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Xue C, Zhou Q, Zhang B, Ke X, Zhang P, Liu X, Li S, Deng J, Zhou J. Vasari-Based Features Nomogram to Predict the Tumor-Infiltrating CD8+ T Cell Levels in Glioblastoma. Acad Radiol 2024; 31:2050-2060. [PMID: 37985291 DOI: 10.1016/j.acra.2023.10.049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 10/25/2023] [Accepted: 10/26/2023] [Indexed: 11/22/2023]
Abstract
RATIONALE AND OBJECTIVES Tumor-infiltrating CD8 + T cells play a key role in glioblastoma (GB) development, malignant progression, and recurrence. The aim of the study was to establish nomograms based on the Visually AcceSAble Rembrandt Images (VASARI) features of multiparametric magnetic resonance imaging (MRI) to determine the expression levels of tumor-infiltrating CD8 + T cells in patients with GB. MATERIALS AND METHODS Pathological and imaging data of 140 patients with GB confirmed by surgery and pathology were retrospectively analyzed. The levels of tumor-infiltrating CD8 + T cells in tumor tissue samples obtained from patients were quantified using immunohistochemical staining. Patients were divided into high and low CD8 expression groups. The MRI images of patients with GB were analyzed by two radiologists using the VASARI scoring system. RESULTS A total of 25 MRI-based VASARI imaging features were evaluated by two neuroradiologists. The features with the greatest predictive power for CD8 expression levels were, cystic (OR, 3.063; 95% CI: 1.387, 6.766; P = 0.006), hemorrhage (OR, 2.980; 95% CI: 1.172, 7.575; P = 0.022), and ependymal extension (OR, 0.257; 95% CI: 0.114 0.581; P = 0.001). A logistic regression model based on these three features showed better sample predictive performance (AUC=0.745; 95% CI: 0.665, 0.825; Sensitivity=0.527; Specificity=0.857). CONCLUSION The VASARI feature-based nomogram model can show promise to predict the level of infiltrative CD8 expression in GB tumors non-invasively for earlier tissue diagnosis and more aggressive treatment.
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Affiliation(s)
- Caiqiang Xue
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Qing Zhou
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Bin Zhang
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Xiaoai Ke
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Peng Zhang
- Department of Pathology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
| | - Xianwang Liu
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Shenglin Li
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Juan Deng
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China.
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Tang WT, Su CQ, Lin J, Xia ZW, Lu SS, Hong XN. T2-FLAIR mismatch sign and machine learning-based multiparametric MRI radiomics in predicting IDH mutant 1p/19q non-co-deleted diffuse lower-grade gliomas. Clin Radiol 2024; 79:e750-e758. [PMID: 38360515 DOI: 10.1016/j.crad.2024.01.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 01/09/2024] [Accepted: 01/16/2024] [Indexed: 02/17/2024]
Abstract
AIM To investigate the application of the T2-weighted (T2)-fluid-attenuated inversion recovery (FLAIR) mismatch sign and machine learning-based multiparametric magnetic resonance imaging (MRI) radiomics in predicting 1p/19q non-co-deletion of lower-grade gliomas (LGGs). MATERIALS AND METHODS One hundred and forty-six patients, who had pathologically confirmed isocitrate dehydrogenase (IDH) mutant LGGs were assigned randomly to the training cohort (n=102) and the testing cohort (n=44) at a ratio of 7:3. The T2-FLAIR mismatch sign and conventional MRI features were evaluated. Radiomics features extracted from T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), FLAIR, apparent diffusion coefficient (ADC), and contrast-enhanced T1WI images (CE-T1WI). The models that displayed the best performance of each sequence were selected, and their predicted values as well as the T2-FLAIR mismatch sign data were collected to establish a final stacking model. Receiver operating characteristic curve (ROC) analyses and area under the curve (AUC) values were applied to evaluate and compare the performance of the models. RESULTS The T2-FLAIR mismatch sign was more common in the IDH mutant 1p/19q non-co-deleted group (p<0.05) and the area under the curve (AUC) value was 0.692 with sensitivity 0.397, specificity 0.987, and accuracy 0.712, respectively. The stacking model showed a favourable performance with an AUC of 0.925 and accuracy of 0.882 in the training cohort and an AUC of 0.886 and accuracy of 0.864 in the testing cohort. CONCLUSION The stacking model based on multiparametric MRI can serve as a supplementary tool for pathological diagnosis, offering valuable guidance for clinical practice.
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Affiliation(s)
- W-T Tang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210029, China
| | - C-Q Su
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210029, China
| | - J Lin
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210029, China
| | - Z-W Xia
- Department of Radiology, 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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Zhang H, Ouyang Y, Zhang H, Zhang Y, Su R, Zhou B, Yang W, Lei Y, Huang B. Sub-region based radiomics analysis for prediction of isocitrate dehydrogenase and telomerase reverse transcriptase promoter mutations in diffuse gliomas. Clin Radiol 2024; 79:e682-e691. [PMID: 38402087 DOI: 10.1016/j.crad.2024.01.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 01/16/2024] [Accepted: 01/21/2024] [Indexed: 02/26/2024]
Abstract
AIM To enhance the prediction of mutation status of isocitrate dehydrogenase (IDH) and telomerase reverse transcriptase (TERT) promoter, which are crucial for glioma prognostication and therapeutic decision-making, via sub-regional radiomics analysis based on multiparametric magnetic resonance imaging (MRI). MATERIALS AND METHODS A retrospective study was conducted on 401 participants with adult-type diffuse gliomas. Employing the K-means algorithm, tumours were clustered into two to four subregions. Sub-regional radiomics features were extracted and selected using the Mann-Whitney U-test, Pearson correlation analysis, and least absolute shrinkage and selection operator, forming the basis for predictive models. The performance of model combinations of different sub-regional features and classifiers (including logistic regression, support vector machines, K-nearest neighbour, light gradient boosting machine, and multilayer perceptron) was evaluated using an external test set. RESULTS The models demonstrated high predictive performance, with area under the receiver operating characteristic curve (AUC) values ranging from 0.918 to 0.994 in the training set for IDH mutation prediction and from 0.758 to 0.939 for TERT promoter mutation prediction. In the external test sets, the two-cluster radiomics features and the logistic regression model yielded the highest prediction for IDH mutation, resulting in an AUC of 0.905. Additionally, the most effective predictive performance with an AUC of 0.803 was achieved using the four-cluster radiomics features and the support vector machine model, specifically for TERT promoter mutation prediction. CONCLUSION The present study underscores the potential of sub-regional radiomics analysis in predicting IDH and TERT promoter mutations in glioma patients. These models have the capacity to refine preoperative glioma diagnosis and contribute to personalised therapeutic interventions for patients.
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Affiliation(s)
- H Zhang
- Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, 517108, China; Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510515, China
| | - Y Ouyang
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510515, China
| | - H Zhang
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, 518035, China
| | - Y Zhang
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510515, China
| | - R Su
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510515, China
| | - B Zhou
- Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, 517108, China
| | - W Yang
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
| | - Y Lei
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, 518035, China.
| | - B Huang
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510515, China.
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Foltyn-Dumitru M, Schell M, Rastogi A, Sahm F, Kessler T, Wick W, Bendszus M, Brugnara G, Vollmuth P. Impact of signal intensity normalization of MRI on the generalizability of radiomic-based prediction of molecular glioma subtypes. Eur Radiol 2024; 34:2782-2790. [PMID: 37672053 PMCID: PMC10957611 DOI: 10.1007/s00330-023-10034-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 05/09/2023] [Accepted: 06/16/2023] [Indexed: 09/07/2023]
Abstract
OBJECTIVES Radiomic features have demonstrated encouraging results for non-invasive detection of molecular biomarkers, but the lack of guidelines for pre-processing MRI-data has led to poor generalizability. Here, we assessed the influence of different MRI-intensity normalization techniques on the performance of radiomics-based models for predicting molecular glioma subtypes. METHODS Preoperative MRI-data from n = 615 patients with newly diagnosed glioma and known isocitrate dehydrogenase (IDH) and 1p/19q status were pre-processed using four different methods: no normalization (naive), N4 bias field correction (N4), N4 followed by either WhiteStripe (N4/WS), or z-score normalization (N4/z-score). A total of 377 Image-Biomarker-Standardisation-Initiative-compliant radiomic features were extracted from each normalized data, and 9 different machine-learning algorithms were trained for multiclass prediction of molecular glioma subtypes (IDH-mutant 1p/19q codeleted vs. IDH-mutant 1p/19q non-codeleted vs. IDH wild type). External testing was performed in public glioma datasets from UCSF (n = 410) and TCGA (n = 160). RESULTS Support vector machine yielded the best performance with macro-average AUCs of 0.84 (naive), 0.84 (N4), 0.87 (N4/WS), and 0.87 (N4/z-score) in the internal test set. Both N4/WS and z-score outperformed the other approaches in the external UCSF and TCGA test sets with macro-average AUCs ranging from 0.85 to 0.87, replicating the performance of the internal test set, in contrast to macro-average AUCs ranging from 0.19 to 0.45 for naive and 0.26 to 0.52 for N4 alone. CONCLUSION Intensity normalization of MRI data is essential for the generalizability of radiomic-based machine-learning models. Specifically, both N4/WS and N4/z-score approaches allow to preserve the high model performance, yielding generalizable performance when applying the developed radiomic-based machine-learning model in an external heterogeneous, multi-institutional setting. CLINICAL RELEVANCE STATEMENT Intensity normalization such as N4/WS or N4/z-score can be used to develop reliable radiomics-based machine learning models from heterogeneous multicentre MRI datasets and provide non-invasive prediction of glioma subtypes. KEY POINTS • MRI-intensity normalization increases the stability of radiomics-based models and leads to better generalizability. • Intensity normalization did not appear relevant when the developed model was applied to homogeneous data from the same institution. • Radiomic-based machine learning algorithms are a promising approach for simultaneous classification of IDH and 1p/19q status of glioma.
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Affiliation(s)
- Martha Foltyn-Dumitru
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany
| | - Marianne Schell
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany
| | - Aditya Rastogi
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany
| | - Felix Sahm
- Department of Neuropathology, Heidelberg University Hospital, Heidelberg, DE, Germany
| | - Tobias Kessler
- Department of Neurology, Heidelberg University Hospital, Heidelberg, DE, Germany
| | - Wolfgang Wick
- Department of Neurology, Heidelberg University Hospital, Heidelberg, DE, Germany
- Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, DE, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany.
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany.
- Division of Medical Image Computing (MIC), German Cancer Research Center (DFKZ), Heidelberg, Germany.
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Kikuchi K, Togao O, Yamashita K, Momosaka D, Kikuchi Y, Kuga D, Yuhei S, Fujioka Y, Narutomi F, Obara M, Yoshimoto K, Ishigami K. Comparison of diagnostic performance of radiologist- and AI-based assessments of T2-FLAIR mismatch sign and quantitative assessment using synthetic MRI in the differential diagnosis between astrocytoma, IDH-mutant and oligodendroglioma, IDH-mutant and 1p/19q-codeleted. Neuroradiology 2024; 66:333-341. [PMID: 38224343 PMCID: PMC10859342 DOI: 10.1007/s00234-024-03288-0] [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: 10/30/2023] [Accepted: 01/07/2024] [Indexed: 01/16/2024]
Abstract
PURPOSE This study aimed to compare assessments by radiologists, artificial intelligence (AI), and quantitative measurement using synthetic MRI (SyMRI) for differential diagnosis between astrocytoma, IDH-mutant and oligodendroglioma, and IDH-mutant and 1p/19q-codeleted and to identify the superior method. METHODS Thirty-three cases (men, 14; women, 19) comprising 19 astrocytomas and 14 oligodendrogliomas were evaluated. Four radiologists independently evaluated the presence of the T2-FLAIR mismatch sign. A 3D convolutional neural network (CNN) model was trained using 50 patients outside the test group (28 astrocytomas and 22 oligodendrogliomas) and transferred to evaluate the T2-FLAIR mismatch lesions in the test group. If the CNN labeled more than 50% of the T2-prolonged lesion area, the result was considered positive. The T1/T2-relaxation times and proton density (PD) derived from SyMRI were measured in both gliomas. Each quantitative parameter (T1, T2, and PD) was compared between gliomas using the Mann-Whitney U-test. Receiver-operating characteristic analysis was used to evaluate the diagnostic performance. RESULTS The mean sensitivity, specificity, and area under the curve (AUC) of radiologists vs. AI were 76.3% vs. 94.7%; 100% vs. 92.9%; and 0.880 vs. 0.938, respectively. The two types of diffuse gliomas could be differentiated using a cutoff value of 2290/128 ms for a combined 90th percentile of T1 and 10th percentile of T2 relaxation times with 94.4/100% sensitivity/specificity with an AUC of 0.981. CONCLUSION Compared to the radiologists' assessment using the T2-FLAIR mismatch sign, the AI and the SyMRI assessments increased both sensitivity and objectivity, resulting in improved diagnostic performance in differentiating gliomas.
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Affiliation(s)
- Kazufumi Kikuchi
- Department of Molecular Imaging and Diagnosis, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan.
| | - Osamu Togao
- Department of Molecular Imaging and Diagnosis, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Koji Yamashita
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Daichi Momosaka
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Yoshitomo Kikuchi
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Daisuke Kuga
- Department of Neurosurgery, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Sangatsuda Yuhei
- Department of Neurosurgery, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Yutaka Fujioka
- Department of Neurosurgery, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Fumiya Narutomi
- Department of Anatomic Pathology, Pathological Sciences, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Makoto Obara
- Philips Japan Ltd., 2-13-37, Konan, Minato-Ku, Tokyo, 108-8507, Japan
| | - Koji Yoshimoto
- Department of Neurosurgery, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Kousei Ishigami
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
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Kino S, Kanamori M, Shimoda Y, Niizuma K, Endo H, Matsuura Y. Distinguishing IDH mutation status in gliomas using FTIR-ATR spectra of peripheral blood plasma indicating clear traces of protein amyloid aggregation. BMC Cancer 2024; 24:222. [PMID: 38365669 PMCID: PMC10870484 DOI: 10.1186/s12885-024-11970-y] [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: 12/04/2023] [Accepted: 02/06/2024] [Indexed: 02/18/2024] Open
Abstract
BACKGROUND Glioma is a primary brain tumor and the assessment of its molecular profile in a minimally invasive manner is important in determining treatment strategies. Among the molecular abnormalities of gliomas, mutations in the isocitrate dehydrogenase (IDH) gene are strong predictors of treatment sensitivity and prognosis. In this study, we attempted to non-invasively diagnose glioma development and the presence of IDH mutations using multivariate analysis of the plasma mid-infrared absorption spectra for a comprehensive and sensitive view of changes in blood components associated with the disease and genetic mutations. These component changes are discussed in terms of absorption wavenumbers that contribute to differentiation. METHODS Plasma samples were collected at our institutes from 84 patients with glioma (13 oligodendrogliomas, 17 IDH-mutant astrocytoma, 7 IDH wild-type diffuse glioma, and 47 glioblastomas) before treatment initiation and 72 healthy participants. FTIR-ATR spectra were obtained for each plasma sample, and PLS discriminant analysis was performed using the absorbance of each wavenumber in the fingerprint region of biomolecules as the explanatory variable. This data was used to distinguish patients with glioma from healthy participants and diagnose the presence of IDH mutations. RESULTS The derived classification algorithm distinguished the patients with glioma from healthy participants with 83% accuracy (area under the curve (AUC) in receiver operating characteristic (ROC) = 0.908) and diagnosed the presence of IDH mutation with 75% accuracy (AUC = 0.752 in ROC) in cross-validation using 30% of the total test data. The characteristic changes in the absorption spectra suggest an increase in the ratio of β-sheet structures in the conformational composition of blood proteins of patients with glioma. Furthermore, these changes were more pronounced in patients with IDH-mutant gliomas. CONCLUSIONS The plasma infrared absorption spectra could be used to diagnose gliomas and the presence of IDH mutations in gliomas with a high degree of accuracy. The spectral shape of the protein absorption band showed that the ratio of β-sheet structures in blood proteins was significantly higher in patients with glioma than in healthy participants, and protein aggregation was a distinct feature in patients with glioma with IDH mutations.
