12/15/2017] [Indexed: 10/18/2022]
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Yu H, Lou H, Zou T, Wang X, Jiang S, Huang Z, Du Y, Jiang C, Ma L, Zhu J, He W, Rui Q, Zhou J, Wen Z. Applying protein-based amide proton transfer MR imaging to distinguish solitary brain metastases from glioblastoma. Eur Radiol 2017; 27:4516-4524. [PMID: 28534162 PMCID: PMC5744886 DOI: 10.1007/s00330-017-4867-z] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2017] [Revised: 03/26/2017] [Accepted: 04/26/2017] [Indexed: 02/05/2023]
Abstract
OBJECTIVES To determine the utility of amide proton transfer-weighted (APTw) MR imaging in distinguishing solitary brain metastases (SBMs) from glioblastomas (GBMs). METHODS Forty-five patients with SBMs and 43 patients with GBMs underwent conventional and APT-weighted sequences before clinical intervention. The APTw parameters and relative APTw (rAPTw) parameters in the tumour core and the peritumoral brain zone (PBZ) were obtained and compared between SBMs and GBMs. The receiver-operating characteristic (ROC) curve was used to assess the best parameter for distinguishing between the two groups. RESULTS The APTwmax, APTwmin, APTwmean, rAPTwmax, rAPTwmin or rAPTwmean values in the tumour core were not significantly different between the SBM and GBM groups (P = 0.141, 0.361, 0.221, 0.305, 0.578 and 0.448, respectively). However, the APTwmax, APTwmin, APTwmean, rAPTwmax, rAPTwmin or rAPTwmean values in the PBZ were significantly lower in the SBM group than in the GBM group (P < 0.001). The APTwmin values had the highest area under the ROC curve 0.905 and accuracy 85.2% in discriminating between the two neoplasms. CONCLUSION As a noninvasive imaging method, APT-weighted MR imaging can be used to distinguish SBMs from GBMs. KEY POINTS • APTw values in the tumour core were not different between SBMs and GBMs. • APTw values in peritumoral brain zone were lower in SBMs than in GBMs. • The APTw min was the best parameter to distinguish SBMs from GBMs.
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Affiliation(s)
- Hao Yu
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Gongye Road M No.253, Haizhu District, Guangzhou, Guangdong, 510282, China
| | - Huiling Lou
- Department of Geriatrics, The First People' Hospital of Guangzhou, Guangzhou, Guangdong, 510180, China
| | - Tianyu Zou
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Gongye Road M No.253, Haizhu District, Guangzhou, Guangdong, 510282, China
| | - Xianlong Wang
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Gongye Road M No.253, Haizhu District, Guangzhou, Guangdong, 510282, China
| | - Shanshan Jiang
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Gongye Road M No.253, Haizhu District, Guangzhou, Guangdong, 510282, China
- Division of MR Research, Department of Radiology, Johns Hopkins University School of Medicine, 600N. Wolfe Street, Park 336, Baltimore, MD, 21287, USA
| | - Zhongqing Huang
- Department of Medical Image Center, Yuebei People's Hospital, Shantou University Medical College, Shantou, Guangdong, 515041, China
| | - Yongxing Du
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Gongye Road M No.253, Haizhu District, Guangzhou, Guangdong, 510282, China
| | - Chunxiu Jiang
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Gongye Road M No.253, Haizhu District, Guangzhou, Guangdong, 510282, China
| | - Ling Ma
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Gongye Road M No.253, Haizhu District, Guangzhou, Guangdong, 510282, China
| | - Jianbin Zhu
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Gongye Road M No.253, Haizhu District, Guangzhou, Guangdong, 510282, China
| | - Wen He
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Gongye Road M No.253, Haizhu District, Guangzhou, Guangdong, 510282, China
| | - Qihong Rui
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Gongye Road M No.253, Haizhu District, Guangzhou, Guangdong, 510282, China
| | - Jianyuan Zhou
- Division of MR Research, Department of Radiology, Johns Hopkins University School of Medicine, 600N. Wolfe Street, Park 336, Baltimore, MD, 21287, USA
| | - Zhibo Wen
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Gongye Road M No.253, Haizhu District, Guangzhou, Guangdong, 510282, China.
