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Pons-Escoda A, Garcia-Ruiz A, Naval-Baudin P, Martinez-Zalacain I, Castell J, Camins A, Vidal N, Bruna J, Cos M, Perez-Lopez R, Oleaga L, Warnert E, Smits M, Majos C. Differentiating IDH-mutant astrocytomas and 1p19q-codeleted oligodendrogliomas using DSC-PWI: high performance through cerebral blood volume and percentage of signal recovery percentiles. Eur Radiol 2024; 34:5320-5330. [PMID: 38282078 PMCID: PMC11255054 DOI: 10.1007/s00330-024-10611-z] [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: 10/31/2023] [Revised: 12/13/2023] [Accepted: 01/01/2024] [Indexed: 01/30/2024]
Abstract
OBJECTIVE Presurgical differentiation between astrocytomas and oligodendrogliomas remains an unresolved challenge in neuro-oncology. This research aims to provide a comprehensive understanding of each tumor's DSC-PWI signatures, evaluate the discriminative capacity of cerebral blood volume (CBV) and percentage of signal recovery (PSR) percentile values, and explore the synergy of CBV and PSR combination for pre-surgical differentiation. METHODS Patients diagnosed with grade 2 and 3 IDH-mutant astrocytomas and IDH-mutant 1p19q-codeleted oligodendrogliomas were retrospectively retrieved (2010-2022). 3D segmentations of each tumor were conducted, and voxel-level CBV and PSR were extracted to compute mean, minimum, maximum, and percentile values. Statistical comparisons were performed using the Mann-Whitney U test and the area under the receiver operating characteristic curve (AUC-ROC). Lastly, the five most discriminative variables were combined for classification with internal cross-validation. RESULTS The study enrolled 52 patients (mean age 45-year-old, 28 men): 28 astrocytomas and 24 oligodendrogliomas. Oligodendrogliomas exhibited higher CBV and lower PSR than astrocytomas across all metrics (e.g., mean CBV = 2.05 and 1.55, PSR = 0.68 and 0.81 respectively). The highest AUC-ROCs and the smallest p values originated from CBV and PSR percentiles (e.g., PSRp70 AUC-ROC = 0.84 and p value = 0.0005, CBVp75 AUC-ROC = 0.8 and p value = 0.0006). The mean, minimum, and maximum values yielded lower results. Combining the best five variables (PSRp65, CBVp70, PSRp60, CBVp75, and PSRp40) achieved a mean AUC-ROC of 0.87 for differentiation. CONCLUSIONS Oligodendrogliomas exhibit higher CBV and lower PSR than astrocytomas, traits that are emphasized when considering percentiles rather than mean or extreme values. The combination of CBV and PSR percentiles results in promising classification outcomes. CLINICAL RELEVANCE STATEMENT The combination of histogram-derived percentile values of cerebral blood volume and percentage of signal recovery from DSC-PWI enhances the presurgical differentiation between astrocytomas and oligodendrogliomas, suggesting that incorporating these metrics into clinical practice could be beneficial. KEY POINTS • The unsupervised selection of percentile values for cerebral blood volume and percentage of signal recovery enhances presurgical differentiation of astrocytomas and oligodendrogliomas. • Oligodendrogliomas exhibit higher cerebral blood volume and lower percentage of signal recovery than astrocytomas. • Cerebral blood volume and percentage of signal recovery combined provide a broader perspective on tumor vasculature and yield promising results for this preoperative classification.
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Affiliation(s)
- Albert Pons-Escoda
- Radiology Department, Feixa Llarga SN, Hospital Universitari de Bellvitge, 08907, Barcelona, Spain.
- Neuro-oncology Unit, Feixa Llarga SN, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, 08907, Barcelona, Spain.
- Facultat de Medicina i Ciències de La Salut, Universitat de Barcelona (UB), Carrer de Casanova 143, 08036, Barcelona, Spain.
- Diagnostic Imaging and Nuclear Medicine Research Group, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, Feixa Llarga SN, 08907, Barcelona, Spain.