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Affiliation(s)
- Saiko Kino
- Graduate School of Biomedical Engineering, Tohoku University, 6-6-05, Aza-Aoba, Aramaki, Aoba, Sendai City, 980-8579, Miyagi Prefecture, Japan
| | - Masayuki Kanamori
- Department of Neurosurgery, Tohoku University Graduate School of Medicine, 980-8574 Seiryo 1-1, Aoba, Sendai City, Miyagi Prefecture, Japan
| | - Yoshiteru Shimoda
- Department of Neurosurgery, Tohoku University Graduate School of Medicine, 980-8574 Seiryo 1-1, Aoba, Sendai City, Miyagi Prefecture, Japan
| | - Kuniyasu Niizuma
- Department of Neurosurgical Engineering and Translational Neuroscience, Graduate School of Biomedical Engineering, Tohoku University, Seiryo 2-1, Aoba, Sendai City, 980-8575, Miyagi Prefecture, Japan
- Department of Neurosurgical Engineering and Translational Neuroscience, Tohoku University Graduate School of Medicine, 980-8575 Seiryo 2-1, Aoba, Sendai City, Miyagi Prefecture, Japan
| | - Hidenori Endo
- Department of Neurosurgery, Tohoku University Graduate School of Medicine, 980-8574 Seiryo 1-1, Aoba, Sendai City, Miyagi Prefecture, Japan
| | - Yuji Matsuura
- Graduate School of Biomedical Engineering, Tohoku University, 6-6-05, Aza-Aoba, Aramaki, Aoba, Sendai City, 980-8579, Miyagi Prefecture, Japan.
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7
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Paech D, Weckesser N, Franke VL, Breitling J, Görke S, Deike-Hofmann K, Wick A, Scherer M, Unterberg A, Wick W, Bendszus M, Bachert P, Ladd ME, Schlemmer HP, Korzowski A. Whole-Brain Intracellular pH Mapping of Gliomas Using High-Resolution 31P MR Spectroscopic Imaging at 7.0 T. Radiol Imaging Cancer 2024; 6:e220127. [PMID: 38133553 PMCID: PMC10825708 DOI: 10.1148/rycan.220127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Revised: 10/24/2023] [Accepted: 11/02/2023] [Indexed: 12/23/2023]
Abstract
Malignant tumors commonly exhibit a reversed pH gradient compared with normal tissue, with a more acidic extracellular pH and an alkaline intracellular pH (pHi). In this prospective study, pHi values in gliomas were quantified using high-resolution phosphorous 31 (31P) spectroscopic MRI at 7.0 T and were used to correlate pHi alterations with histopathologic findings. A total of 12 participants (mean age, 58 years ± 18 [SD]; seven male, five female) with histopathologically proven, newly diagnosed glioma were included between September 2018 and November 2019. The 31P spectroscopic MRI scans were acquired using a double-resonant 31P/1H phased-array head coil together with a three-dimensional (3D) 31P chemical shift imaging sequence (5.7-mL voxel volume) performed with a 7.0-T whole-body system. The 3D volumetric segmentations were performed for the whole-tumor volumes (WTVs); tumor subcompartments of necrosis, gadolinium enhancement, and nonenhancing T2 (NCE T2) hyperintensity; and normal-appearing white matter (NAWM), and pHi values were compared. Spearman correlation was used to assess association between pHi and the proliferation index Ki-67. For all study participants, mean pHi values were higher in the WTV (7.057 ± 0.024) compared with NAWM (7.006 ± 0.012; P < .001). In eight participants with high-grade gliomas, pHi was increased in all tumor subcompartments (necrosis, 7.075 ± 0.033; gadolinium enhancement, 7.075 ± 0.024; NCE T2 hyperintensity, 7.043 ± 0.015) compared with NAWM (7.004 ± 0.014; all P < .01). The pHi values of WTV positively correlated with Ki-67 (R2 = 0.74, r = 0.78, P = .001). In conclusion, 31P spectroscopic MRI at 7.0 T enabled high-resolution quantification of pHi in gliomas, with pHi alteration associated with the Ki-67 proliferation index, and may aid in diagnosis and treatment monitoring. Keywords: 31P MRSI, pH, Glioma, Glioblastoma, Ultra-High-Field MRI, Imaging Biomarker, 7 Tesla Supplemental material is available for this article. © RSNA, 2023.
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Affiliation(s)
| | | | - Vanessa L. Franke
- From the Divisions of Radiology (D.P., N.W., K.D.H., H.P.S.) and
Medical Physics in Radiology (V.L.F., J.B., S.G., P.B., M.E.L., A.K.), German
Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg,
Germany; Faculties of Medicine (N.W., M.E.L.) and Physics and Astronomy (V.L.F.,
P.B., M.E.L.), University of Heidelberg, Heidelberg, Germany; and Departments of
Neurology (A.W., W.W.), Neurosurgery (M.S., A.U.), and Neuroradiology (M.B.),
Heidelberg University Hospital, Heidelberg, Germany
| | - Johannes Breitling
- From the Divisions of Radiology (D.P., N.W., K.D.H., H.P.S.) and
Medical Physics in Radiology (V.L.F., J.B., S.G., P.B., M.E.L., A.K.), German
Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg,
Germany; Faculties of Medicine (N.W., M.E.L.) and Physics and Astronomy (V.L.F.,
P.B., M.E.L.), University of Heidelberg, Heidelberg, Germany; and Departments of
Neurology (A.W., W.W.), Neurosurgery (M.S., A.U.), and Neuroradiology (M.B.),
Heidelberg University Hospital, Heidelberg, Germany
| | - Steffen Görke
- From the Divisions of Radiology (D.P., N.W., K.D.H., H.P.S.) and
Medical Physics in Radiology (V.L.F., J.B., S.G., P.B., M.E.L., A.K.), German
Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg,
Germany; Faculties of Medicine (N.W., M.E.L.) and Physics and Astronomy (V.L.F.,
P.B., M.E.L.), University of Heidelberg, Heidelberg, Germany; and Departments of
Neurology (A.W., W.W.), Neurosurgery (M.S., A.U.), and Neuroradiology (M.B.),
Heidelberg University Hospital, Heidelberg, Germany
| | - Katerina Deike-Hofmann
- From the Divisions of Radiology (D.P., N.W., K.D.H., H.P.S.) and
Medical Physics in Radiology (V.L.F., J.B., S.G., P.B., M.E.L., A.K.), German
Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg,
Germany; Faculties of Medicine (N.W., M.E.L.) and Physics and Astronomy (V.L.F.,
P.B., M.E.L.), University of Heidelberg, Heidelberg, Germany; and Departments of
Neurology (A.W., W.W.), Neurosurgery (M.S., A.U.), and Neuroradiology (M.B.),
Heidelberg University Hospital, Heidelberg, Germany
| | - Antje Wick
- From the Divisions of Radiology (D.P., N.W., K.D.H., H.P.S.) and
Medical Physics in Radiology (V.L.F., J.B., S.G., P.B., M.E.L., A.K.), German
Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg,
Germany; Faculties of Medicine (N.W., M.E.L.) and Physics and Astronomy (V.L.F.,
P.B., M.E.L.), University of Heidelberg, Heidelberg, Germany; and Departments of
Neurology (A.W., W.W.), Neurosurgery (M.S., A.U.), and Neuroradiology (M.B.),
Heidelberg University Hospital, Heidelberg, Germany
| | - Moritz Scherer
- From the Divisions of Radiology (D.P., N.W., K.D.H., H.P.S.) and
Medical Physics in Radiology (V.L.F., J.B., S.G., P.B., M.E.L., A.K.), German
Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg,
Germany; Faculties of Medicine (N.W., M.E.L.) and Physics and Astronomy (V.L.F.,
P.B., M.E.L.), University of Heidelberg, Heidelberg, Germany; and Departments of
Neurology (A.W., W.W.), Neurosurgery (M.S., A.U.), and Neuroradiology (M.B.),
Heidelberg University Hospital, Heidelberg, Germany
| | - Andreas Unterberg
- From the Divisions of Radiology (D.P., N.W., K.D.H., H.P.S.) and
Medical Physics in Radiology (V.L.F., J.B., S.G., P.B., M.E.L., A.K.), German
Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg,
Germany; Faculties of Medicine (N.W., M.E.L.) and Physics and Astronomy (V.L.F.,
P.B., M.E.L.), University of Heidelberg, Heidelberg, Germany; and Departments of
Neurology (A.W., W.W.), Neurosurgery (M.S., A.U.), and Neuroradiology (M.B.),
Heidelberg University Hospital, Heidelberg, Germany
| | - Wolfgang Wick
- From the Divisions of Radiology (D.P., N.W., K.D.H., H.P.S.) and
Medical Physics in Radiology (V.L.F., J.B., S.G., P.B., M.E.L., A.K.), German
Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg,
Germany; Faculties of Medicine (N.W., M.E.L.) and Physics and Astronomy (V.L.F.,
P.B., M.E.L.), University of Heidelberg, Heidelberg, Germany; and Departments of
Neurology (A.W., W.W.), Neurosurgery (M.S., A.U.), and Neuroradiology (M.B.),
Heidelberg University Hospital, Heidelberg, Germany
| | - Martin Bendszus
- From the Divisions of Radiology (D.P., N.W., K.D.H., H.P.S.) and
Medical Physics in Radiology (V.L.F., J.B., S.G., P.B., M.E.L., A.K.), German
Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg,
Germany; Faculties of Medicine (N.W., M.E.L.) and Physics and Astronomy (V.L.F.,
P.B., M.E.L.), University of Heidelberg, Heidelberg, Germany; and Departments of
Neurology (A.W., W.W.), Neurosurgery (M.S., A.U.), and Neuroradiology (M.B.),
Heidelberg University Hospital, Heidelberg, Germany
| | - Peter Bachert
- From the Divisions of Radiology (D.P., N.W., K.D.H., H.P.S.) and
Medical Physics in Radiology (V.L.F., J.B., S.G., P.B., M.E.L., A.K.), German
Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg,
Germany; Faculties of Medicine (N.W., M.E.L.) and Physics and Astronomy (V.L.F.,
P.B., M.E.L.), University of Heidelberg, Heidelberg, Germany; and Departments of
Neurology (A.W., W.W.), Neurosurgery (M.S., A.U.), and Neuroradiology (M.B.),
Heidelberg University Hospital, Heidelberg, Germany
| | - Mark E. Ladd
- From the Divisions of Radiology (D.P., N.W., K.D.H., H.P.S.) and
Medical Physics in Radiology (V.L.F., J.B., S.G., P.B., M.E.L., A.K.), German
Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg,
Germany; Faculties of Medicine (N.W., M.E.L.) and Physics and Astronomy (V.L.F.,
P.B., M.E.L.), University of Heidelberg, Heidelberg, Germany; and Departments of
Neurology (A.W., W.W.), Neurosurgery (M.S., A.U.), and Neuroradiology (M.B.),
Heidelberg University Hospital, Heidelberg, Germany
| | - Heinz-Peter Schlemmer
- From the Divisions of Radiology (D.P., N.W., K.D.H., H.P.S.) and
Medical Physics in Radiology (V.L.F., J.B., S.G., P.B., M.E.L., A.K.), German
Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg,
Germany; Faculties of Medicine (N.W., M.E.L.) and Physics and Astronomy (V.L.F.,
P.B., M.E.L.), University of Heidelberg, Heidelberg, Germany; and Departments of
Neurology (A.W., W.W.), Neurosurgery (M.S., A.U.), and Neuroradiology (M.B.),
Heidelberg University Hospital, Heidelberg, Germany
| | - Andreas Korzowski
- From the Divisions of Radiology (D.P., N.W., K.D.H., H.P.S.) and
Medical Physics in Radiology (V.L.F., J.B., S.G., P.B., M.E.L., A.K.), German
Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg,
Germany; Faculties of Medicine (N.W., M.E.L.) and Physics and Astronomy (V.L.F.,
P.B., M.E.L.), University of Heidelberg, Heidelberg, Germany; and Departments of
Neurology (A.W., W.W.), Neurosurgery (M.S., A.U.), and Neuroradiology (M.B.),
Heidelberg University Hospital, Heidelberg, Germany
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8
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Zhang H, Zhang H, Zhang Y, Zhou B, Wu L, Yang W, Lei Y, Huang B. Multiparametric MRI-based fusion radiomics for predicting telomerase reverse transcriptase (TERT) promoter mutations and progression-free survival in glioblastoma: a multicentre study. Neuroradiology 2024; 66:81-92. [PMID: 37978079 DOI: 10.1007/s00234-023-03245-3] [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: 08/23/2023] [Accepted: 10/29/2023] [Indexed: 11/19/2023]
Abstract
PURPOSE This study evaluated the performance of multiparametric magnetic resonance imaging (MRI)-based fusion radiomics models (MMFRs) to predict telomerase reverse transcriptase (TERT) promoter mutation status and progression-free survival (PFS) in glioblastoma patients. METHODS We retrospectively analysed 208 glioblastoma patients from two hospitals. Quantitative imaging features were extracted from each patient's T1-weighted, T1-weighted contrast-enhanced, and T2-weighted preoperative images. Using a coarse-to-fine feature selection strategy, four radiomics signature models were constructed based on the three MRI sequences and their combination for TERT promoter mutation status and PFS; model performance was subsequently evaluated. Subgroup analyses were performed by the radiomics signature of TERT promoter mutation status and PFS to distinguish patients who could benefit from prolonged temozolomide chemotherapy cycles. RESULTS TERT promoter mutation status was best predicted by MMFR, with an area under the curve (AUC) of 0.816 and 0.812 for the training and internal validation sets, respectively. The external test set also achieved stable and optimal prediction results (AUC, 0.823). MMFR better predicted patient PFS compared with the single-sequence radiomics signature in the test set (C-index, 0.643 vs 0.561 vs 0.620 vs 0.628). Subgroup analyses showed that more than six cycles of postoperative temozolomide chemotherapy were associated with improved PFS for patients in class 2 (high TERT promoter mutation and high survival rates; HR, 0.222; 95% CI, 0.054 - 0.923; p = 0.025). CONCLUSION MMFR is an effective method to predict TERT promoter mutations and PFS in patients with glioblastoma. Moreover, subgroup analysis could differentiate patients who may benefit from prolonged TMZ chemotherapy cycles.
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Affiliation(s)
- Hongbo Zhang
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510515, China
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, #106 Zhongshan 2Nd Road, Guangzhou, 510080, China
- Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, 517108, China
| | - Hanwen Zhang
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, #3002 SunGangXi Road, Shenzhen, 518035, China
| | - Yuze Zhang
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510515, China
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, #106 Zhongshan 2Nd Road, Guangzhou, 510080, China
| | - Beibei Zhou
- Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, 517108, China
| | - Lei Wu
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, #106 Zhongshan 2Nd Road, Guangzhou, 510080, China
| | - Wanqun Yang
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, #106 Zhongshan 2Nd Road, Guangzhou, 510080, China
| | - Yi Lei
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, #3002 SunGangXi Road, Shenzhen, 518035, China.
| | - Biao Huang
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510515, China.
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, #106 Zhongshan 2Nd Road, Guangzhou, 510080, China.
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9
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Bai L, Jiang J, Zhou J. Assessment of Ki-67 expression levels in IDH-wildtype glioblastoma using logistic regression modelling of VASARI features. Neurosurg Rev 2023; 47:20. [PMID: 38135816 DOI: 10.1007/s10143-023-02258-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 11/29/2023] [Accepted: 12/18/2023] [Indexed: 12/24/2023]
Abstract
To investigate the value of using VASARI signs preoperatively to assess Ki-67 proliferation index levels in patients with IDH-wildtype glioblastoma (GB).Pathological and imaging data of 154 patients with GB confirmed by surgical pathology were retrospectively analysed, and the level of Ki-67 proliferative index was assessed in tumour tissue samples from patients using immunohistochemistry (IHC) staining. Patients were divided into a high and low Ki-67 proliferation index expression group. Two radiologists analysed MRI images of patients with IDH-wildtype GB using the VASARI features system. VASARI parameters between the two groups were statistically analysed to identify characteristic parameters with significant differences and their predictive performance was determined using ROC curves.Among the obtained clinical and VASARI features of IDH-wildtype GB patients, the distribution of Maximum diameter, Proportion of necrosis and Hemorrhage was significantly different between the two groups (all p < 0.05). Multivariate logistic regression analysis showed that Maximum diameter and Hemorrhage were independent risk factors distinguishing the group with high and low expression of Ki-67 proliferative index. ROC curve analysis showed that the logistic regression model achieved an AUC value of 0.730 (95% CI: 0.639, 0.822), sensitivity of 0.628 and specificity of 0.756.Logistic regression modelling of preoperative VASARI features can be used as a reliable tool for predicting the level of Ki-67 proliferative index in IDH-wildtype GB patients, which can help in preoperative development of treatment and follow-up strategies for patients.
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Affiliation(s)
- Liangcai Bai
- Department of Radiology, The Second Hospital of Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou, 730030, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Jian Jiang
- Department of Radiology, The Second Hospital of Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou, 730030, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
| | - Junlin Zhou
- Department of Radiology, The Second Hospital of Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou, 730030, China.
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China.
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China.
- Second Clinical School, Lanzhou University, Lanzhou, China.