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Abstract
Modern imaging techniques, particularly functional imaging techniques that interrogate some specific aspect of underlying tumor biology, have enormous potential in neuro-oncology for disease detection, grading, and tumor delineation to guide biopsy and resection; monitoring treatment response; and targeting radiotherapy. This brief review considers the role of magnetic resonance imaging and spectroscopy, and positron emission tomography in these areas and discusses the factors that limit translation of new techniques to the clinic, in particular, the cost and difficulties associated with validation in multicenter clinical trials.
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Affiliation(s)
- Kevin M Brindle
- Kevin M. Brindle, Richard J. Mair, and Alan J. Wright, Cancer Research UK Cambridge Institute, Cambridge; David Y. Lewis, Cancer Research UK Beatson Institute, Glasgow, United Kingdom; José L. Izquierdo-García, Fundación Centro Nacional de Investigaciones Cardiovasculares Carlos III and Centro de Investigación Biomédica en Red Enfermedades Respiratorias, Madrid, Spain
| | - José L Izquierdo-García
- Kevin M. Brindle, Richard J. Mair, and Alan J. Wright, Cancer Research UK Cambridge Institute, Cambridge; David Y. Lewis, Cancer Research UK Beatson Institute, Glasgow, United Kingdom; José L. Izquierdo-García, Fundación Centro Nacional de Investigaciones Cardiovasculares Carlos III and Centro de Investigación Biomédica en Red Enfermedades Respiratorias, Madrid, Spain
| | - David Y Lewis
- Kevin M. Brindle, Richard J. Mair, and Alan J. Wright, Cancer Research UK Cambridge Institute, Cambridge; David Y. Lewis, Cancer Research UK Beatson Institute, Glasgow, United Kingdom; José L. Izquierdo-García, Fundación Centro Nacional de Investigaciones Cardiovasculares Carlos III and Centro de Investigación Biomédica en Red Enfermedades Respiratorias, Madrid, Spain
| | - Richard J Mair
- Kevin M. Brindle, Richard J. Mair, and Alan J. Wright, Cancer Research UK Cambridge Institute, Cambridge; David Y. Lewis, Cancer Research UK Beatson Institute, Glasgow, United Kingdom; José L. Izquierdo-García, Fundación Centro Nacional de Investigaciones Cardiovasculares Carlos III and Centro de Investigación Biomédica en Red Enfermedades Respiratorias, Madrid, Spain
| | - Alan J Wright
- Kevin M. Brindle, Richard J. Mair, and Alan J. Wright, Cancer Research UK Cambridge Institute, Cambridge; David Y. Lewis, Cancer Research UK Beatson Institute, Glasgow, United Kingdom; José L. Izquierdo-García, Fundación Centro Nacional de Investigaciones Cardiovasculares Carlos III and Centro de Investigación Biomédica en Red Enfermedades Respiratorias, Madrid, Spain
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Lahmiri S. Glioma detection based on multi-fractal features of segmented brain MRI by particle swarm optimization techniques. Biomed Signal Process Control 2017. [DOI: 10.1016/j.bspc.2016.07.008] [Citation(s) in RCA: 97] [Impact Index Per Article: 13.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Chaddad A, Desrosiers C, Hassan L, Tanougast C. A quantitative study of shape descriptors from glioblastoma multiforme phenotypes for predicting survival outcome. Br J Radiol 2016; 89:20160575. [PMID: 27781499 DOI: 10.1259/bjr.20160575] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Abstract
OBJECTIVE Predicting the survival outcome of patients with glioblastoma multiforme (GBM) is of key importance to clinicians for selecting the optimal course of treatment. The goal of this study was to evaluate the usefulness of geometric shape features, extracted from MR images, as a potential non-invasive way to characterize GBM tumours and predict the overall survival times of patients with GBM. METHODS The data of 40 patients with GBM were obtained from the Cancer Genome Atlas and Cancer Imaging Archive. The T1 weighted post-contrast and fluid-attenuated inversion-recovery volumes of patients were co-registered and segmented into delineate regions corresponding to three GBM phenotypes: necrosis, active tumour and oedema/invasion. A set of two-dimensional shape features were then extracted slicewise from each phenotype region and combined over slices to describe the three-dimensional shape of these phenotypes. Thereafter, a Kruskal-Wallis test was employed to identify shape features with significantly different distributions across phenotypes. Moreover, a Kaplan-Meier analysis was performed to find features strongly associated with GBM survival. Finally, a multivariate analysis based on the random forest model was used for predicting the survival group of patients with GBM. RESULTS Our analysis using the Kruskal-Wallis test showed that all but one shape feature had statistically significant differences across phenotypes, with p-value < 0.05, following Holm-Bonferroni correction, justifying the analysis of GBM tumour shapes on a per-phenotype basis. Furthermore, the survival analysis based on the Kaplan-Meier estimator identified three features derived from necrotic regions (i.e. Eccentricity, Extent and Solidity) that were significantly correlated with overall survival (corrected p-value < 0.05; hazard ratios between 1.68 and 1.87). In the multivariate analysis, features from necrotic regions gave the highest accuracy in predicting the survival group of patients, with a mean area under the receiver-operating characteristic curve (AUC) of 63.85%. Combining the features of all three phenotypes increased the mean AUC to 66.99%, suggesting that shape features from different phenotypes can be used in a synergic manner to predict GBM survival. CONCLUSION Results show that shape features, in particular those extracted from necrotic regions, can be used effectively to characterize GBM tumours and predict the overall survival of patients with GBM. Advances in knowledge: Simple volumetric features have been largely used to characterize the different phenotypes of a GBM tumour (i.e. active tumour, oedema and necrosis). This study extends previous work by considering a wide range of shape features, extracted in different phenotypes, for the prediction of survival in patients with GBM.
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Affiliation(s)
- Ahmad Chaddad
- 1 Laboratory for Imagery, Vision and Artificial Intelligence, University of Québec, École de Technologie Supérieure, Montréal, QC, Canada.,2 Laboratory of Conception, Optimization and Modeling of Systems, University of Lorraine, Metz, Lorraine, France
| | - Christian Desrosiers
- 1 Laboratory for Imagery, Vision and Artificial Intelligence, University of Québec, École de Technologie Supérieure, Montréal, QC, Canada
| | - Lama Hassan
- 1 Laboratory for Imagery, Vision and Artificial Intelligence, University of Québec, École de Technologie Supérieure, Montréal, QC, Canada.,2 Laboratory of Conception, Optimization and Modeling of Systems, University of Lorraine, Metz, Lorraine, France
| | - Camel Tanougast
- 2 Laboratory of Conception, Optimization and Modeling of Systems, University of Lorraine, Metz, Lorraine, France
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Miquelini L, Pérez Akly M, Funes J, Besada C. Usefulness of the apparent diffusion coefficient for the evaluation of the white matter to differentiate between glioblastoma and brain metastases. RADIOLOGIA 2016. [DOI: 10.1016/j.rxeng.2016.05.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Miquelini LA, Pérez Akly MS, Funes JA, Besada CH. Usefulness of the apparent diffusion coefficient for the evaluation of the white matter to differentiate between glioblastoma and brain metastases. RADIOLOGIA 2015; 58:207-13. [PMID: 26655126 DOI: 10.1016/j.rx.2015.10.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2015] [Revised: 09/24/2015] [Accepted: 10/08/2015] [Indexed: 10/22/2022]
Abstract
OBJECTIVE To determine whether there are significant differences in the apparent diffusion coefficient (ADC) between the apparently normal peritumor white matter surrounding glioblastomas and that surrounding brain metastases. MATERIAL AND METHODS We retrospectively reviewed 42 patients with histologically confirmed glioblastomas and 42 patients with a single cerebral metastasis. We measured the signal intensity in the apparently normal peritumor white matter and in the abnormal peritumor white matter on the ADC maps. We used mean ADC values in the contralateral occipital white matter as a reference from which to design normalized ADC indices. We compared mean values between the two tumor types. We calculated the area under the receiver operator characteristic curve and estimated the sensitivity and specificity of the measurements taken. RESULTS Supratentorial lesions and compromise of the corpus callosum were more common in patients with glioblastoma than in patients with brain metastases. The maximum diameter of the enhanced area after injection of a contrast agent was greater in the glioblastomas (p<0.001). The minimum ADC value measured in the apparently normal peritumor white matter was higher for the glioblastomas than for the metastases (p=0.002). Significant differences in the ADC index were found only for the minimum ADC value in apparently normal peritumor white matter. The sensitivity and specificity were less than 70% for all variables analyzed. CONCLUSIONS There are differences in the ADC values of apparently normal peritumor white matter between glioblastomas and cerebral metastases, but the magnitude of these differences is slight and the application of these differences in clinical practice is still limited.