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10
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Nakhate V, Gonzalez Castro LN. Artificial intelligence in neuro-oncology. Front Neurosci 2023; 17:1217629. [PMID: 38161802 PMCID: PMC10755952 DOI: 10.3389/fnins.2023.1217629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 11/14/2023] [Indexed: 01/03/2024] Open
Abstract
Artificial intelligence (AI) describes the application of computer algorithms to the solution of problems that have traditionally required human intelligence. Although formal work in AI has been slowly advancing for almost 70 years, developments in the last decade, and particularly in the last year, have led to an explosion of AI applications in multiple fields. Neuro-oncology has not escaped this trend. Given the expected integration of AI-based methods to neuro-oncology practice over the coming years, we set to provide an overview of existing technologies as they are applied to the neuropathology and neuroradiology of brain tumors. We highlight current benefits and limitations of these technologies and offer recommendations on how to appraise novel AI-tools as they undergo consideration for integration into clinical workflows.
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Affiliation(s)
- Vihang Nakhate
- Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - L. Nicolas Gonzalez Castro
- Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- The Center for Neuro-Oncology, Dana–Farber Cancer Institute, Boston, MA, United States
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11
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Li J, Wang Y, Weng J, Qu L, Wu M, Guo M, Sun J, Hu G, Gong X, Liu X, Duan Y, Zhuo Z, Jia W, Liu Y. Automated Determination of the H3 K27-Altered Status in Spinal Cord Diffuse Midline Glioma by Radiomics Based on T2-Weighted MR Images. AJNR Am J Neuroradiol 2023; 44:1464-1470. [PMID: 38081676 PMCID: PMC10714849 DOI: 10.3174/ajnr.a8056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 10/08/2023] [Indexed: 12/18/2023]
Abstract
BACKGROUND AND PURPOSE Conventional MR imaging is not sufficient to discern the H3 K27-altered status of spinal cord diffuse midline glioma. This study aimed to develop a radiomics-based model based on preoperative T2WI to determine the H3 K27-altered status of spinal cord diffuse midline glioma. MATERIALS AND METHODS Ninety-seven patients with confirmed spinal cord diffuse midline gliomas were retrospectively recruited and randomly assigned to the training (n = 67) and test (n = 30) sets. One hundred seven radiomics features were initially extracted from automatically-segmented tumors on T2WI, then 11 features selected by the Pearson correlation coefficient and the Kruskal-Wallis test were used to train and test a logistic regression model for predicting the H3 K27-altered status. Sensitivity analysis was performed using additional random splits of the training and test sets, as well as applying other classifiers for comparison. The performance of the model was evaluated through its accuracy, sensitivity, specificity, and area under the curve. Finally, a prospective set including 28 patients with spinal cord diffuse midline gliomas was used to validate the logistic regression model independently. RESULTS The logistic regression model accurately predicted the H3 K27-altered status with accuracies of 0.833 and 0.786, sensitivities of 0.813 and 0.750, specificities of 0.857 and 0.833, and areas under the curve of 0.839 and 0.818 in the test and prospective sets, respectively. Sensitivity analysis confirmed the robustness of the model, with predictive accuracies of 0.767-0.833. CONCLUSIONS Radiomics signatures based on preoperative T2WI could accurately predict the H3 K27-altered status of spinal cord diffuse midline glioma, providing potential benefits for clinical management.
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Affiliation(s)
- Junjie Li
- From the Department of Radiology (J.L., L.Q., M.W., M.G., J.S., Y.D., Z.Z., Y.L.), Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - YongZhi Wang
- Department of Neurosurgery (Y.W., W.J.), Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Jinyuan Weng
- Department of Medical Imaging Products (J.W., X.G.), Neusoft, Group Ltd., Shenyang, People's Republic of China
| | - Liying Qu
- From the Department of Radiology (J.L., L.Q., M.W., M.G., J.S., Y.D., Z.Z., Y.L.), Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Minghao Wu
- From the Department of Radiology (J.L., L.Q., M.W., M.G., J.S., Y.D., Z.Z., Y.L.), Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Min Guo
- From the Department of Radiology (J.L., L.Q., M.W., M.G., J.S., Y.D., Z.Z., Y.L.), Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Jun Sun
- From the Department of Radiology (J.L., L.Q., M.W., M.G., J.S., Y.D., Z.Z., Y.L.), Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Geli Hu
- Clinical and Technical Support (G.H.), Philips Healthcare, Beijing, People's Republic of China
| | - Xiaodong Gong
- Department of Medical Imaging Products (J.W., X.G.), Neusoft, Group Ltd., Shenyang, People's Republic of China
| | - Xing Liu
- Department of Pathology (X.L.), Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Yunyun Duan
- From the Department of Radiology (J.L., L.Q., M.W., M.G., J.S., Y.D., Z.Z., Y.L.), Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Zhizheng Zhuo
- From the Department of Radiology (J.L., L.Q., M.W., M.G., J.S., Y.D., Z.Z., Y.L.), Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Wenqing Jia
- Department of Neurosurgery (Y.W., W.J.), Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Yaou Liu
- From the Department of Radiology (J.L., L.Q., M.W., M.G., J.S., Y.D., Z.Z., Y.L.), Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China
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12
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Ioannidis GS, Pigott LE, Iv M, Surlan-Popovic K, Wintermark M, Bisdas S, Marias K. Investigating the value of radiomics stemming from DSC quantitative biomarkers in IDH mutation prediction in gliomas. Front Neurol 2023; 14:1249452. [PMID: 38046592 PMCID: PMC10690367 DOI: 10.3389/fneur.2023.1249452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 10/31/2023] [Indexed: 12/05/2023] Open
Abstract
Objective This study aims to assess the value of biomarker based radiomics to predict IDH mutation in gliomas. The patient cohort consists of 160 patients histopathologicaly proven of primary glioma (WHO grades 2-4) from 3 different centers. Methods To quantify the DSC perfusion signal two different mathematical modeling methods were used (Gamma fitting, leakage correction algorithms) considering the assumptions about the compartments contributing in the blood flow between the extra- and intra vascular space. Results The Mean slope of increase (MSI) and the K1 parameter of the bidirectional exchange model exhibited the highest performance with (ACC 74.3% AUROC 74.2%) and (ACC 75% AUROC 70.5%) respectively. Conclusion The proposed framework on DSC-MRI radiogenomics in gliomas has the potential of becoming a reliable diagnostic support tool exploiting the mathematical modeling of the DSC signal to characterize IDH mutation status through a more reproducible and standardized signal analysis scheme for facilitating clinical translation.
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Affiliation(s)
- Georgios S. Ioannidis
- Computational BioMedicine Laboratory (CBML), Institute of Computer Science, Foundation for Research and Technology—Hellas (FORTH), Heraklion, Greece
| | - Laura Elin Pigott
- Institute of Health and Social Care, London South Bank University, London, United Kingdom
- Faculty of Brain Science, Queen Square Institute of Neurology, University College London, London, United Kingdom
- Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery University College London, London, United Kingdom
| | - Michael Iv
- Department of Radiology, Division of Neuroimaging and Neurointervention, Stanford University, Stanford, CA, United States
| | - Katarina Surlan-Popovic
- Department of Radiology, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
- Department of Neuroradiology, University Medical Centre, Ljubljana, Slovenia
| | - Max Wintermark
- Department of Radiology, Division of Neuroimaging and Neurointervention, Stanford University, Stanford, CA, United States
| | - Sotirios Bisdas
- Department of Brain Repair and Rehabilitation, Queen Square Institute of Neurology, UCL, London, United Kingdom
- Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, University College London NHS Foundation Trust, London, United Kingdom
| | - Kostas Marias
- Computational BioMedicine Laboratory (CBML), Institute of Computer Science, Foundation for Research and Technology—Hellas (FORTH), Heraklion, Greece
- Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Heraklion, Greece
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13
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Wei Y, Chen X, Zhu L, Zhang L, Schonlieb CB, Price S, Li C. Multi-Modal Learning for Predicting the Genotype of Glioma. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:3167-3178. [PMID: 37022918 DOI: 10.1109/tmi.2023.3244038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
The isocitrate dehydrogenase (IDH) gene mutation is an essential biomarker for the diagnosis and prognosis of glioma. It is promising to better predict glioma genotype by integrating focal tumor image and geometric features with brain network features derived from MRI. Convolutional neural networks show reasonable performance in predicting IDH mutation, which, however, cannot learn from non-Euclidean data, e.g., geometric and network data. In this study, we propose a multi-modal learning framework using three separate encoders to extract features of focal tumor image, tumor geometrics and global brain networks. To mitigate the limited availability of diffusion MRI, we develop a self-supervised approach to generate brain networks from anatomical multi-sequence MRI. Moreover, to extract tumor-related features from the brain network, we design a hierarchical attention module for the brain network encoder. Further, we design a bi-level multi-modal contrastive loss to align the multi-modal features and tackle the domain gap at the focal tumor and global brain. Finally, we propose a weighted population graph to integrate the multi-modal features for genotype prediction. Experimental results on the testing set show that the proposed model outperforms the baseline deep learning models. The ablation experiments validate the performance of different components of the framework. The visualized interpretation corresponds to clinical knowledge with further validation. In conclusion, the proposed learning framework provides a novel approach for predicting the genotype of glioma.
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14
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Tejani AS. To BERT or not to BERT: advancing non-invasive prediction of tumor biomarkers using transformer-based natural language processing (NLP). Eur Radiol 2023; 33:8014-8016. [PMID: 37740083 DOI: 10.1007/s00330-023-10224-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 08/27/2023] [Accepted: 08/29/2023] [Indexed: 09/24/2023]
Affiliation(s)
- Ali S Tejani
- Department of Radiology, The University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75390, USA.
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15
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Lasocki A, Roberts-Thomson SJ, Gaillard F. Radiogenomics of adult intracranial gliomas after the 2021 World Health Organisation classification: a review of changes, challenges and opportunities. Quant Imaging Med Surg 2023; 13:7572-7581. [PMID: 37969636 PMCID: PMC10644132 DOI: 10.21037/qims-22-1365] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 03/29/2023] [Indexed: 11/17/2023]
Abstract
The classification of diffuse gliomas has undergone substantial changes over the last decade, starting with the 2016 World Health Organisation (WHO) classification, which introduced the importance of molecular markers for glioma diagnosis, in particular, isocitrate dehydrogenase (IDH) status and 1p/19-codeletion. This has spurred research into the correlation of imaging features with the key molecular markers, known as "radiogenomics" or "imaging genomics". Radiogenomics has a variety of possible benefits, including supplementing immunohistochemistry to refine the histological diagnosis and overcoming some of the limitations of the histological assessment. The recent 2021 WHO classification has introduced a variety of changes and continues the trend of increasing the importance of molecular markers in the diagnosis. Key changes include a formal distinction between adult- and paediatric-type diffuse gliomas, the addition of new diagnostic entities, refinements to the nomenclature for IDH-mutant (IDHmut) and IDH-wildtype (IDHwt) gliomas, a shift to grading within tumour types, and the addition of molecular markers as a determinant of tumour grade in addition to phenotype. These changes provide both challenges and opportunities for the field of radiogenomics, which are discussed in this review. This includes implications for the interpretation of research performed prior to the 2021 classification, based on the shift to first classifying gliomas based on genotype ahead of grade, as well as opportunities for future research and priorities for clinical integration.
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Affiliation(s)
- Arian Lasocki
- Department of Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia
- Department of Radiology, The University of Melbourne, Parkville, Victoria, Australia
| | | | - Frank Gaillard
- Department of Radiology, The University of Melbourne, Parkville, Victoria, Australia
- Department of Radiology, The Royal Melbourne Hospital, Parkville, Victoria, Australia
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Lasocki A, Buckland ME, Molinaro T, Xie J, Gaillard F. Radiogenomics Provides Insights into Gliomas Demonstrating Single-Arm 1p or 19q Deletion. AJNR Am J Neuroradiol 2023; 44:1270-1274. [PMID: 37884300 PMCID: PMC10631530 DOI: 10.3174/ajnr.a8034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 09/15/2023] [Indexed: 10/28/2023]
Abstract
BACKGROUND AND PURPOSE IDH-mutant gliomas are further divided on the basis of 1p/19q status: oligodendroglioma, IDH-mutant and 1p/19q-codeleted, and astrocytoma, IDH-mutant (without codeletion). Occasionally, testing may reveal single-arm 1p or 19q deletion (unideletion), which remains within the diagnosis of astrocytoma. Molecular assessment has some limitations, however, raising the possibility that some unideleted tumors could actually be codeleted. This study assessed whether unideleted tumors had MR imaging features and survival more consistent with astrocytomas or oligodendrogliomas. MATERIALS AND METHODS One hundred twenty-one IDH-mutant grade 2-3 gliomas with 1p/19q results were identified. Two neuroradiologists assessed the T2-FLAIR mismatch sign and calcifications, as differentiators of astrocytomas and oligodendrogliomas. MR imaging features and survival were compared among the unideleted tumors, codeleted tumors, and those without 1p or 19q deletion. RESULTS The cohort comprised 65 tumors without 1p or 19q deletion, 12 unideleted tumors, and 44 codeleted. The proportion of unideleted tumors demonstrating the T2-FLAIR mismatch sign (33%) was similar to that in tumors without deletion (49%; P = .39), but significantly higher than codeleted tumors (0%; P = .001). Calcifications were less frequent in unideleted tumors (0%) than in codeleted tumors (25%), but this difference did not reach statistical significance (P = .097). The median survival of patients with unideleted tumors was 7.8 years, which was similar to that in tumors without deletion (8.5 years; P = .72) but significantly shorter than that in codeleted tumors (not reaching median survival after 12 years; P = .013). CONCLUSIONS IDH-mutant gliomas with single-arm 1p or 19q deletion have MR imaging appearance and survival that are similar to those of astrocytomas without 1p or 19q deletion and significantly different from those of 1p/19q-codeleted oligodendrogliomas.
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Affiliation(s)
- Arian Lasocki
- From the Department of Cancer Imaging (A.L.), Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology (A.L.), The University of Melbourne, Parkville, Victoria, Australia
- Department of Radiology (A.L., F.G.), The University of Melbourne, Parkville, Victoria, Australia
| | - Michael E Buckland
- Department of Neuropathology (M.E.B.), Royal Prince Alfred Hospital, Camperdown, New South Wales, Australia
- School of Medical Sciences (M.E.B.), University of Sydney, Camperdown, New South Wales, Australia
| | - Tahlia Molinaro
- Department of Medical Oncology (T.M.), Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Jing Xie
- Centre for Biostatistics and Clinical Trials (J.X.), Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Frank Gaillard
- Department of Radiology (A.L., F.G.), The University of Melbourne, Parkville, Victoria, Australia
- Department of Radiology (F.G.), The Royal Melbourne Hospital, Parkville, Victoria, Australia
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Akkurt BH, Spille DC, Peetz-Dienhart S, Kiolbassa NM, Mawrin C, Musigmann M, Heindel WL, Paulus W, Stummer W, Mannil M, Brokinkel B. Radiomics-Based Prediction of TERT Promotor Mutations in Intracranial High-Grade Meningiomas. Cancers (Basel) 2023; 15:4415. [PMID: 37686690 PMCID: PMC10486806 DOI: 10.3390/cancers15174415] [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: 07/26/2023] [Revised: 08/30/2023] [Accepted: 09/01/2023] [Indexed: 09/10/2023] Open
Abstract
PURPOSE In meningiomas, TERT promotor mutations are rare but qualify the diagnosis of anaplasia, directly impacting adjuvant therapy. Effective screening for patients at risk for promotor mutations could enable more targeted molecular analyses and improve diagnosis and treatment. METHODS Semiautomatic segmentation of intracranial grade 2/3 meningiomas was performed on preoperative magnetic resonance imaging. Discriminatory power to predict TERT promoter mutations was analyzed using a random forest algorithm with an increasing number of radiomic features. Two final models with five and eight features with both fixed and differing radiomics features were developed and adjusted to eliminate random effects and to avoid overfitting. RESULTS A total of 117 image sets including training (N = 94) and test data (N = 23) were analyzed. To eliminate random effects and demonstrate the robustness of our approach, data partitioning and subsequent model development and testing were repeated a total of 100 times (each time with repartitioned training and independent test data). The established five- and eight-feature models with both fixed and different radiomics features enabled the prediction of TERT with similar but excellent performance. The five-feature (different/fixed) model predicted TERT promotor mutation status with a mean AUC of 91.8%/94.3%, mean accuracy of 85.5%/88.9%, mean sensitivity of 88.6%/91.4%, mean specificity of 83.2%/87.0%, and a mean Cohen's Kappa of 71.0%/77.7%. The eight-feature (different/fixed) model predicted TERT promotor mutation status with a mean AUC of 92.7%/94.6%, mean accuracy of 87.3%/88.9%, mean sensitivity of 89.6%/90.6%, mean specificity of 85.5%/87.5%, and a mean Cohen's Kappa of 74.4%/77.6%. Of note, the addition of further features of up to N = 8 only slightly increased the performance. CONCLUSIONS Radiomics-based machine learning enables prediction of TERT promotor mutation status in meningiomas with excellent discriminatory performance. Future analyses in larger cohorts should include grade 1 lesions as well as additional molecular alterations.