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Affiliation(s)
- L A Miquelini
- Área de Neurorradiología, Servicio de Diagnóstico por Imágenes, Hospital Italiano de Buenos Aires, Ciudad Autónoma de Buenos Aires, Argentina.
| | - M S Pérez Akly
- Área de Neurorradiología, Servicio de Diagnóstico por Imágenes, Hospital Italiano de Buenos Aires, Ciudad Autónoma de Buenos Aires, Argentina
| | - J A Funes
- Área de Neurorradiología, Servicio de Diagnóstico por Imágenes, Hospital Italiano de Buenos Aires, Ciudad Autónoma de Buenos Aires, Argentina
| | - C H Besada
- Área de Neurorradiología, Servicio de Diagnóstico por Imágenes, Hospital Italiano de Buenos Aires, Ciudad Autónoma de Buenos Aires, Argentina
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Julià-Sapé M, Griffiths JR, Tate AR, Howe FA, Acosta D, Postma G, Underwood J, Majós C, Arús C. Classification of brain tumours from MR spectra: the INTERPRET collaboration and its outcomes. NMR IN BIOMEDICINE 2015; 28:1772-1787. [PMID: 26768492 DOI: 10.1002/nbm.3439] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2014] [Revised: 07/15/2015] [Accepted: 10/01/2015] [Indexed: 06/05/2023]
Abstract
The INTERPRET project was a multicentre European collaboration, carried out from 2000 to 2002, which developed a decision-support system (DSS) for helping neuroradiologists with no experience of MRS to utilize spectroscopic data for the diagnosis and grading of human brain tumours. INTERPRET gathered a large collection of MR spectra of brain tumours and pseudo-tumoural lesions from seven centres. Consensus acquisition protocols, a standard processing pipeline and strict methods for quality control of the aquired data were put in place. Particular emphasis was placed on ensuring the diagnostic certainty of each case, for which all cases were evaluated by a clinical data validation committee. One outcome of the project is a database of 304 fully validated spectra from brain tumours, pseudotumoural lesions and normal brains, along with their associated images and clinical data, which remains available to the scientific and medical community. The second is the INTERPRET DSS, which has continued to be developed and clinically evaluated since the project ended. We also review here the results of the post-INTERPRET period. We evaluate the results of the studies with the INTERPRET database by other consortia or research groups. A summary of the clinical evaluations that have been performed on the post-INTERPRET DSS versions is also presented. Several have shown that diagnostic certainty can be improved for certain tumour types when the INTERPRET DSS is used in conjunction with conventional radiological image interpretation. About 30 papers concerned with the INTERPRET single-voxel dataset have so far been published. We discuss stengths and weaknesses of the DSS and the lessons learned. Finally we speculate on how the INTERPRET concept might be carried into the future.
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Affiliation(s)
- Margarida Julià-Sapé
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Cerdanyola del Vallès, Spain
- Departament de Bioquímica i Biologia Molecular, Unitat de Bioquímica de Biociències, Universitat Autònoma de Barcelona (UAB), Cerdanyola del Vallès, Spain
- Institut de Biotecnologia i de Biomedicina (IBB), Universitat Autònoma de Barcelona (UAB), Cerdanyola del Vallès, Spain
| | | | - A Rosemary Tate
- School of Informatics, University of Sussex, Falmer, Brighton, UK
| | - Franklyn A Howe
- Cardiovascular and Cell Sciences Research Institute, St George's, University of London, London, UK
| | - Dionisio Acosta
- CHIME, University College London, The Farr Institute of Health Informatics Research, London, UK
| | - Geert Postma
- Radboud University Nijmegen, Institute for Molecules and Materials, Analytical Chemistry, Nijmegen, The Netherlands
| | | | - Carles Majós
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Cerdanyola del Vallès, Spain
- Institut de Diagnòstic per la Imatge (IDI), CSU de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain
| | - Carles Arús
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Cerdanyola del Vallès, Spain
- Departament de Bioquímica i Biologia Molecular, Unitat de Bioquímica de Biociències, Universitat Autònoma de Barcelona (UAB), Cerdanyola del Vallès, Spain
- Institut de Biotecnologia i de Biomedicina (IBB), Universitat Autònoma de Barcelona (UAB), Cerdanyola del Vallès, Spain
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Wang R, Ma J, Niu G, Zheng J, Liu Z, Du Y, Yu B, Yang J. Differentiation between Solitary Cerebral Metastasis and Astrocytoma on the Basis of Subventricular Zone Involvement on Magnetic Resonance Imaging. PLoS One 2015. [PMID: 26197398 PMCID: PMC4510882 DOI: 10.1371/journal.pone.0133480] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Purpose To determine the relationship between the subventricular zone (SVZ) and astrocytoma based on magnetic resonance imaging (MRI) and whether SVZ involvement can be used to distinguish solitary cerebral metastases (SCMs) from astrocytomas. Methods This retrospective study involved 154 patients with solitary low-grade astrocytoma (LGA), high-grade astrocytoma (HGA), and SCM, who underwent T1-weighted imaging (T1WI), Gd-DTPA–enhanced T1WI, and T2-weighted imaging (T2WI) or fluid-attenuated inversion recovery (FLAIR) T2WI. The spatial relationship between the tumor and SVZ was classified as “involvement” or “segregation” on contrast-enhanced T1WI for enhanced tumors and T2WI/FLAIR T2WI for non-enhanced tumors. Patient-based SVZ-contact rates were compared between the LGA, HGA, and SCM groups. The frequencies of involvement of various lateral ventricle regions by astrocytoma were compared. The correlation between SVZ involvement and tumor necrosis was analyzed. Results Patient-based SVZ-contact rates in SCM, LGA, and HGA were 24.1%, 68.8%, and 85.4%, respectively. Univariate analysis showed that the SVZ-contact rate was significantly different between SCM and astrocytoma (24.1% vs. 75.2% P < 0.001), also between LGA and HGA (68.1% vs. 85.4% P=0.037). After the tumor volume was adjusted as a covariate, SVZ-contact rates still differed between SCMs and astrocytomas (Odds ratio [OR]: 4.58, 95% Confidence interval [CI]: 1.65 to 12.8, P=0.004). Tumor volume differed between LGA and HGA (P< 0.001), and influenced the association between SVZ involvement and astrocytoma grade (P = 0.05). Among the lateral ventricle regions, the frontal horn was the most frequently involved by astrocytomas. SVZ-contact rates were higher in necrosis group compared with non-necrosis groups (83.9% vs. 50.0%, P < 0.001) among astrocytoma patients. Necrosis positively correlated with SVZ involvement in astrocytomas (rs = 0.342, P < 0.001), but did not correlate with SVZ involvement in SCMs (P = 0.193). Conclusions Compared to SCMs, solitary cerebral astrocytomas exhibited spatial proximity to the SVZ, which might distinguish the supratentorial astrocytomas from SCMs.
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Affiliation(s)
- Rong Wang
- Radiology Department of the First Affiliated Hospital, Xi’an Jiaotong University, Xi’an, Shaanxi, People’s Republic of China
| | - Jiaqi Ma
- Radiology Department of the First Affiliated Hospital, Xi’an Jiaotong University, Xi’an, Shaanxi, People’s Republic of China
| | - Gang Niu
- Radiology Department of the First Affiliated Hospital, Xi’an Jiaotong University, Xi’an, Shaanxi, People’s Republic of China
| | - Jie Zheng
- Clinical research center of the First Affiliated Hospital, Xi’an Jiaotong University, Xi’an, Shaanxi, People’s Republic of China
| | - Zhe Liu
- Radiology Department of the First Affiliated Hospital, Xi’an Jiaotong University, Xi’an, Shaanxi, People’s Republic of China
| | - Yonghao Du
- Radiology Department of the First Affiliated Hospital, Xi’an Jiaotong University, Xi’an, Shaanxi, People’s Republic of China
| | - Bolang Yu
- Radiology Department of the First Affiliated Hospital, Xi’an Jiaotong University, Xi’an, Shaanxi, People’s Republic of China
| | - Jian Yang
- Radiology Department of the First Affiliated Hospital, Xi’an Jiaotong University, Xi’an, Shaanxi, People’s Republic of China
- * E-mail:
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Johnson DR, Fogh SE, Giannini C, Kaufmann TJ, Raghunathan A, Theodosopoulos PV, Clarke JL. Case-Based Review: newly diagnosed glioblastoma. Neurooncol Pract 2015; 2:106-121. [PMID: 31386093 DOI: 10.1093/nop/npv020] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2015] [Indexed: 12/28/2022] Open
Abstract
Glioblastoma (WHO grade IV astrocytoma) is the most common and most aggressive primary brain tumor in adults. Optimal treatment of a patient with glioblastoma requires collaborative care across numerous specialties. The diagnosis of glioblastoma may be suggested by the symptomatic presentation and imaging, but it must be pathologically confirmed via surgery, which can have dual diagnostic and therapeutic roles. Standard of care postsurgical treatment for newly diagnosed patients involves radiation therapy and oral temozolomide chemotherapy. Despite numerous recent trials of novel therapeutic approaches, this standard of care has not changed in over a decade. Treatment options under active investigation include molecularly targeted therapies, immunotherapeutic approaches, and the use of alternating electrical field to disrupt tumor cell division. These trials may be aided by new insights into glioblastoma heterogeneity, allowing for focused evaluation of new treatments in the patient subpopulations most likely to benefit from them. Because glioblastoma is incurable by current therapies, frequent clinical and radiographic assessment is needed after initial treatment to allow for early intervention upon progressive tumor when it occurs.