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Affiliation(s)
- Burak Han Akkurt
- Department of Radiology, University Hospital Muenster, DE-48149 Muenster, Germany (M.M.)
| | | | - Susanne Peetz-Dienhart
- Institute of Neuropathology, University Hospital Muenster, DE-48149 Muenster, Germany (W.P.)
| | - Nora Maren Kiolbassa
- Department of Neurosurgery, University Hospital Muenster, DE-48149 Muenster, Germany
| | - Christian Mawrin
- Department of Neuropathology, University Hospital Magdeburg, 39120 Magdeburg, Germany
| | - Manfred Musigmann
- Department of Radiology, University Hospital Muenster, DE-48149 Muenster, Germany (M.M.)
| | | | - Werner Paulus
- Institute of Neuropathology, University Hospital Muenster, DE-48149 Muenster, Germany (W.P.)
| | - Walter Stummer
- Department of Neurosurgery, University Hospital Muenster, DE-48149 Muenster, Germany
| | - Manoj Mannil
- Department of Radiology, University Hospital Muenster, DE-48149 Muenster, Germany (M.M.)
- Institute for Diagnostic and Interventional Radiology, Caritas-Hospital, DE-97980 Bad Mergentheim, Germany
| | - Benjamin Brokinkel
- Department of Neurosurgery, University Hospital Muenster, DE-48149 Muenster, Germany
- Institute of Neuropathology, University Hospital Muenster, DE-48149 Muenster, Germany (W.P.)
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Kalaroopan D, Lasocki A. MRI-based deep learning techniques for the prediction of isocitrate dehydrogenase and 1p/19q status in grade 2-4 adult gliomas. J Med Imaging Radiat Oncol 2023; 67:492-498. [PMID: 36919468 DOI: 10.1111/1754-9485.13522] [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: 11/24/2021] [Accepted: 02/16/2023] [Indexed: 03/16/2023]
Abstract
Molecular biomarkers are becoming increasingly important in the classification of intracranial gliomas. While tissue sampling remains the gold standard, there is growing interest in the use of deep learning (DL) techniques to predict these markers. This narrative review with a systematic approach identifies and synthesises the current published data on DL techniques using conventional MRI sequences for predicting isocitrate dehydrogenase (IDH) and 1p/19q-codeletion status in World Health Organisation grade 2-4 gliomas. Three databases were searched for relevant studies. In all, 13 studies met the inclusion criteria after exclusions. Key results, limitations and discrepancies between studies were synthesised. High accuracy has been reported in some studies, but the existing literature has several limitations, including generally small cohort sizes, a paucity of studies with independent testing cohorts and a lack of studies assessing IDH and 1p/19q together. While DL shows promise as a non-invasive means of predicting glioma genotype, addressing these limitations in future research will be important for facilitating clinical translation.
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Affiliation(s)
- Dinusha Kalaroopan
- Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia
| | - Arian Lasocki
- Department of Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia
- Department of Radiology, The University of Melbourne, Melbourne, Victoria, Australia
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Zhong J, Lu J, Zhang G, Mao S, Chen H, Yin Q, Hu Y, Xing Y, Ding D, Ge X, Zhang H, Yao W. An overview of meta-analyses on radiomics: more evidence is needed to support clinical translation. Insights Imaging 2023; 14:111. [PMID: 37336830 DOI: 10.1186/s13244-023-01437-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 04/14/2023] [Indexed: 06/21/2023] Open
Abstract
OBJECTIVE To conduct an overview of meta-analyses of radiomics studies assessing their study quality and evidence level. METHODS A systematical search was updated via peer-reviewed electronic databases, preprint servers, and systematic review protocol registers until 15 November 2022. Systematic reviews with meta-analysis of primary radiomics studies were included. Their reporting transparency, methodological quality, and risk of bias were assessed by PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) 2020 checklist, AMSTAR-2 (A MeaSurement Tool to Assess systematic Reviews, version 2) tool, and ROBIS (Risk Of Bias In Systematic reviews) tool, respectively. The evidence level supporting the radiomics for clinical use was rated. RESULTS We identified 44 systematic reviews with meta-analyses on radiomics research. The mean ± standard deviation of PRISMA adherence rate was 65 ± 9%. The AMSTAR-2 tool rated 5 and 39 systematic reviews as low and critically low confidence, respectively. The ROBIS assessment resulted low, unclear and high risk in 5, 11, and 28 systematic reviews, respectively. We reperformed 53 meta-analyses in 38 included systematic reviews. There were 3, 7, and 43 meta-analyses rated as convincing, highly suggestive, and weak levels of evidence, respectively. The convincing level of evidence was rated in (1) T2-FLAIR radiomics for IDH-mutant vs IDH-wide type differentiation in low-grade glioma, (2) CT radiomics for COVID-19 vs other viral pneumonia differentiation, and (3) MRI radiomics for high-grade glioma vs brain metastasis differentiation. CONCLUSIONS The systematic reviews on radiomics were with suboptimal quality. A limited number of radiomics approaches were supported by convincing level of evidence. CLINICAL RELEVANCE STATEMENT The evidence supporting the clinical application of radiomics are insufficient, calling for researches translating radiomics from an academic tool to a practicable adjunct towards clinical deployment.
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Affiliation(s)
- Jingyu Zhong
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Junjie Lu
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Guangcheng Zhang
- Department of Orthopedics, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
| | - Shiqi Mao
- Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, 200433, China
| | - Haoda Chen
- Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Qian Yin
- Department of Pathology, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
| | - Yangfan Hu
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Yue Xing
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Defang Ding
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Xiang Ge
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Huan Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Weiwu Yao
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China.
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Siakallis L, Topriceanu CC, Panovska-Griffiths J, Bisdas S. The role of DSC MR perfusion in predicting IDH mutation and 1p19q codeletion status in gliomas: meta-analysis and technical considerations. Neuroradiology 2023:10.1007/s00234-023-03154-5. [PMID: 37173578 DOI: 10.1007/s00234-023-03154-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 04/18/2023] [Indexed: 05/15/2023]
Abstract
PURPOSE Isocitrate dehydrogenase (IDH) mutation and 1p19q codeletion status are important for managing glioma patients. However, current practice dictates invasive tissue sampling for histomolecular classification. We investigated the current value of dynamic susceptibility contrast (DSC) MR perfusion imaging as a tool for the non-invasive identification of these biomarkers. METHODS A systematic search of PubMed, Medline, and Embase up to 2023 was performed, and meta-analyses were conducted. We removed studies employing machine learning models or using multiparametric imaging. We used random-effects standardized mean difference (SMD) and bivariate sensitivity-specificity meta-analyses, calculated the area under the hierarchical summary receiver operating characteristic curve (AUC) and performed meta-regressions using technical acquisition parameters (e.g., time to echo [TE], repetition time [TR]) as moderators to explore sources of heterogeneity. For all estimates, 95% confidence intervals (CIs) are provided. RESULTS Sixteen eligible manuscripts comprising 1819 patients were included in the quantitative analyses. IDH mutant (IDHm) gliomas had lower rCBV values compared to their wild-type (IDHwt) counterparts. The highest SMD was observed for rCBVmean, rCBVmax, and rCBV 75th percentile (SMD≈ - 0.8, 95% CI ≈ [- 1.2, - 0.5]). In meta-regression, shorter TEs, shorter TRs, and smaller slice thicknesses were linked to higher absolute SMDs. When discriminating IDHm from IDHwt, the highest pooled specificity was observed for rCBVmean (82% [72, 89]), and the highest pooled sensitivity (i.e., 92% [86, 93]) and AUC (i.e., 0.91) for rCBV 10th percentile. In the bivariate meta-regression, shorter TEs and smaller slice gaps were linked to higher pooled sensitivities. In IDHm, 1p19q codeletion was associated with higher rCBVmean (SMD = 0.9 [0.2, 1.5]) and rCBV 90th percentile (SMD = 0.9 [0.1, 1.7]) values. CONCLUSIONS Identification of vascular signatures predictive of IDH and 1p19q status is a novel promising application of DSC perfusion. Standardization of acquisition protocols and post-processing of DSC perfusion maps are warranted before widespread use in clinical practice.
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Affiliation(s)
- Loizos Siakallis
- University College London (UCL) Queen Square Institute of Neurology, London, UK.
- Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, University College London Hospitals (UCLH) NHS Foundation Trust, London, UK.
| | - Constantin-Cristian Topriceanu
- University College London (UCL) Queen Square Institute of Neurology, London, UK
- Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, University College London Hospitals (UCLH) NHS Foundation Trust, London, UK
- UCL Institute of Cardiovascular Science, University College London, London, UK
| | - Jasmina Panovska-Griffiths
- The Big Data Institute and the Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The Queen's College, University of Oxford, Oxford, UK
| | - Sotirios Bisdas
- Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, University College London Hospitals (UCLH) NHS Foundation Trust, London, UK
- Department of Brain Repair & Rehabilitation, Queen Square Institute of Neurology, University College London, London, UK
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Landers MJF, Brouwers HB, Kortman GJ, Boukrab I, De Baene W, Rutten GJM. Oligodendrogliomas tend to infiltrate the frontal aslant tract, whereas astrocytomas tend to displace it. Neuroradiology 2023:10.1007/s00234-023-03153-6. [PMID: 37127719 DOI: 10.1007/s00234-023-03153-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 04/13/2023] [Indexed: 05/03/2023]
Abstract
INTRODUCTION MR-tractography is increasingly used in neurosurgical practice to evaluate the anatomical relationships between glioma and nearby subcortical tracts. In some patients, the subcortical tracts seem displaced by the glioma, while in other patients, the subcortical tracts seem infiltrated without displacement. At this point, it is unknown whether these different patterns are related to tumor type. The aim of this exploratory study was to investigate whether tumor type is related to the spatial tractography pattern of the frontal aslant tract (FAT) in low-grade gliomas (LGGs). METHODS In 64 IDH-mutated LGG patients, the FAT was generated using a pipeline for automatic tractography. In 41 patients, the glioma adjoined the FAT, and four blinded reviewers independently assessed the following two dichotomous categories (yes/no): (i) glioma displaces the tract, and (ii) glioma infiltrates the tract. RESULTS Fisher's exact tests demonstrated strong and significant positive associations between displacement and astrocytomas (p = .002, φ = .497) and infiltration and oligodendrogliomas (p = .004, φ = .484). The interobserver agreement was good for both categories: (i) κ = 0.76 and (ii) κ = 0.71. CONCLUSION High sensitivity but low specificity for displacement in astrocytomas demonstrates that in the case of an astrocytoma, the tract is most likely displaced, but that displacement in itself is not necessarily predictive for astrocytomas, as oligodendrogliomas may both infiltrate and displace a tract. Overall, these results demonstrate that oligodendrogliomas tend to infiltrate the nearby subcortical tract, whereas astrocytomas only tend to displace it.
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Affiliation(s)
- M J F Landers
- Department of Neurosurgery, Elisabeth-Tweesteden Hospital Tilburg, Tilburg, The Netherlands.
- Department of Cognitive Neuropsychology, Tilburg University, Tilburg, The Netherlands.
| | - H B Brouwers
- Department of Neurosurgery, Elisabeth-Tweesteden Hospital Tilburg, Tilburg, The Netherlands
| | - G J Kortman
- Department of Neuroradiology, Elisabeth-Tweesteden Hospital Tilburg, Tilburg, The Netherlands
| | - I Boukrab
- Department of Neuroradiology, Elisabeth-Tweesteden Hospital Tilburg, Tilburg, The Netherlands
| | - W De Baene
- Department of Neurosurgery, Elisabeth-Tweesteden Hospital Tilburg, Tilburg, The Netherlands
- Department of Cognitive Neuropsychology, Tilburg University, Tilburg, The Netherlands
| | - G J M Rutten
- Department of Neurosurgery, Elisabeth-Tweesteden Hospital Tilburg, Tilburg, The Netherlands
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Spadarella G, Stanzione A, Akinci D'Antonoli T, Andreychenko A, Fanni SC, Ugga L, Kotter E, Cuocolo R. Systematic review of the radiomics quality score applications: an EuSoMII Radiomics Auditing Group Initiative. Eur Radiol 2023; 33:1884-1894. [PMID: 36282312 PMCID: PMC9935718 DOI: 10.1007/s00330-022-09187-3] [Citation(s) in RCA: 44] [Impact Index Per Article: 44.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 08/31/2022] [Accepted: 09/19/2022] [Indexed: 11/25/2022]
Abstract
OBJECTIVE The main aim of the present systematic review was a comprehensive overview of the Radiomics Quality Score (RQS)-based systematic reviews to highlight common issues and challenges of radiomics research application and evaluate the relationship between RQS and review features. METHODS The literature search was performed on multiple medical literature archives according to PRISMA guidelines for systematic reviews that reported radiomic quality assessment through the RQS. Reported scores were converted to a 0-100% scale. The Mann-Whitney and Kruskal-Wallis tests were used to compare RQS scores and review features. RESULTS The literature research yielded 345 articles, from which 44 systematic reviews were finally included in the analysis. Overall, the median of RQS was 21.00% (IQR = 11.50). No significant differences of RQS were observed in subgroup analyses according to targets (oncological/not oncological target, neuroradiology/body imaging focus and one imaging technique/more than one imaging technique, characterization/prognosis/detection/other). CONCLUSIONS Our review did not reveal a significant difference of quality of radiomic articles reported in systematic reviews, divided in different subgroups. Furthermore, low overall methodological quality of radiomics research was found independent of specific application domains. While the RQS can serve as a reference tool to improve future study designs, future research should also be aimed at improving its reliability and developing new tools to meet an ever-evolving research space. KEY POINTS • Radiomics is a promising high-throughput method that may generate novel imaging biomarkers to improve clinical decision-making process, but it is an inherently complex analysis and often lacks reproducibility and generalizability. • The Radiomics Quality Score serves a necessary role as the de facto reference tool for assessing radiomics studies. • External auditing of radiomics studies, in addition to the standard peer-review process, is valuable to highlight common limitations and provide insights to improve future study designs and practical applicability of the radiomics models.