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Affiliation(s)
- Derek R Johnson
- Department of Neurology and Division of Medical Oncology, Mayo Clinic, Rochester, Minnesota (D.R.J.); Department of Radiation Oncology, University of California, San Francisco, California (S.E.F.); Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota (C.G., A.R.); Department of Radiology, Mayo Clinic, Rochester, Minnesota (T.J.K.); Department of Neurological Surgery, University of California, San Francisco, California (P.V.T.); Department of Neurology and Department of Neurological Surgery, University of California, San Francisco, California (J.L.C.)
| | - Shannon E Fogh
- Department of Neurology and Division of Medical Oncology, Mayo Clinic, Rochester, Minnesota (D.R.J.); Department of Radiation Oncology, University of California, San Francisco, California (S.E.F.); Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota (C.G., A.R.); Department of Radiology, Mayo Clinic, Rochester, Minnesota (T.J.K.); Department of Neurological Surgery, University of California, San Francisco, California (P.V.T.); Department of Neurology and Department of Neurological Surgery, University of California, San Francisco, California (J.L.C.)
| | - Caterina Giannini
- Department of Neurology and Division of Medical Oncology, Mayo Clinic, Rochester, Minnesota (D.R.J.); Department of Radiation Oncology, University of California, San Francisco, California (S.E.F.); Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota (C.G., A.R.); Department of Radiology, Mayo Clinic, Rochester, Minnesota (T.J.K.); Department of Neurological Surgery, University of California, San Francisco, California (P.V.T.); Department of Neurology and Department of Neurological Surgery, University of California, San Francisco, California (J.L.C.)
| | - Timothy J Kaufmann
- Department of Neurology and Division of Medical Oncology, Mayo Clinic, Rochester, Minnesota (D.R.J.); Department of Radiation Oncology, University of California, San Francisco, California (S.E.F.); Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota (C.G., A.R.); Department of Radiology, Mayo Clinic, Rochester, Minnesota (T.J.K.); Department of Neurological Surgery, University of California, San Francisco, California (P.V.T.); Department of Neurology and Department of Neurological Surgery, University of California, San Francisco, California (J.L.C.)
| | - Aditya Raghunathan
- Department of Neurology and Division of Medical Oncology, Mayo Clinic, Rochester, Minnesota (D.R.J.); Department of Radiation Oncology, University of California, San Francisco, California (S.E.F.); Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota (C.G., A.R.); Department of Radiology, Mayo Clinic, Rochester, Minnesota (T.J.K.); Department of Neurological Surgery, University of California, San Francisco, California (P.V.T.); Department of Neurology and Department of Neurological Surgery, University of California, San Francisco, California (J.L.C.)
| | - Philip V Theodosopoulos
- Department of Neurology and Division of Medical Oncology, Mayo Clinic, Rochester, Minnesota (D.R.J.); Department of Radiation Oncology, University of California, San Francisco, California (S.E.F.); Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota (C.G., A.R.); Department of Radiology, Mayo Clinic, Rochester, Minnesota (T.J.K.); Department of Neurological Surgery, University of California, San Francisco, California (P.V.T.); Department of Neurology and Department of Neurological Surgery, University of California, San Francisco, California (J.L.C.)
| | - Jennifer L Clarke
- Department of Neurology and Division of Medical Oncology, Mayo Clinic, Rochester, Minnesota (D.R.J.); Department of Radiation Oncology, University of California, San Francisco, California (S.E.F.); Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota (C.G., A.R.); Department of Radiology, Mayo Clinic, Rochester, Minnesota (T.J.K.); Department of Neurological Surgery, University of California, San Francisco, California (P.V.T.); Department of Neurology and Department of Neurological Surgery, University of California, San Francisco, California (J.L.C.)