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Affiliation(s)
- Gaia Spadarella
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy.
| | - Tugba Akinci D'Antonoli
- Institute of Radiology and Nuclear Medicine, Cantonal Hospital Baselland, Liestal, Switzerland
| | - Anna Andreychenko
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Healthcare Department, Moscow, Russia
| | | | - Lorenzo Ugga
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Elmar Kotter
- Department of Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Renato Cuocolo
- Department of Medicine, Surgery, and Dentistry, University of Salerno, Baronissi, Italy
- Augmented Reality for Health Monitoring Laboratory (ARHeMLab), Department of Electrical Engineering and Information Technology, University of Naples "Federico II", Naples, Italy
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23
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Shen N, Lv W, Li S, Liu D, Xie Y, Zhang J, Zhang J, Jiang J, Jiang R, Zhu W. Noninvasive Evaluation of the Notch Signaling Pathway via Radiomic Signatures Based on Multiparametric MRI in Association With Biological Functions of Patients With Glioma: A Multi-institutional Study. J Magn Reson Imaging 2023; 57:884-896. [PMID: 35929909 DOI: 10.1002/jmri.28378] [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: 04/14/2022] [Revised: 07/18/2022] [Accepted: 07/18/2022] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Noninvasive determination of Notch signaling is important for prognostic evaluation and therapeutic intervention in glioma. PURPOSE To predict Notch signaling using multiparametric (mp) MRI radiomics and correlate with biological characteristics in gliomas. STUDY TYPE Retrospective. POPULATION A total of 63 patients for model construction and 47 patients from two public databases for external testing. FIELD STRENGTH/SEQUENCE A 1.5 T and 3.0 T, T1-weighted imaging (T1WI), T2WI, T2 fluid attenuated inversion recovery (FLAIR), contrast-enhanced (CE)-T1WI. ASSESSMENT Radiomic features were extracted from CE-T1WI, T1WI, T2WI, and T2FLAIR and imaging signatures were selected using a least absolute shrinkage and selection operator. Diagnostic performance was compared between single modality and a combined mpMRI radiomics model. A radiomic-clinical nomogram was constructed incorporating the mpMRI radiomic signature and Karnofsky Performance score. The performance was validated in the test set. The radiomic signatures were correlated with immunohistochemistry (IHC) analysis of downstream Notch pathway components. STATISTICAL TESTS Receiver operating characteristic curve, decision curve analysis (DCA), Pearson correlation, and Hosmer-Lemeshow test. A P value < 0.05 was considered statistically significant. RESULTS The radiomic signature derived from the combination of all sequences numerically showed highest area under the curve (AUC) in both training and external test sets (AUCs of 0.857 and 0.823). The radiomics nomogram that incorporated the mpMRI radiomic signature and KPS status resulted in AUCs of 0.891 and 0.859 in the training and test sets. The calibration curves showed good agreement between prediction and observation in both sets (P= 0.279 and 0.170, respectively). DCA confirmed the clinical usefulness of the nomogram. IHC identified Notch pathway inactivation and the expression levels of Hes1 correlated with higher combined radiomic scores (r = -0.711) in Notch1 mutant tumors. DATA CONCLUSION The mpMRI-based radiomics nomogram may reflect the intratumor heterogeneity associated with downstream biofunction that predicts Notch signaling in a noninvasive manner. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Nanxi Shen
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wenzhi Lv
- Department of Artificial Intelligence, Julei Technology Company, Wuhan, China
| | - Shihui Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Dong Liu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yan Xie
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ju Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jiaxuan Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jingjing Jiang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Rifeng Jiang
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Bhandari A, Scott L, Weilbach M, Marwah R, Lasocki A. Assessment of artificial intelligence (AI) reporting methodology in glioma MRI studies using the Checklist for AI in Medical Imaging (CLAIM). Neuroradiology 2023; 65:907-913. [PMID: 36746792 PMCID: PMC10105653 DOI: 10.1007/s00234-023-03126-9] [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: 09/27/2022] [Accepted: 01/30/2023] [Indexed: 02/08/2023]
Abstract
PURPOSE The Checklist for Artificial Intelligence in Medical Imaging (CLAIM) is a recently released guideline designed for the optimal reporting methodology of artificial intelligence (AI) studies. Gliomas are the most common form of primary malignant brain tumour and numerous outcomes derived from AI algorithms such as grading, survival, treatment-related effects and molecular status have been reported. The aim of the study is to evaluate the AI reporting methodology for outcomes relating to gliomas in magnetic resonance imaging (MRI) using the CLAIM criteria. METHODS A literature search was performed on three databases pertaining to AI augmentation of glioma MRI, published between the start of 2018 and the end of 2021 RESULTS: A total of 4308 articles were identified and 138 articles remained after screening. These articles were categorised into four main AI tasks: grading (n= 44), predicting molecular status (n= 50), predicting survival (n= 25) and distinguishing true tumour progression from treatment-related effects (n= 10). The average CLAIM score was 20/42 (range: 10-31). Studies most consistently reported the scientific background and clinical role of their AI approach. Areas of improvement were identified in the reporting of data collection, data management, ground truth and validation of AI performance. CONCLUSION AI may be a means of producing high-accuracy results for certain tasks in glioma MRI; however, there remain issues with reporting quality. AI reporting guidelines may aid in a more reproducible and standardised approach to reporting and will aid in clinical integration.
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Affiliation(s)
- Abhishta Bhandari
- Townsville University Hospital, 100 Angus Smith Drive, Townsville, QLD, 4814, Australia. .,School of Medicine and Dentistry, James Cook University, 1 James Cook Drive, Townsville, QLD, 4814, Australia.
| | - Luke Scott
- Cairns Hospital, 165 Esplanade, Cairns, QLD, 4870, Australia
| | - Manuela Weilbach
- Redcliffe Hospital, Anzac Avenue, Redcliffe, QLD, 4020, Australia
| | - Ravi Marwah
- Townsville University Hospital, 100 Angus Smith Drive, Townsville, QLD, 4814, Australia.,School of Medicine and Dentistry, James Cook University, 1 James Cook Drive, Townsville, QLD, 4814, Australia
| | - Arian Lasocki
- Department of Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia
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Hosseini SA, Hosseini E, Hajianfar G, Shiri I, Servaes S, Rosa-Neto P, Godoy L, Nasrallah MP, O’Rourke DM, Mohan S, Chawla S. MRI-Based Radiomics Combined with Deep Learning for Distinguishing IDH-Mutant WHO Grade 4 Astrocytomas from IDH-Wild-Type Glioblastomas. Cancers (Basel) 2023; 15:cancers15030951. [PMID: 36765908 PMCID: PMC9913426 DOI: 10.3390/cancers15030951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Revised: 01/30/2023] [Accepted: 01/30/2023] [Indexed: 02/05/2023] Open
Abstract
This study aimed to investigate the potential of quantitative radiomic data extracted from conventional MR images in discriminating IDH-mutant grade 4 astrocytomas from IDH-wild-type glioblastomas (GBMs). A cohort of 57 treatment-naïve patients with IDH-mutant grade 4 astrocytomas (n = 23) and IDH-wild-type GBMs (n = 34) underwent anatomical imaging on a 3T MR system with standard parameters. Post-contrast T1-weighted and T2-FLAIR images were co-registered. A semi-automatic segmentation approach was used to generate regions of interest (ROIs) from different tissue components of neoplasms. A total of 1050 radiomic features were extracted from each image. The data were split randomly into training and testing sets. A deep learning-based data augmentation method (CTGAN) was implemented to synthesize 200 datasets from the training sets. A total of 18 classifiers were used to distinguish two genotypes of grade 4 astrocytomas. From generated data using 80% training set, the best discriminatory power was obtained from core tumor regions overlaid on post-contrast T1 using the K-best feature selection algorithm and a Gaussian naïve Bayes classifier (AUC = 0.93, accuracy = 0.92, sensitivity = 1, specificity = 0.86, PR_AUC = 0.92). Similarly, high diagnostic performances were obtained from original and generated data using 50% and 30% training sets. Our findings suggest that conventional MR imaging-based radiomic features combined with machine/deep learning methods may be valuable in discriminating IDH-mutant grade 4 astrocytomas from IDH-wild-type GBMs.
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Affiliation(s)
- Seyyed Ali Hosseini
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montréal, QC H4H 1R3, Canada
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montréal, QC H3A 2B4, Canada
- Correspondence: (S.A.H.); (S.C.); Tel.: +1-438-929-6575 (S.A.H.); +1-215-615-1662 (S.C.)
| | - Elahe Hosseini
- Department of Electrical and Computer Engineering, Kharazmi University, Tehran 15719-14911, Iran
| | - Ghasem Hajianfar
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran 19956-14331, Iran
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland
| | - Stijn Servaes
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montréal, QC H4H 1R3, Canada
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montréal, QC H3A 2B4, Canada
| | - Pedro Rosa-Neto
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montréal, QC H4H 1R3, Canada
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montréal, QC H3A 2B4, Canada
| | - Laiz Godoy
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - MacLean P. Nasrallah
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Donald M. O’Rourke
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Suyash Mohan
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sanjeev Chawla
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA
- Correspondence: (S.A.H.); (S.C.); Tel.: +1-438-929-6575 (S.A.H.); +1-215-615-1662 (S.C.)
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26
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Ahn SH, Ahn SS, Park YW, Park CJ, Lee SK. Association of dynamic susceptibility contrast- and dynamic contrast-enhanced magnetic resonance imaging parameters with molecular marker status in lower-grade gliomas: A retrospective study. Neuroradiol J 2023; 36:49-58. [PMID: 35532193 PMCID: PMC9893160 DOI: 10.1177/19714009221098369] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
PURPOSE Molecular marker status is clinically relevant for treatment planning and predicting the prognosis of gliomas. This study aimed to assess whether quantitative imaging parameters from dynamic susceptibility contrast- (DSC-) and dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) can predict the molecular marker status of lower-grade gliomas (LGGs). MATERIALS AND METHODS Overall, 132 patients with LGGs who underwent DSC- and DCE-MRI were retrospectively enrolled. Statuses of relevant molecular markers including isocitrate dehydrogenase isoenzyme (IDH), 1p19q codeletion, epidermal growth factor receptor (EGFR), O6-methylguanine-DNA methyltransferase (MGMT), and telomerase reverse transcriptase (TERT) were collected. For each molecular marker, age, tumor diameter and location, and DSC- and DCE-MRI parameters, including the normalized cerebral blood volume (nCBV), volume transfer constant (Ktrans), rate transfer coefficient (Kep), extravascular extracellular volume fraction (Ve), and plasma volume fraction (Vp), were compared. Multivariable logistic regression analyses were performed. RESULTS The nCBV was significantly lower in LGGs with IDH mutation (p = .001) and TERT mutation (p = .027) than those without these mutations. Ktrans (p = .034), Ve (p = .023), and Vp (p = .044) values were significantly lower in MGMT methylated LGGs than in MGMT unmethylated LGGs. Perfusion parameters were not significantly associated with EGFR amplification and 1p19q codeletion. Young age (p < .001) and small diameter (p = .001) were significantly associated with IDH mutation. The nCBV was independently associated with IDH status (AUC, 0.817; 95% CI: 0.739-0.894). CONCLUSIONS DSC- and DCE-MRI parameters demonstrated correlations with molecular markers of LGGs. Especially, the nCBV can be helpful in predicting the IDH mutation status.
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Affiliation(s)
- Sung Hee Ahn
- Department of Radiology, Yonsei University College of
Medicine, Seoul, Korea
| | - Sung Soo Ahn
- Department of Radiology, Yonsei University College of
Medicine, Seoul, Korea
| | - Yae Won Park
- Department of Radiology, Yonsei University College of
Medicine, Seoul, Korea
| | - Chae Jung Park
- Department of Radiology, Yonsei University College of
Medicine, Seoul, Korea
| | - Seung-Koo Lee
- Department of Radiology, Yonsei University College of
Medicine, Seoul, Korea
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Lasocki A, Abdalla G, Chow G, Thust SC. Imaging features associated with H3 K27-altered and H3 G34-mutant gliomas: a narrative systematic review. Cancer Imaging 2022; 22:63. [DOI: 10.1186/s40644-022-00500-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 10/23/2022] [Indexed: 11/18/2022] Open
Abstract
Abstract
Background
Advances in molecular diagnostics accomplished the discovery of two malignant glioma entities harboring alterations in the H3 histone: diffuse midline glioma, H3 K27-altered and diffuse hemispheric glioma, H3 G34-mutant. Radiogenomics research, which aims to correlate tumor imaging features with genotypes, has not comprehensively examined histone-altered gliomas (HAG). The aim of this research was to synthesize the current published data on imaging features associated with HAG.
Methods
A systematic search was performed in March 2022 using PubMed and the Cochrane Library, identifying studies on the imaging features associated with H3 K27-altered and/or H3 G34-mutant gliomas.
Results
Forty-seven studies fulfilled the inclusion criteria, the majority on H3 K27-altered gliomas. Just under half (21/47) were case reports or short series, the remainder being diagnostic accuracy studies. Despite heterogeneous methodology, some themes emerged. In particular, enhancement of H3 K27M-altered gliomas is variable and can be less than expected given their highly malignant behavior. Low apparent diffusion coefficient values have been suggested as a biomarker of H3 K27-alteration, but high values do not exclude this genotype. Promising correlations between high relative cerebral blood volume values and H3 K27-alteration require further validation. Limited data on H3 G34-mutant gliomas suggest some morphologic overlap with 1p/19q-codeleted oligodendrogliomas.
Conclusions
The existing data are limited, especially for H3 G34-mutant gliomas and artificial intelligence techniques. Current evidence indicates that imaging-based predictions of HAG are insufficient to replace histological assessment. In particular, H3 K27-altered gliomas should be considered when occurring in typical midline locations irrespective of enhancement characteristics.
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Uribe-Cardenas R, Giantini-Larsen AM, Garton A, Juthani RG, Schwartz TH. Innovations in the Diagnosis and Surgical Management of Low-Grade Gliomas. World Neurosurg 2022; 166:321-327. [PMID: 36192864 DOI: 10.1016/j.wneu.2022.06.070] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 06/14/2022] [Indexed: 12/15/2022]
Abstract
Low-grade gliomas are a broad category of tumors that can manifest at different stages of life. As a group, their prognosis has historically been considered to be favorable, and surgery is a mainstay of treatment. Advances in the molecular characterization of individual lesions has led to newer classification systems, a better understanding of the biological behavior of different neoplasms, and the identification of previously unrecognized entities. New prospective genetic and molecular data will help delineate better treatment paradigms and will continue to change the taxonomy of central nervous system tumors in the coming years. Advances in the field of radiomics will help predict the molecular profile of a particular tumor through noninvasive testing. Similarly, more precise methods of intraoperative tumor tissue analysis will aid surgical planning. Improved surgical outcomes propelled by novel surgical techniques and intraoperative adjuncts and emerging forms of medical treatment in the field of immunotherapy have enriched the management of these lesions. We review the contemporary management and innovations in the treatment of low-grade gliomas.
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Affiliation(s)
- Rafael Uribe-Cardenas
- Department of Neurological Surgery, Weill Cornell Medical College, New York Presbyterian Hospital, New York, New York, USA
| | - Alexandra M Giantini-Larsen
- Department of Neurological Surgery, Weill Cornell Medical College, New York Presbyterian Hospital, New York, New York, USA
| | - Andrew Garton
- Department of Neurological Surgery, Weill Cornell Medical College, New York Presbyterian Hospital, New York, New York, USA
| | - Rupa Gopalan Juthani
- Department of Neurological Surgery, Weill Cornell Medical College, New York Presbyterian Hospital, New York, New York, USA.
| | - Theodore H Schwartz
- Department of Neurological Surgery, Weill Cornell Medical College, New York Presbyterian Hospital, New York, New York, USA
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29
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Karabacak M, Ozkara BB, Mordag S, Bisdas S. Deep learning for prediction of isocitrate dehydrogenase mutation in gliomas: a critical approach, systematic review and meta-analysis of the diagnostic test performance using a Bayesian approach. Quant Imaging Med Surg 2022; 12:4033-4046. [PMID: 35919062 PMCID: PMC9338374 DOI: 10.21037/qims-22-34] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 05/25/2022] [Indexed: 11/08/2022]
Abstract
Background Conventionally, identifying isocitrate dehydrogenase (IDH) mutation in gliomas is based on histopathological analysis of tissue specimens acquired via stereotactic biopsy or definitive resection. Accurate pre-treatment prediction of IDH mutation status using magnetic resonance imaging (MRI) can guide clinical decision-making. We aim to evaluate the diagnostic performance of deep learning (DL) to determine IDH mutation status in gliomas. Methods A systematic search of Cochrane Library, Web of Science, Medline, and Scopus was conducted to identify relevant publications until August 1, 2021. Articles were included if all the following criteria were met: (I) patients with histopathologically confirmed World Health Organization (WHO) grade II, III, or IV gliomas; (II) histopathological examination with the IDH mutation; (III) DL was used to predict the IDH mutation status; (IV) sufficient data for reconstruction of confusion matrices in terms of the diagnostic performance of the DL algorithms; and (V) original research articles. Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) and Checklist for Artificial Intelligence in Medical Imaging (CLAIM) was used to assess the studies' quality. Bayes theorem was utilized to calculate the posttest probability. Results Four studies with a total of 1,295 patients were included. In the training set, the pooled sensitivity, specificity, and area under the summary receiver operating characteristic (SROC) curve were 93.9%, 90.9% and 0.958, respectively. In the validation set, the pooled sensitivity, specificity, and area under the SROC curve were 90.8%, 85.5% and 0.939, respectively. With a known pretest probability of 80.2%, the Bayes theorem yielded a posttest probability of 97.6% and 96.0% for a positive test and 27.0% and 30.6% for a negative test for training sets and validation sets, respectively. Discussion This is the first meta-analysis that summarizes the diagnostic performance of DL in predicting IDH mutation status in gliomas via the Bayes theorem. DL algorithms demonstrate excellent diagnostic performance in predicting IDH mutation in gliomas. Radiomic features associated with IDH mutation, and its underlying pathophysiology extracted from advanced MRI may improve prediction probability. However, more studies are required to optimize and increase its reliability. Limitations include obtaining some data via email and lack of training and test sets statistics.