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Yang G, Jones TL, Howe FA, Barrick TR. Morphometric model for discrimination between glioblastoma multiforme and solitary metastasis using three-dimensional shape analysis. Magn Reson Med 2015; 75:2505-16. [PMID: 26173745 DOI: 10.1002/mrm.25845] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2015] [Revised: 05/28/2015] [Accepted: 06/23/2015] [Indexed: 01/21/2023]
Abstract
PURPOSE Glioblastoma multiforme (GBM) and brain metastasis (MET) are the most common intra-axial brain neoplasms in adults and often pose a diagnostic dilemma using standard clinical MRI. These tumor types require different oncological and surgical management, which subsequently influence prognosis and clinical outcome. METHODS Here, we hypothesize that GBM and MET possess different three-dimensional (3D) morphological attributes based on their physical characteristics. A 3D morphological analysis was applied on the tumor surface defined by our diffusion tensor imaging (DTI) segmentation technique. It segments the DTI data into clusters representing different isotropic and anisotropic water diffusion characteristics, from which a distinct surface boundary between healthy and pathological tissue was identified. Morphometric features of shape index and curvedness were then computed for each tumor surface and used to build a morphometric model of GBM and MET pathology with the goal of developing a tumor classification method based on shape characteristics. RESULTS Our 3D morphometric method was applied on 48 untreated brain tumor patients. Cross-validation resulted in a 95.8% accuracy classification with only two shape features needed and that can be objectively derived from quantitative imaging methods. CONCLUSION The proposed 3D morphometric analysis framework can be applied to distinguish GBMs from solitary METs. Magn Reson Med 75:2505-2516, 2016. © 2015 Wiley Periodicals, Inc.
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Affiliation(s)
- Guang Yang
- Neuroscience Research Centre, Cardiovascular and Cell Sciences Institute, St. George's, University of London, London, United Kingdom
| | - Timothy L Jones
- Neuroscience Research Centre, Cardiovascular and Cell Sciences Institute, St. George's, University of London, London, United Kingdom
| | - Franklyn A Howe
- Neuroscience Research Centre, Cardiovascular and Cell Sciences Institute, St. George's, University of London, London, United Kingdom
| | - Thomas R Barrick
- Neuroscience Research Centre, Cardiovascular and Cell Sciences Institute, St. George's, University of London, London, United Kingdom
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Differentiation of solitary brain metastasis from glioblastoma multiforme: a predictive multiparametric approach using combined MR diffusion and perfusion. Neuroradiology 2015; 57:697-703. [DOI: 10.1007/s00234-015-1524-6] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2014] [Accepted: 03/24/2015] [Indexed: 10/23/2022]
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Abstract
Discerning between primary brain tumor progression and treatment-related effect is a significant issue and a major challenge in neuro-oncology. The difficulty in differentiating tumor progression from treatment-related effects has important implications for treatment decisions and prognosis, as well as for clinical trial design and results. Conventional MRI is widely used to assess disease status, but cannot reliably distinguish between tumor progression and treatment-related effects. Several advanced imaging techniques are promising, but have yet to be prospectively validated for this use. This review explores two treatment-related effects, pseudoprogression and radiation necrosis, as well as the concept of pseudoresponse, and highlights several advanced imaging modalities and the evidence supporting their use in differentiating tumor progression from treatment-related effect.