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Affiliation(s)
- Mert Karabacak
- Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Cerrahpasa, Istanbul, Turkey
| | - Burak Berksu Ozkara
- Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Cerrahpasa, Istanbul, Turkey
| | - Seren Mordag
- Faculty of Medicine, Hacettepe University, Sihhiye, Ankara, Turkey
| | - Sotirios Bisdas
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, London, UK
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Kikuchi K, Togao O, Yamashita K, Momosaka D, Kikuchi Y, Kuga D, Hata N, Mizoguchi M, Yamamoto H, Iwaki T, Hiwatashi A, Ishigami K. Quantitative relaxometry using synthetic MRI could be better than T2-FLAIR mismatch sign for differentiation of IDH-mutant gliomas: a pilot study. Sci Rep 2022; 12:9197. [PMID: 35654812 PMCID: PMC9163057 DOI: 10.1038/s41598-022-13036-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Accepted: 05/19/2022] [Indexed: 11/09/2022] Open
Abstract
This study aimed to determine whether quantitative relaxometry using synthetic magnetic resonance imaging (SyMRI) could differentiate between two diffuse glioma groups with isocitrate dehydrogenase (IDH)-mutant tumors, achieving an increased sensitivity compared to the qualitative T2-fluid-attenuated inversion recovery (FLAIR) mismatch sign. Between May 2019 and May 2020, thirteen patients with IDH-mutant diffuse gliomas, including seven with astrocytomas and six with oligodendrogliomas, were evaluated. Five neuroradiologists independently evaluated the presence of the qualitative T2-FLAIR mismatch sign. Interrater agreement on the presence of the T2-FLAIR mismatch sign was calculated using the Fleiss kappa coefficient. SyMRI parameters (T1 and T2 relaxation times and proton density) were measured in the gliomas and compared by the Mann-Whitney U test. Receiver operating characteristic curve analysis was used to evaluate the diagnostic performance. The sensitivity, specificity, and kappa coefficient were 57.1%, 100%, and 0.60, respectively, for the qualitative T2-FLAIR mismatch sign. The two types of diffuse gliomas could be differentiated using a cutoff value of 178 ms for the T2 relaxation time parameter with 100% sensitivity, specificity, accuracy, and positive and negative predictive values, with an area under the curve (AUC) of 1.00. Quantitative relaxometry using SyMRI could differentiate astrocytomas from oligodendrogliomas, achieving an increased sensitivity and objectivity compared to the qualitative T2-FLAIR mismatch sign.
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Affiliation(s)
- Kazufumi Kikuchi
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Osamu Togao
- Department of Molecular Imaging and Diagnosis, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan.
| | - Koji Yamashita
- National Hospital Organization, Kyushu Medical Center, Fukuoka, Japan
| | - Daichi Momosaka
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Yoshitomo Kikuchi
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Daisuke Kuga
- Department of Neurosurgery, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Nobuhiro Hata
- Department of Neurosurgery, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Masahiro Mizoguchi
- Department of Neurosurgery, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Hidetaka Yamamoto
- Department of Anatomic Pathology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Toru Iwaki
- Department of Neuropathology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Akio Hiwatashi
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Kousei Ishigami
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
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Tozzi AE, Fabozzi F, Eckley M, Croci I, Dell’Anna VA, Colantonio E, Mastronuzzi A. Gaps and Opportunities of Artificial Intelligence Applications for Pediatric Oncology in European Research: A Systematic Review of Reviews and a Bibliometric Analysis. Front Oncol 2022; 12:905770. [PMID: 35712463 PMCID: PMC9194810 DOI: 10.3389/fonc.2022.905770] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Accepted: 05/04/2022] [Indexed: 12/23/2022] Open
Abstract
The application of artificial intelligence (AI) systems is emerging in many fields in recent years, due to the increased computing power available at lower cost. Although its applications in various branches of medicine, such as pediatric oncology, are many and promising, its use is still in an embryonic stage. The aim of this paper is to provide an overview of the state of the art regarding the AI application in pediatric oncology, through a systematic review of systematic reviews, and to analyze current trends in Europe, through a bibliometric analysis of publications written by European authors. Among 330 records found, 25 were included in the systematic review. All papers have been published since 2017, demonstrating only recent attention to this field. The total number of studies included in the selected reviews was 674, with a third including an author with a European affiliation. In bibliometric analysis, 304 out of the 978 records found were included. Similarly, the number of publications began to dramatically increase from 2017. Most explored AI applications regard the use of diagnostic images, particularly radiomics, as well as the group of neoplasms most involved are the central nervous system tumors. No evidence was found regarding the use of AI for process mining, clinical pathway modeling, or computer interpreted guidelines to improve the healthcare process. No robust evidence is yet available in any of the domains investigated by systematic reviews. However, the scientific production in Europe is significant and consistent with the topics covered in systematic reviews at the global level. The use of AI in pediatric oncology is developing rapidly with promising results, but numerous gaps and challenges persist to validate its utilization in clinical practice. An important limitation is the need for large datasets for training algorithms, calling for international collaborative studies.
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Affiliation(s)
- Alberto Eugenio Tozzi
- Multifactorial and Complex Diseases Research Area, Bambino Gesù Children’s Hospital, Istituto di Ricerca e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Francesco Fabozzi
- Department of Onco Hematology and Cell and Gene Therapy, Bambino Gesù Pediatric Hospital, Istituto di Ricerca e Cura a Carattere Scientifico (IRCCS), Rome, Italy
- Department of Pediatrics, University of Rome Tor Vergata, Rome, Italy
| | - Megan Eckley
- Department of Onco Hematology and Cell and Gene Therapy, Bambino Gesù Pediatric Hospital, Istituto di Ricerca e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Ileana Croci
- Multifactorial and Complex Diseases Research Area, Bambino Gesù Children’s Hospital, Istituto di Ricerca e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Vito Andrea Dell’Anna
- Department of Onco Hematology and Cell and Gene Therapy, Bambino Gesù Pediatric Hospital, Istituto di Ricerca e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Erica Colantonio
- Department of Onco Hematology and Cell and Gene Therapy, Bambino Gesù Pediatric Hospital, Istituto di Ricerca e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Angela Mastronuzzi
- Department of Onco Hematology and Cell and Gene Therapy, Bambino Gesù Pediatric Hospital, Istituto di Ricerca e Cura a Carattere Scientifico (IRCCS), Rome, Italy
- *Correspondence: Angela Mastronuzzi,
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32
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Lasocki A, Buckland ME, Drummond KJ, Wei H, Xie J, Christie M, Neal A, Gaillard F. Conventional MRI features can predict the molecular subtype of adult grade 2-3 intracranial diffuse gliomas. Neuroradiology 2022; 64:2295-2305. [PMID: 35606654 PMCID: PMC9643259 DOI: 10.1007/s00234-022-02975-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 05/04/2022] [Indexed: 12/02/2022]
Abstract
Purpose Molecular biomarkers are important for classifying intracranial gliomas, prompting research into correlating imaging with genotype (“radiogenomics”). A limitation of the existing radiogenomics literature is the paucity of studies specifically characterizing grade 2–3 gliomas into the three key molecular subtypes. Our study investigated the accuracy of multiple different conventional MRI features for genotype prediction. Methods Grade 2–3 gliomas diagnosed between 2007 and 2013 were identified. Two neuroradiologists independently assessed nine conventional MRI features. Features with better inter-observer agreement (κ ≥ 0.6) proceeded to consensus assessment. MRI features were correlated with genotype, classified as IDH-mutant and 1p/19q-codeleted (IDHmut/1p19qcodel), IDH-mutant and 1p/19q-intact (IDHmut/1p19qint), or IDH-wildtype (IDHwt). For IDHwt tumors, additional molecular markers of glioblastoma were noted. Results One hundred nineteen patients were included. T2-FLAIR mismatch (stratified as > 50%, 25–50%, or < 25%) was the most predictive feature across genotypes (p < 0.001). All 30 tumors with > 50% mismatch were IDHmut/1p19qint, and all seven with 25–50% mismatch. Well-defined margins correlated with IDHmut/1p19qint status on univariate analysis (p < 0.001), but this related to correlation with T2-FLAIR mismatch; there was no longer an association when considering only tumors with < 25% mismatch (p = 0.386). Enhancement (p = 0.001), necrosis (p = 0.002), and hemorrhage (p = 0.027) correlated with IDHwt status (especially “molecular glioblastoma”). Calcification correlated with IDHmut/1p19qcodel status (p = 0.003). A simple, step-wise algorithm incorporating these features, when present, correctly predicted genotype with a positive predictive value 91.8%. Conclusion T2-FLAIR mismatch strongly predicts IDHmut/1p19qint even with a lower threshold of ≥ 25% mismatch and outweighs other features. Secondary features include enhancement, necrosis and hemorrhage (predicting IDHwt, especially “molecular glioblastoma”), and calcification (predicting IDHmut/1p19qcodel).
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Affiliation(s)
- Arian Lasocki
- Department of Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, VIC, Australia
| | - Michael E Buckland
- Department of Neuropathology, Royal Prince Alfred Hospital, Camperdown, NSW, Australia.,School of Medical Sciences, University of Sydney, Camperdown, NSW, Australia
| | - Katharine J Drummond
- Department of Neurosurgery, The Royal Melbourne Hospital, Parkville, VIC, Australia.,Department of Surgery, The University of Melbourne, Parkville, VIC, Australia
| | - Heng Wei
- Department of Neuropathology, Royal Prince Alfred Hospital, Camperdown, NSW, Australia
| | - Jing Xie
- Centre for Biostatistics and Clinical Trials, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Michael Christie
- Department of Anatomical Pathology, The Royal Melbourne Hospital, Parkville, VIC, Australia
| | - Andrew Neal
- Department of Neurology, The Royal Melbourne Hospital, Parkville, VIC, Australia.,Department of Neuroscience, Central Clinical School, Monash University, Clayton, VIC, Australia
| | - Frank Gaillard
- Department of Radiology, The Royal Melbourne Hospital, Parkville, VIC, Australia.,Department of Radiology, The University of Melbourne, Parkville, VIC, Australia
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Yao J, Hagiwara A, Oughourlian TC, Wang C, Raymond C, Pope WB, Salamon N, Lai A, Ji M, Nghiemphu PL, Liau LM, Cloughesy TF, Ellingson BM. Diagnostic and Prognostic Value of pH- and Oxygen-Sensitive Magnetic Resonance Imaging in Glioma: A Retrospective Study. Cancers (Basel) 2022; 14:2520. [PMID: 35626127 PMCID: PMC9139712 DOI: 10.3390/cancers14102520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 05/11/2022] [Accepted: 05/17/2022] [Indexed: 01/19/2023] Open
Abstract
Characterization of hypoxia and tissue acidosis could advance the understanding of glioma biology and improve patient management. In this study, we evaluated the ability of a pH- and oxygen-sensitive magnetic resonance imaging (MRI) technique to differentiate glioma genotypes, including isocitrate dehydrogenase (IDH) mutation, 1p/19q co-deletion, and epidermal growth factor receptor (EGFR) amplification, and investigated its prognostic value. A total of 159 adult glioma patients were scanned with pH- and oxygen-sensitive MRI at 3T. We quantified the pH-sensitive measure of magnetization transfer ratio asymmetry (MTRasym) and oxygen-sensitive measure of R2’ within the tumor region-of-interest. IDH mutant gliomas showed significantly lower MTRasym × R2’ (p < 0.001), which differentiated IDH mutation status with sensitivity and specificity of 90.0% and 71.9%. Within IDH mutants, 1p/19q codeletion was associated with lower tumor acidity (p < 0.0001, sensitivity 76.9%, specificity 91.3%), while IDH wild-type, EGFR-amplified gliomas were more hypoxic (R2’ p = 0.024, sensitivity 66.7%, specificity 76.9%). Both R2’ and MTRasym × R2’ were significantly associated with patient overall survival (R2’: p = 0.045; MTRasym × R2’: p = 0.002) and progression-free survival (R2’: p = 0.010; MTRasym × R2’: p < 0.0001), independent of patient age, treatment status, and IDH status. The pH- and oxygen-sensitive MRI is a clinically feasible and potentially valuable imaging technique for distinguishing glioma subtypes and providing additional prognostic value to clinical practice.
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Affiliation(s)
- Jingwen Yao
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, CA 90024, USA; (J.Y.); (A.H.); (T.C.O.); (C.W.); (C.R.)
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA; (W.B.P.); (N.S.)
| | - Akifumi Hagiwara
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, CA 90024, USA; (J.Y.); (A.H.); (T.C.O.); (C.W.); (C.R.)
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA; (W.B.P.); (N.S.)
| | - Talia C. Oughourlian
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, CA 90024, USA; (J.Y.); (A.H.); (T.C.O.); (C.W.); (C.R.)
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA; (W.B.P.); (N.S.)
- Neuroscience Interdepartmental Program, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA
| | - Chencai Wang
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, CA 90024, USA; (J.Y.); (A.H.); (T.C.O.); (C.W.); (C.R.)
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA; (W.B.P.); (N.S.)
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, CA 90024, USA; (J.Y.); (A.H.); (T.C.O.); (C.W.); (C.R.)
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA; (W.B.P.); (N.S.)
| | - Whitney B. Pope
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA; (W.B.P.); (N.S.)
| | - Noriko Salamon
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA; (W.B.P.); (N.S.)
| | - Albert Lai
- UCLA Neuro-Oncology Program, University of California, Los Angeles, CA 90024, USA; (A.L.); (M.J.); (P.L.N.); (T.F.C.)
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA
| | - Matthew Ji
- UCLA Neuro-Oncology Program, University of California, Los Angeles, CA 90024, USA; (A.L.); (M.J.); (P.L.N.); (T.F.C.)
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA
| | - Phioanh L. Nghiemphu
- UCLA Neuro-Oncology Program, University of California, Los Angeles, CA 90024, USA; (A.L.); (M.J.); (P.L.N.); (T.F.C.)
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA
| | - Linda M. Liau
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA;
| | - Timothy F. Cloughesy
- UCLA Neuro-Oncology Program, University of California, Los Angeles, CA 90024, USA; (A.L.); (M.J.); (P.L.N.); (T.F.C.)
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA
| | - Benjamin M. Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, CA 90024, USA; (J.Y.); (A.H.); (T.C.O.); (C.W.); (C.R.)
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA; (W.B.P.); (N.S.)
- UCLA Neuro-Oncology Program, University of California, Los Angeles, CA 90024, USA; (A.L.); (M.J.); (P.L.N.); (T.F.C.)
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High Expression of CISD2 in Relation to Adverse Outcome and Abnormal Immune Cell Infiltration in Glioma. DISEASE MARKERS 2022; 2022:8133505. [PMID: 35493303 PMCID: PMC9050253 DOI: 10.1155/2022/8133505] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Revised: 03/09/2022] [Accepted: 03/17/2022] [Indexed: 12/02/2022]
Abstract
Glioma is a serious disease burden globally, with high mortality and recurrence rates. CDGSH iron sulfur domain 2 (CISD2) is an evolutionarily conserved protein that is involved in several cancers. However, its role in the prognosis and immune infiltration in glioma remains unclear. In our research, RNA-seq matrix and clinicopathological relevant data for CISD2 were downloaded from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. Human Protein Atlas was used to verify the CISD2 protein level in glioma, and STRING was used to establish relative coexpression gene network. The Kaplan-Meier plotter was adopted to analyze the effect of CISD2 on prognosis. The connection between CISD2 expression and immune infiltration was analyzed using single-sample GSEA (ssGSEA), TIMER, and GEPIA. In contrast to normal tissues, CISD2 expression was significantly higher in glioma tissues, and CISD2 presented a certain diagnostic value in distinguishing glioma tissues from normal tissues. Furthermore, the CISD2 level was correlated with age, histologic grade, histological type, isocitrate dehydrogenase (IDH) status, 1p/19q codeletion status, and primary therapy outcome of glioma, while high CISD2 mRNA expression was correlated with grave overall survival. Multivariate analysis demonstrated that CISD2 was an independent risk factor for patients with glioma. Functional enrichment analysis indicated that CISD2 could regulate proliferation, immune reaction, and mitochondrial function. The results from the ssGSEA and TIMER databases confirmed that CISD2 acts a prominent role in immune cell infiltration in the tumor microenvironment, especially in low-grade glioma (LGG). Furthermore, CISD2 expression was observably correlated to M2 polarization in macrophages with glioma progression. This is the first research to investigate the immune role of CISD2 in glioma. CISD2 may be an innovative prognostic biomarker and can act as a potential target for future therapy for glioma.
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Afridi M, Jain A, Aboian M, Payabvash S. Brain Tumor Imaging: Applications of Artificial Intelligence. Semin Ultrasound CT MR 2022; 43:153-169. [PMID: 35339256 PMCID: PMC8961005 DOI: 10.1053/j.sult.2022.02.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Artificial intelligence has become a popular field of research with goals of integrating it into the clinical decision-making process. A growing number of predictive models are being employed utilizing machine learning that includes quantitative, computer-extracted imaging features known as radiomic features, and deep learning systems. This is especially true in brain-tumor imaging where artificial intelligence has been proposed to characterize, differentiate, and prognostication. We reviewed current literature regarding the potential uses of machine learning-based, and deep learning-based artificial intelligence in neuro-oncology as it pertains to brain tumor molecular classification, differentiation, and treatment response. While there is promising evidence supporting the use of artificial intelligence in neuro-oncology, there are still more investigations needed on a larger, multicenter scale along with a streamlined and standardized image processing workflow prior to its introduction in routine clinical decision-making protocol.
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Affiliation(s)
- Muhammad Afridi
- School of Osteopathic Medicine, Rowan University, Stratford, NJ
| | - Abhi Jain
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT
| | - Mariam Aboian
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT
| | - Seyedmehdi Payabvash
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT.
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Cassinelli Petersen GI, Shatalov J, Verma T, Brim WR, Subramanian H, Brackett A, Bahar RC, Merkaj S, Zeevi T, Staib LH, Cui J, Omuro A, Bronen RA, Malhotra A, Aboian MS. Machine Learning in Differentiating Gliomas from Primary CNS Lymphomas: A Systematic Review, Reporting Quality, and Risk of Bias Assessment. AJNR Am J Neuroradiol 2022; 43:526-533. [PMID: 35361577 PMCID: PMC8993193 DOI: 10.3174/ajnr.a7473] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 01/31/2022] [Indexed: 12/12/2022]
Abstract
BACKGROUND Differentiating gliomas and primary CNS lymphoma represents a diagnostic challenge with important therapeutic ramifications. Biopsy is the preferred method of diagnosis, while MR imaging in conjunction with machine learning has shown promising results in differentiating these tumors. PURPOSE Our aim was to evaluate the quality of reporting and risk of bias, assess data bases with which the machine learning classification algorithms were developed, the algorithms themselves, and their performance. DATA SOURCES Ovid EMBASE, Ovid MEDLINE, Cochrane Central Register of Controlled Trials, and the Web of Science Core Collection were searched according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. STUDY SELECTION From 11,727 studies, 23 peer-reviewed studies used machine learning to differentiate primary CNS lymphoma from gliomas in 2276 patients. DATA ANALYSIS Characteristics of data sets and machine learning algorithms were extracted. A meta-analysis on a subset of studies was performed. Reporting quality and risk of bias were assessed using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) and Prediction Model Study Risk Of Bias Assessment Tool. DATA SYNTHESIS The highest area under the receiver operating characteristic curve (0.961) and accuracy (91.2%) in external validation were achieved by logistic regression and support vector machines models using conventional radiomic features. Meta-analysis of machine learning classifiers using these features yielded a mean area under the receiver operating characteristic curve of 0.944 (95% CI, 0.898-0.99). The median TRIPOD score was 51.7%. The risk of bias was high for 16 studies. LIMITATIONS Exclusion of abstracts decreased the sensitivity in evaluating all published studies. Meta-analysis had high heterogeneity. CONCLUSIONS Machine learning-based methods of differentiating primary CNS lymphoma from gliomas have shown great potential, but most studies lack large, balanced data sets and external validation. Assessment of the studies identified multiple deficiencies in reporting quality and risk of bias. These factors reduce the generalizability and reproducibility of the findings.
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Affiliation(s)
- G I Cassinelli Petersen
- From the Department of Radiology and Biomedical Imaging (G.I.C.P., T.V., H.S., R.C.B., S.M., T.Z., L.H.S., J.C., R.A.B., A.M., M.S.A.)
- Universitätsmedizin Göttingen (G.I.C.P.), Göttingen, Germany
| | - J Shatalov
- University of Richmond (J.S.), Richmond, Virginia
| | - T Verma
- From the Department of Radiology and Biomedical Imaging (G.I.C.P., T.V., H.S., R.C.B., S.M., T.Z., L.H.S., J.C., R.A.B., A.M., M.S.A.)
- New York University (T.V.), New York, New York
| | - W R Brim
- Whiting School of Engineering (W.R.B.), Johns Hopkins University, Baltimore, Maryland
| | - H Subramanian
- From the Department of Radiology and Biomedical Imaging (G.I.C.P., T.V., H.S., R.C.B., S.M., T.Z., L.H.S., J.C., R.A.B., A.M., M.S.A.)
| | | | - R C Bahar
- From the Department of Radiology and Biomedical Imaging (G.I.C.P., T.V., H.S., R.C.B., S.M., T.Z., L.H.S., J.C., R.A.B., A.M., M.S.A.)
| | - S Merkaj
- From the Department of Radiology and Biomedical Imaging (G.I.C.P., T.V., H.S., R.C.B., S.M., T.Z., L.H.S., J.C., R.A.B., A.M., M.S.A.)
| | - T Zeevi
- From the Department of Radiology and Biomedical Imaging (G.I.C.P., T.V., H.S., R.C.B., S.M., T.Z., L.H.S., J.C., R.A.B., A.M., M.S.A.)
| | - L H Staib
- From the Department of Radiology and Biomedical Imaging (G.I.C.P., T.V., H.S., R.C.B., S.M., T.Z., L.H.S., J.C., R.A.B., A.M., M.S.A.)
| | - J Cui
- From the Department of Radiology and Biomedical Imaging (G.I.C.P., T.V., H.S., R.C.B., S.M., T.Z., L.H.S., J.C., R.A.B., A.M., M.S.A.)
| | - A Omuro
- Department of Neurology (A.O.), Yale School of Medicine, New Haven, Connecticut
| | - R A Bronen
- From the Department of Radiology and Biomedical Imaging (G.I.C.P., T.V., H.S., R.C.B., S.M., T.Z., L.H.S., J.C., R.A.B., A.M., M.S.A.)
| | - A Malhotra
- From the Department of Radiology and Biomedical Imaging (G.I.C.P., T.V., H.S., R.C.B., S.M., T.Z., L.H.S., J.C., R.A.B., A.M., M.S.A.)
| | - M S Aboian
- From the Department of Radiology and Biomedical Imaging (G.I.C.P., T.V., H.S., R.C.B., S.M., T.Z., L.H.S., J.C., R.A.B., A.M., M.S.A.)
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Zhang X, Zhang Y, Zhang G, Qiu X, Tan W, Yin X, Liao L. Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential. Front Oncol 2022; 12:773840. [PMID: 35251962 PMCID: PMC8891653 DOI: 10.3389/fonc.2022.773840] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 01/17/2022] [Indexed: 12/12/2022] Open
Abstract
The high-throughput extraction of quantitative imaging features from medical images for the purpose of radiomic analysis, i.e., radiomics in a broad sense, is a rapidly developing and emerging research field that has been attracting increasing interest, particularly in multimodality and multi-omics studies. In this context, the quantitative analysis of multidimensional data plays an essential role in assessing the spatio-temporal characteristics of different tissues and organs and their microenvironment. Herein, recent developments in this method, including manually defined features, data acquisition and preprocessing, lesion segmentation, feature extraction, feature selection and dimension reduction, statistical analysis, and model construction, are reviewed. In addition, deep learning-based techniques for automatic segmentation and radiomic analysis are being analyzed to address limitations such as rigorous workflow, manual/semi-automatic lesion annotation, and inadequate feature criteria, and multicenter validation. Furthermore, a summary of the current state-of-the-art applications of this technology in disease diagnosis, treatment response, and prognosis prediction from the perspective of radiology images, multimodality images, histopathology images, and three-dimensional dose distribution data, particularly in oncology, is presented. The potential and value of radiomics in diagnostic and therapeutic strategies are also further analyzed, and for the first time, the advances and challenges associated with dosiomics in radiotherapy are summarized, highlighting the latest progress in radiomics. Finally, a robust framework for radiomic analysis is presented and challenges and recommendations for future development are discussed, including but not limited to the factors that affect model stability (medical big data and multitype data and expert knowledge in medical), limitations of data-driven processes (reproducibility and interpretability of studies, different treatment alternatives for various institutions, and prospective researches and clinical trials), and thoughts on future directions (the capability to achieve clinical applications and open platform for radiomics analysis).
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Affiliation(s)
- Xingping Zhang
- Institute of Advanced Cyberspace Technology, Guangzhou University, Guangzhou, China
- Department of New Networks, Peng Cheng Laboratory, Shenzhen, China
| | - Yanchun Zhang
- Institute of Advanced Cyberspace Technology, Guangzhou University, Guangzhou, China
- Department of New Networks, Peng Cheng Laboratory, Shenzhen, China
| | - Guijuan Zhang
- Department of Respiratory Medicine, First Affiliated Hospital of Gannan Medical University, Ganzhou, China
| | - Xingting Qiu
- Department of Radiology, First Affiliated Hospital of Gannan Medical University, Ganzhou, China
| | - Wenjun Tan
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Shenyang, China
| | - Xiaoxia Yin
- Institute of Advanced Cyberspace Technology, Guangzhou University, Guangzhou, China
| | - Liefa Liao
- School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, China
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38
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Foundations of Lesion Detection Using Machine Learning in Clinical Neuroimaging. ACTA NEUROCHIRURGICA. SUPPLEMENT 2021; 134:171-182. [PMID: 34862541 DOI: 10.1007/978-3-030-85292-4_21] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
This chapter describes technical considerations and current and future clinical applications of lesion detection using machine learning in the clinical setting. Lesion detection is central to neuroradiology and precedes all further processes which include but are not limited to lesion characterization, quantification, longitudinal disease assessment, prognosis, and prediction of treatment response. A number of machine learning algorithms focusing on lesion detection have been developed or are currently under development which may either support or extend the imaging process. Examples include machine learning applications in stroke, aneurysms, multiple sclerosis, neuro-oncology, neurodegeneration, and epilepsy.
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39
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Mellinghoff IK, Chang SM, Jaeckle KA, van den Bent M. Isocitrate Dehydrogenase Mutant Grade II and III Glial Neoplasms. Hematol Oncol Clin North Am 2021; 36:95-111. [PMID: 34711457 DOI: 10.1016/j.hoc.2021.08.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Mutations in isocitrate dehydrogenase (IDH) 1 or IDH2 occur in most of the adult low-grade gliomas and, less commonly, in cholangiocarcinoma, chondrosarcoma, acute myeloid leukemia, and other human malignancies. Cancer-associated mutations alter the function of the enzyme, resulting in production of R(-)-2-hydroxyglutarate and broad epigenetic dysregulation. Small molecule IDH inhibitors have received regulatory approval for the treatment of IDH mutant (mIDH) leukemia and are under development for the treatment of mIDH solid tumors. This article provides a current view of mIDH adult astrocytic and oligodendroglial tumors, including their clinical presentation and treatment, and discusses novel approaches and challenges toward improving the treatment of these tumors.
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Affiliation(s)
- Ingo K Mellinghoff
- Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - Susan M Chang
- Department of Neurological Surgery, University of California San Francisco, 505 Parnassus Room M 774SF, San Francisco, CA 94142-0112, USA
| | - Kurt A Jaeckle
- Department of Neurology and Oncology, Mayo Clinic Florida, Mangurian 4415, 4500 San Pablo Road, Jacksonville, FL 32224, USA
| | - Martin van den Bent
- Department of Neuro-onoclogy, Brain Tumor Center at Erasmus MC Cancer Institute, Nt-542, Dr Molenwaterplein 40, Rotterdam 3015 GD, The Netherlands.
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40
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Abstract
This article reviews recent advances in the use of standard and advanced imaging techniques for diagnosis and treatment of central nervous system (CNS) tumors, including glioma and brain metastasis. Following the recent transition from a histology-based approach in classifying CNS tumors to one that integrates histology with the molecular information of tumor, the approaches for imaging CNS tumors have also been adapted to this new framework. Some challenges related to the diagnosis and treatment of CNS tumors, such as differentiating tumor from treatment-related imaging changes, require further progress to implement advanced imaging for clinical use.
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Affiliation(s)
- Raymond Y Huang
- Department of Neuroradiology, Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
| | - Whitney B Pope
- Radiology, Section of Neuroradiology, Brain Tumor Imaging, UCLA Medical Center, Los Angeles, CA, USA; Department of Radiological Sciences, David Geffen School of Medicine, University of California-Los Angeles, 924 Westwood Boulevard, Suite 615, Los Angeles, CA 90024, USA; Department of Neurology, David Geffen School of Medicine, University of California-Los Angeles, 924 Westwood Boulevard, Suite 615, Los Angeles, CA 90024, USA
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Kinoshita M, Kanemura Y, Narita Y, Kishima H. Reverse Engineering Glioma Radiomics to Conventional Neuroimaging. Neurol Med Chir (Tokyo) 2021; 61:505-514. [PMID: 34373429 PMCID: PMC8443974 DOI: 10.2176/nmc.ra.2021-0133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
A novel radiological research field pursuing comprehensive quantitative image, namely “Radiomics,” gained traction along with the advancement of computational technology and artificial intelligence. This novel concept for analyzing medical images brought extensive interest to the neuro-oncology and neuroradiology research community to build a diagnostic workflow to detect clinically relevant genetic alteration of gliomas noninvasively. Although quite a few promising results were published regarding MRI-based diagnosis of isocitrate dehydrogenase (IDH) mutation in gliomas, it has become clear that an ample amount of effort is still needed to render this technology clinically applicable. At the same time, many significant insights were discovered through this research project, some of which could be “reverse engineered” to improve conventional non-radiomic MR image acquisition. In this review article, the authors aim to discuss the recent advancements and encountering issues of radiomics, how we can apply the knowledge provided by radiomics to standard clinical images, and further expected technological advances in the realm of radiomics and glioma.
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Affiliation(s)
- Manabu Kinoshita
- Department of Neurosurgery, Asahikawa Medical University.,Department of Neurosurgery, Osaka University Graduate School of Medicine.,Department of Neurosurgery, Osaka International Cancer Institute
| | - Yonehiro Kanemura
- Department of Biomedical Research and Innovation, Institute for Clinical Research, National Hospital Organization Osaka National Hospital
| | - Yoshitaka Narita
- Department of Neurosurgery and Neuro-Oncology, National Cancer Center Hospital
| | - Haruhiko Kishima
- Department of Neurosurgery, Osaka University Graduate School of Medicine
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Manikis GC, Ioannidis GS, Siakallis L, Nikiforaki K, Iv M, Vozlic D, Surlan-Popovic K, Wintermark M, Bisdas S, Marias K. Multicenter DSC-MRI-Based Radiomics Predict IDH Mutation in Gliomas. Cancers (Basel) 2021; 13:cancers13163965. [PMID: 34439118 PMCID: PMC8391559 DOI: 10.3390/cancers13163965] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 07/25/2021] [Accepted: 07/31/2021] [Indexed: 11/24/2022] Open
Abstract
Simple Summary Significant efforts have been put toward developing MRI-based radiogenomics for IDH status subtyping predictions; however, in the vast majority of these approaches, the external validation sets are absent. Another limitation in current studies is the lack of explainability in radiomics models, which hampers clinical trust and translation. Motivated by these challenges, we proposed a multicenter DSC–MRI-based radiomics study based on an independent exploratory set, which was externally validated on two independent cohorts, for IDH mutation status prediction. Our results demonstrated that DSC–MRI radiogenomics in gliomas, coupled with dynamic-based image standardization techniques, hold the potential to provide (a) increased predictive performance by offering models that generalize well, (b) reasoning behind the IDH mutation status predictions, and (c) interpretability of the radiomics features’ impacts in model performance. Abstract To address the current lack of dynamic susceptibility contrast magnetic resonance imaging (DSC–MRI)-based radiomics to predict isocitrate dehydrogenase (IDH) mutations in gliomas, we present a multicenter study that featured an independent exploratory set for radiomics model development and external validation using two independent cohorts. The maximum performance of the IDH mutation status prediction on the validation set had an accuracy of 0.544 (Cohen’s kappa: 0.145, F1-score: 0.415, area under the curve-AUC: 0.639, sensitivity: 0.733, specificity: 0.491), which significantly improved to an accuracy of 0.706 (Cohen’s kappa: 0.282, F1-score: 0.474, AUC: 0.667, sensitivity: 0.6, specificity: 0.736) when dynamic-based standardization of the images was performed prior to the radiomics. Model explainability using local interpretable model-agnostic explanations (LIME) and Shapley additive explanations (SHAP) revealed potential intuitive correlations between the IDH–wildtype increased heterogeneity and the texture complexity. These results strengthened our hypothesis that DSC–MRI radiogenomics in gliomas hold the potential to provide increased predictive performance from models that generalize well and provide understandable patterns between IDH mutation status and the extracted features toward enabling the clinical translation of radiogenomics in neuro-oncology.
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Affiliation(s)
- Georgios C. Manikis
- Computational BioMedicine Laboratory (CBML), Foundation for Research and Technology—Hellas (FORTH), 70013 Heraklion, Greece; (G.S.I.); (K.N.); (K.M.)