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Affiliation(s)
- Barbara J O'Brien
- Department of Neuro-Oncology, MD Anderson Cancer Center, 1515 Holcombe Blvd. Unit 0431, Houston, TX, 77030, USA,
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Yang G, Jones TL, Barrick TR, Howe FA. Discrimination between glioblastoma multiforme and solitary metastasis using morphological features derived from the p:q tensor decomposition of diffusion tensor imaging. NMR IN BIOMEDICINE 2014; 27:1103-1111. [PMID: 25066520 DOI: 10.1002/nbm.3163] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2014] [Revised: 06/04/2014] [Accepted: 06/12/2014] [Indexed: 06/03/2023]
Abstract
The management and treatment of high-grade glioblastoma multiforme (GBM) and solitary metastasis (MET) are very different and influence the prognosis and subsequent clinical outcomes. In the case of a solitary MET, diagnosis using conventional radiology can be equivocal. Currently, a definitive diagnosis is based on histopathological analysis on a biopsy sample. Here, we present a computerised decision support framework for discrimination between GBM and solitary MET using MRI, which includes: (i) a semi-automatic segmentation method based on diffusion tensor imaging; (ii) two-dimensional morphological feature extraction and selection; and (iii) a pattern recognition module for automated tumour classification. Ground truth was provided by histopathological analysis from pre-treatment stereotactic biopsy or at surgical resection. Our two-dimensional morphological analysis outperforms previous methods with high cross-validation accuracy of 97.9% and area under the receiver operating characteristic curve of 0.975 using a neural networks-based classifier.
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Affiliation(s)
- Guang Yang
- Neuroscience Research Centre, Cardiovascular and Cell Sciences Institute, St. George's, University of London, London, UK
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Gradient of apparent diffusion coefficient values in peritumoral edema helps in differentiation of glioblastoma from solitary metastatic lesions. AJR Am J Roentgenol 2014; 203:163-9. [PMID: 24951211 DOI: 10.2214/ajr.13.11186] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
OBJECTIVE Glioblastoma and solitary metastatic lesions can be difficult to differentiate with conventional MRI. The use of diffusion-weighted MRI to better characterize peritumoral edema has been explored for this purpose, but the results have been conflicting. The purpose of this study was to test the hypothesis that the gradient of apparent diffusion coefficient (ADC) values in peritumoral edema--that is, the difference in ADC values from the region closest to the enhancing tumor and the one closest to the normal-appearing white matter--may be a marker for differentiating glioblastoma from a metastatic lesion. MATERIALS AND METHODS Forty patients, 20 with glioblastoma and 20 with a solitary metastatic lesion, underwent diffusion-weighted brain MRI before surgical resection. The ADC values were retrospectively collected in the peritumoral edema in three positions: near, an intermediate distance from, and far from the core enhancing tumor (G1, G2, and G3). The ADC gradient in the peritumoral edema was calculated as the subtractions ADCG3 - ADCG1, ADCG3 - ADCG2, and ADCG2 - ADCG1. The ADC values in the enhancing tumor, peritumoral edema, ipsilateral normal-appearing white matter, contralateral healthy white matter, and CSF were also collected. RESULTS A gradient of ADC values was found in the peritumoral edema of glioblastoma. The ADC values increased from the region close to the enhancing tumor (1.36 ± 0.24 × 10(-3) mm(2)/s) to the area near the normal-appearing white matter (1.57 ± 0.34 × 10(-3) mm(2)/s). In metastatic lesions, however, those values were nearly homogeneous (p = 0.04). CONCLUSION The ADC gradient in peritumoral edema appears to be a promising tool for differentiating glioblastoma from a metastatic lesion.
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Svolos P, Kousi E, Kapsalaki E, Theodorou K, Fezoulidis I, Kappas C, Tsougos I. The role of diffusion and perfusion weighted imaging in the differential diagnosis of cerebral tumors: a review and future perspectives. Cancer Imaging 2014; 14:20. [PMID: 25609475 PMCID: PMC4331825 DOI: 10.1186/1470-7330-14-20] [Citation(s) in RCA: 71] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2014] [Accepted: 03/20/2014] [Indexed: 12/31/2022] Open
Abstract
The role of conventional Magnetic Resonance Imaging (MRI) in the detection of cerebral tumors has been well established. However its excellent soft tissue visualization and variety of imaging sequences are in many cases non-specific for the assessment of brain tumor grading. Hence, advanced MRI techniques, like Diffusion-Weighted Imaging (DWI), Diffusion Tensor Imaging (DTI) and Dynamic-Susceptibility Contrast Imaging (DSCI), which are based on different contrast principles, have been used in the clinical routine to improve diagnostic accuracy. The variety of quantitative information derived from these techniques provides significant structural and functional information in a cellular level, highlighting aspects of the underlying brain pathophysiology. The present work, reviews physical principles and recent results obtained using DWI/DTI and DSCI, in tumor characterization and grading of the most common cerebral neoplasms, and discusses how the available MR quantitative data can be utilized through advanced methods of analysis, in order to optimize clinical decision making.
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