- Correspondence: ; Tel.: +30-281-139-1593
| | - Georgios S. Ioannidis
- Computational BioMedicine Laboratory (CBML), Foundation for Research and Technology—Hellas (FORTH), 70013 Heraklion, Greece; (G.S.I.); (K.N.); (K.M.)
| | - Loizos Siakallis
- Department of Brain Repair and Rehabilitation, Queen Square Institute of Neurology, UCL, London WC1N 3BG, UK; (L.S.); (S.B.)
| | - Katerina Nikiforaki
- Computational BioMedicine Laboratory (CBML), Foundation for Research and Technology—Hellas (FORTH), 70013 Heraklion, Greece; (G.S.I.); (K.N.); (K.M.)
| | - Michael Iv
- Division of Neuroimaging and Neurointervention, Department of Radiology, Stanford University, Stanford, CA 94305, USA; (M.I.); (M.W.)
| | - Diana Vozlic
- Department of Radiology, Faculty of Medicine, University of Ljubljana, 1000 Ljubljana, Slovenia; (D.V.); (K.S.-P.)
- Department of Neuroradiology, University Medical Centre, 1000 Ljubljana, Slovenia
| | - Katarina Surlan-Popovic
- Department of Radiology, Faculty of Medicine, University of Ljubljana, 1000 Ljubljana, Slovenia; (D.V.); (K.S.-P.)
- Department of Neuroradiology, University Medical Centre, 1000 Ljubljana, Slovenia
| | - Max Wintermark
- Division of Neuroimaging and Neurointervention, Department of Radiology, Stanford University, Stanford, CA 94305, USA; (M.I.); (M.W.)
| | - Sotirios Bisdas
- Department of Brain Repair and Rehabilitation, Queen Square Institute of Neurology, UCL, London WC1N 3BG, UK; (L.S.); (S.B.)
- Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, University College London NHS Foundation Trust, London WC1N 3BG, UK
| | - Kostas Marias
- Computational BioMedicine Laboratory (CBML), Foundation for Research and Technology—Hellas (FORTH), 70013 Heraklion, Greece; (G.S.I.); (K.N.); (K.M.)
- Department of Electrical & Computer Engineering, Hellenic Mediterranean University, 71410 Heraklion, Greece
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Abstract
The central role of MRI in neuro-oncology is undisputed. The technique is used, both in clinical practice and in clinical trials, to diagnose and monitor disease activity, support treatment decision-making, guide the use of focused treatments and determine response to treatment. Despite recent substantial advances in imaging technology and image analysis techniques, clinical MRI is still primarily used for the qualitative subjective interpretation of macrostructural features, as opposed to quantitative analyses that take into consideration multiple pathophysiological features. However, the field of quantitative imaging and imaging biomarker development is maturing. The European Imaging Biomarkers Alliance (EIBALL) and Quantitative Imaging Biomarkers Alliance (QIBA) are setting standards for biomarker development, validation and implementation, as well as promoting the use of quantitative imaging and imaging biomarkers by demonstrating their clinical value. In parallel, advanced imaging techniques are reaching the clinical arena, providing quantitative, commonly physiological imaging parameters that are driving the discovery, validation and implementation of quantitative imaging and imaging biomarkers in the clinical routine. Additionally, computational analysis techniques are increasingly being used in the research setting to convert medical images into objective high-dimensional data and define radiomic signatures of disease states. Here, I review the definition and current state of MRI biomarkers in neuro-oncology, and discuss the clinical potential of quantitative image analysis techniques.
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van Kempen EJ, Post M, Mannil M, Kusters B, ter Laan M, Meijer FJA, Henssen DJHA. Accuracy of Machine Learning Algorithms for the Classification of Molecular Features of Gliomas on MRI: A Systematic Literature Review and Meta-Analysis. Cancers (Basel) 2021; 13:cancers13112606. [PMID: 34073309 PMCID: PMC8198025 DOI: 10.3390/cancers13112606] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 05/17/2021] [Accepted: 05/18/2021] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Glioma prognosis and treatment are based on histopathological characteristics and molecular profile. Following the World Health Organization (WHO) guidelines (2016), the most important molecular diagnostic markers include IDH1/2-genotype and 1p/19q codeletion status, although more recent publications also include ARTX genotype and TERT- and MGMT promoter methylation. Machine learning algorithms (MLAs), however, were described to successfully determine these molecular characteristics non-invasively by using magnetic resonance imaging (MRI) data. The aim of this review and meta-analysis was to define the diagnostic accuracy of MLAs with regard to these different molecular markers. We found high accuracies of MLAs to predict each individual molecular marker, with IDH1/2-genotype being the most investigated and the most accurate. Radiogenomics could therefore be a promising tool for discriminating genetically determined gliomas in a non-invasive fashion. Although encouraging results are presented here, large-scale, prospective trials with external validation groups are warranted. Abstract Treatment planning and prognosis in glioma treatment are based on the classification into low- and high-grade oligodendroglioma or astrocytoma, which is mainly based on molecular characteristics (IDH1/2- and 1p/19q codeletion status). It would be of great value if this classification could be made reliably before surgery, without biopsy. Machine learning algorithms (MLAs) could play a role in achieving this by enabling glioma characterization on magnetic resonance imaging (MRI) data without invasive tissue sampling. The aim of this study is to provide a performance evaluation and meta-analysis of various MLAs for glioma characterization. Systematic literature search and meta-analysis were performed on the aggregated data, after which subgroup analyses for several target conditions were conducted. This study is registered with PROSPERO, CRD42020191033. We identified 724 studies; 60 and 17 studies were eligible to be included in the systematic review and meta-analysis, respectively. Meta-analysis showed excellent accuracy for all subgroups, with the classification of 1p/19q codeletion status scoring significantly poorer than other subgroups (AUC: 0.748, p = 0.132). There was considerable heterogeneity among some of the included studies. Although promising results were found with regard to the ability of MLA-tools to be used for the non-invasive classification of gliomas, large-scale, prospective trials with external validation are warranted in the future.
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Affiliation(s)
- Evi J. van Kempen
- Department of Medical Imaging, Radboud University Medical Center, Radboud University, 6500HB Nijmegen, The Netherlands; (E.J.v.K.); (M.P.); (F.J.A.M.)
| | - Max Post
- Department of Medical Imaging, Radboud University Medical Center, Radboud University, 6500HB Nijmegen, The Netherlands; (E.J.v.K.); (M.P.); (F.J.A.M.)
| | - Manoj Mannil
- Clinic of Radiology, University Hospital Münster, WWU University of Münster, 48149 Münster, Germany;
| | - Benno Kusters
- Department of Pathology, Radboud University Medical Center, Radboud University, 6500HB Nijmegen, The Netherlands;
| | - Mark ter Laan
- Department of Neurosurgery, Radboud University Medical Center, Radboud University, 6500HB Nijmegen, The Netherlands;
| | - Frederick J. A. Meijer
- Department of Medical Imaging, Radboud University Medical Center, Radboud University, 6500HB Nijmegen, The Netherlands; (E.J.v.K.); (M.P.); (F.J.A.M.)
| | - Dylan J. H. A. Henssen
- Department of Medical Imaging, Radboud University Medical Center, Radboud University, 6500HB Nijmegen, The Netherlands; (E.J.v.K.); (M.P.); (F.J.A.M.)
- Correspondence:
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Casale R, Lavrova E, Sanduleanu S, Woodruff HC, Lambin P. Development and external validation of a non-invasive molecular status predictor of chromosome 1p/19q co-deletion based on MRI radiomics analysis of Low Grade Glioma patients. Eur J Radiol 2021; 139:109678. [PMID: 33848780 DOI: 10.1016/j.ejrad.2021.109678] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Revised: 03/04/2021] [Accepted: 03/21/2021] [Indexed: 12/18/2022]
Abstract
PURPOSE The 1p/19q co-deletion status has been demonstrated to be a prognostic biomarker in lower grade glioma (LGG). The objective of this study was to build a magnetic resonance (MRI)-derived radiomics model to predict the 1p/19q co-deletion status. METHOD 209 pathology-confirmed LGG patients from 2 different datasets from The Cancer Imaging Archive were retrospectively reviewed; one dataset with 159 patients as the training and discovery dataset and the other one with 50 patients as validation dataset. Radiomics features were extracted from T2- and T1-weighted post-contrast MRI resampled data using linear and cubic interpolation methods. For each of the voxel resampling methods a three-step approach was used for feature selection and a random forest (RF) classifier was trained on the training dataset. Model performance was evaluated on training and validation datasets and clinical utility indexes (CUIs) were computed. The distributions and intercorrelation for selected features were analyzed. RESULTS Seven radiomics features were selected from the cubic interpolated features and five from the linear interpolated features on the training dataset. The RF classifier showed similar performance for cubic and linear interpolation methods in the training dataset with accuracies of 0.81 (0.75-0.86) and 0.76 (0.71-0.82) respectively; in the validation dataset the accuracy dropped to 0.72 (0.6-0.82) using cubic interpolation and 0.72 (0.6-0.84) using linear resampling. CUIs showed the model achieved satisfactory negative values (0.605 using cubic interpolation and 0.569 for linear interpolation). CONCLUSIONS MRI has the potential for predicting the 1p/19q status in LGGs. Both cubic and linear interpolation methods showed similar performance in external validation.
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Affiliation(s)
- Roberto Casale
- The D-Lab, Department of Precision Medicine, GROW- School for Oncology, Maastricht University Maastricht, the Netherlands.
| | - Elizaveta Lavrova
- The D-Lab, Department of Precision Medicine, GROW- School for Oncology, Maastricht University Maastricht, the Netherlands
| | - Sebastian Sanduleanu
- The D-Lab, Department of Precision Medicine, GROW- School for Oncology, Maastricht University Maastricht, the Netherlands
| | - Henry C Woodruff
- The D-Lab, Department of Precision Medicine, GROW- School for Oncology, Maastricht University Maastricht, the Netherlands; Department of Radiology and Nuclear Medicine, GROW - School for Oncology, Maastricht University Medical Centre+, Maastricht, the Netherlands
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW- School for Oncology, Maastricht University Maastricht, the Netherlands; Department of Radiology and Nuclear Medicine, GROW - School for Oncology, Maastricht University Medical Centre+, Maastricht, the Netherlands
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The role of 2-hydroxyglutarate magnetic resonance spectroscopy for the determination of isocitrate dehydrogenase status in lower grade gliomas versus glioblastoma: a systematic review and meta-analysis of diagnostic test accuracy. Neuroradiology 2021; 63:1823-1830. [PMID: 33811494 DOI: 10.1007/s00234-021-02702-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Accepted: 03/28/2021] [Indexed: 10/21/2022]
Abstract
PURPOSE Magnetic resonance spectroscopy (MRS) provides a non-invasive means of determining isocitrate dehydrogenase (IDH) status. Determination of 2-hydroxyglutarate (2-HG) presence through MRS is a means of determining IDH status; however, differences may be seen by grade. The goal of this paper is to perform a diagnostic test accuracy (DTA) meta-analysis on 2-HG MRS for IDH status in both lower-grade glioma (LGG) and glioblastoma (GBM) in preoperative patients. METHODS A systematic review and meta-analysis were performed in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Diagnostic Test Accuracy guidelines. Quality assessment was performed using the Quality Assessment of Diagnostic Accuracy Studies 2. The search was up to date as of 09/02/2021. Nine English-language journal articles were included. RESULTS The meta-analysis found a pooled sensitivity of 93% (95% CI 58-99%) and specificity of 84% (95% CI 51-96%) for LGG (n= 181). For GBM (n= 77), the pooled sensitivity was 84% (95% CI 25.0-99%) and the specificity was 97% (95% CI 43-100%). CONCLUSION 2-HG MRS shows promise as a non-invasive means of determining IDH status, with specificity higher for GBM and sensitivity higher for LGG. The wide confidence intervals are notable, however, in particular related to the small number of IDH-mutant GBM studied. Diagnostic heterogeneity was particularly present for LGG, and the hierarchical summary receiver operator curves showed poor predictive accuracy in both groups. For more conclusive results, diagnostic test accuracy statistics need to be quantified with larger studies and more deliberate study design.
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Kandemirli SG, Kocak B, Naganawa S, Ozturk K, Yip SSF, Chopra S, Rivetti L, Aldine AS, Jones K, Cayci Z, Moritani T, Sato TS. Machine Learning-Based Multiparametric Magnetic Resonance Imaging Radiomics for Prediction of H3K27M Mutation in Midline Gliomas. World Neurosurg 2021; 151:e78-e85. [PMID: 33819703 DOI: 10.1016/j.wneu.2021.03.135] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 03/24/2021] [Accepted: 03/25/2021] [Indexed: 12/19/2022]
Abstract
OBJECTIVE H3K27M mutation in gliomas has prognostic implications. Previous magnetic resonance imaging (MRI) studies have reported variable rates of tumoral enhancement, necrotic changes, and peritumoral edema in H3K27M-mutant gliomas, with no distinguishing imaging features compared with wild-type gliomas. We aimed to construct an MRI machine learning (ML)-based radiomic model to predict H3K27M mutation in midline gliomas. METHODS A total of 109 patients from 3 academic centers were included in this study. Fifty patients had H3K27M mutation and 59 were wild-type. Conventional MRI sequences (T1-weighted, T2-weighted, T2-fluid-attenuated inversion recovery, postcontrast T1-weighted, and apparent diffusion coefficient maps) were used for feature extraction. A total of 651 radiomic features per each sequence were extracted. Patients were randomly selected with a 7:3 ratio to create training (n = 76) and test (n = 33) data sets. An extreme gradient boosting algorithm (XGBoost) was used in ML-based model development. Performance of the model was assessed by area under the receiver operating characteristic curve. RESULTS Pediatric patients accounted for a larger proportion of the study cohort (60 pediatric [55%] vs. 49 adult [45%] patients). XGBoost with additional feature selection had an area under the receiver operating characteristic curve of 0.791 and 0.737 in the training and test data sets, respectively. The model achieved accuracy, precision (positive predictive value), recall (sensitivity), and F1 (harmonic mean of precision and recall) measures of 72.7%, 76.5%, 72.2%, and 74.3%, respectively, in the test set. CONCLUSIONS Our multi-institutional study suggests that ML-based radiomic analysis of multiparametric MRI can be a promising noninvasive technique to predict H3K27M mutation status in midline gliomas.
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Affiliation(s)
| | - Burak Kocak
- Department of Radiology, Başakşehir Çam and Sakura City Hospital, Istanbul, Turkey
| | - Shotaro Naganawa
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Kerem Ozturk
- Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Stephen S F Yip
- Department of Medical Physics, University of Wisconsin, Madison, Wisconsin, USA; AIQ Solutions, Madison, Wisconsin, USA
| | - Saurav Chopra
- Department of Pathology, University of Iowa Hospital and Clinics, Iowa City, Iowa, USA
| | | | - Amro Saad Aldine
- Department of Radiology, Louisiana State University Health Sciences Center, Louisiana, Missouri, USA
| | - Karra Jones
- Department of Pathology, University of Iowa Hospital and Clinics, Iowa City, Iowa, USA
| | - Zuzan Cayci
- Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Toshio Moritani
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Takashi Shawn Sato
- Department of Radiology, University of Iowa Hospital and Clinics, Iowa City, Iowa, USA
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MRI-Based Radiomics and Radiogenomics in the Management of Low-Grade Gliomas: Evaluating the Evidence for a Paradigm Shift. J Clin Med 2021; 10:jcm10071411. [PMID: 33915813 PMCID: PMC8036428 DOI: 10.3390/jcm10071411] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 03/23/2021] [Accepted: 03/25/2021] [Indexed: 12/29/2022] Open
Abstract
Low-grade gliomas (LGGs) are tumors that affect mostly adults. These neoplasms are comprised mainly of oligodendrogliomas and diffuse astrocytomas. LGGs remain vexing to current management and therapeutic modalities although they exhibit more favorable survival rates compared with high-grade gliomas (HGGs). The specific genetic subtypes that these tumors exhibit result in variable clinical courses and the need to involve multidisciplinary teams of neurologists, epileptologists, neurooncologists and neurosurgeons. Currently, the diagnosis of an LGG pivots mainly around the preliminary radiological findings and the subsequent definitive surgical diagnosis (via surgical sampling). The introduction of radiomics as a high throughput quantitative imaging technique that allows for improved diagnostic, prognostic and predictive indices has created more interest for such techniques in cancer research and especially in neurooncology (MRI-based classification of LGGs, predicting Isocitrate dehydrogenase (IDH) and Telomerase reverse transcriptase (TERT) promoter mutations and predicting LGG associated seizures). Radiogenomics refers to the linkage of imaging findings with the tumor/tissue genomics. Numerous applications of radiomics and radiogenomics have been described in the clinical context and management of LGGs. In this review, we describe the recently published studies discussing the potential application of radiomics and radiogenomics in LGGs. We also highlight the potential pitfalls of the above-mentioned high throughput computerized techniques and, most excitingly, explore the use of machine learning artificial intelligence technologies as standalone and adjunct imaging tools en route to enhance a personalized MRI-based tumor diagnosis and management plan design.
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