| | - Alonso Garcia-Ruiz
- Radiomics Group, Vall d'Hebron Institut d'Oncologia- VHIO, Carrer de Natzaret, 115-117, 08035, Barcelona, Spain
| | - Pablo Naval-Baudin
- Radiology Department, Feixa Llarga SN, Hospital Universitari de Bellvitge, 08907, Barcelona, Spain
- Diagnostic Imaging and Nuclear Medicine Research Group, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, Feixa Llarga SN, 08907, Barcelona, Spain
| | - Ignacio Martinez-Zalacain
- Radiology Department, Feixa Llarga SN, Hospital Universitari de Bellvitge, 08907, Barcelona, Spain
- Diagnostic Imaging and Nuclear Medicine Research Group, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, Feixa Llarga SN, 08907, Barcelona, Spain
| | - Josep Castell
- Radiology Department, Feixa Llarga SN, Hospital Universitari de Bellvitge, 08907, Barcelona, Spain
| | - Angels Camins
- Radiology Department, Feixa Llarga SN, Hospital Universitari de Bellvitge, 08907, Barcelona, Spain
| | - Noemi Vidal
- Neuro-oncology Unit, Feixa Llarga SN, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, 08907, Barcelona, Spain
- Pathology Department, Feixa Llarga SN, Hospital Universitari de Bellvitge, 08907, Barcelona, Spain
| | - Jordi Bruna
- Neuro-oncology Unit, Feixa Llarga SN, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, 08907, Barcelona, Spain
| | - Monica Cos
- Radiology Department, Feixa Llarga SN, Hospital Universitari de Bellvitge, 08907, Barcelona, Spain
| | - Raquel Perez-Lopez
- Radiomics Group, Vall d'Hebron Institut d'Oncologia- VHIO, Carrer de Natzaret, 115-117, 08035, Barcelona, Spain
| | - Laura Oleaga
- Radiology Department, Hospital Clinic de Barcelona, Villarroel 170, 08036, Barcelona, Spain
| | - Esther Warnert
- Department of Radiology & Nuclear Medicine, Erasmus MC, Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
- Erasmus MC Cancer Institute, Erasmus MC, Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - Marion Smits
- Department of Radiology & Nuclear Medicine, Erasmus MC, Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
- Erasmus MC Cancer Institute, Erasmus MC, Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
- Medical Delta, Delft, The Netherlands
| | - Carles Majos
- Radiology Department, Feixa Llarga SN, Hospital Universitari de Bellvitge, 08907, Barcelona, Spain
- Neuro-oncology Unit, Feixa Llarga SN, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, 08907, Barcelona, Spain
- Diagnostic Imaging and Nuclear Medicine Research Group, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, Feixa Llarga SN, 08907, Barcelona, Spain
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Patel SH, Lopes MB, Williams ES. A Note of Caution Regarding Single-Arm 1p or 19q Deletion in IDH-Mutant Gliomas. AJNR Am J Neuroradiol 2024; 45:E22. [PMID: 38844371 PMCID: PMC11286021 DOI: 10.3174/ajnr.a8201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Affiliation(s)
- Sohil H Patel
- Department of Radiology and Medical ImagingUniversity of Virginia Health, Charlottesville, Virginia
| | - M Beatriz Lopes
- Department of Department of Pathology (Neuropathology)University of Virginia Health, Charlottesville, Virginia
| | - Eli S Williams
- Department of Department of Pathology (Genomics and Cytogenomics)University of Virginia Health, Charlottesville, Virginia
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Lasocki A, Buckland ME, Gaillard F. Reply. AJNR Am J Neuroradiol 2024; 45:E23. [PMID: 38844369 DOI: 10.3174/ajnr.a8347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Affiliation(s)
- Arian Lasocki
- Department of Cancer ImagingPeter MacCallum Cancer CentreMelbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology
- Department of RadiologyThe University of MelbourneParkville, Victoria, Australia
| | - Michael E Buckland
- Department of NeuropathologyRoyal Prince Alfred HospitalCamperdown, New South Wales, Australia
- School of Medical SciencesUniversity of SydneyCamperdown, New South Wales, Australia
| | - Frank Gaillard
- Department of RadiologyThe University of MelbourneParkville, Victoria, Australia
- Department of RadiologyThe Royal Melbourne HospitalParkville, Victoria, Australia
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Ikeda S, Sakata A, Arakawa Y, Mineharu Y, Makino Y, Takeuchi Y, Fushimi Y, Okuchi S, Nakajima S, Otani S, Nakamoto Y. Clinical and imaging characteristics of supratentorial glioma with IDH2 mutation. Neuroradiology 2024; 66:973-981. [PMID: 38653782 DOI: 10.1007/s00234-024-03361-8] [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: 02/19/2024] [Accepted: 04/13/2024] [Indexed: 04/25/2024]
Abstract
PURPOSE The rarity of IDH2 mutations in supratentorial gliomas has led to gaps in understanding their radiological characteristics, potentially resulting in misdiagnosis based solely on negative IDH1 immunohistochemical staining. We aimed to investigate the clinical and imaging characteristics of IDH2-mutant gliomas. METHODS We analyzed imaging data from adult patients with pathologically confirmed diffuse lower-grade gliomas and known IDH1/2 alteration and 1p/19q codeletion statuses obtained from the records of our institute (January 2011 to August 2022, Cohort 1) and The Cancer Imaging Archive (TCIA, Cohort 2). Two radiologists evaluated clinical information and radiological findings using standardized methods. Furthermore, we compared the data for IDH2-mutant and IDH-wildtype gliomas. Multivariate logistic regression was used to identify the predictors of IDH2 mutation status, and receiver operating characteristic curve analysis was employed to assess the predictive performance of the model. RESULTS Of the 20 IDH2-mutant supratentorial gliomas, 95% were in the frontal lobes, with 75% classified as oligodendrogliomas. Age and the T2-FLAIR discordance were independent predictors of IDH2 mutations. Receiver operating characteristic curve analysis for the model using age and T2-FLAIR discordance demonstrated a strong potential for discriminating between IDH2-mutant and IDH-wildtype gliomas, with an area under the curve of 0.96 (95% CI, 0.91-0.98, P = .02). CONCLUSION A high frequency of oligodendrogliomas with 1p/19q codeletion was observed in IDH2-mutated gliomas. Younger age and the presence of the T2-FLAIR discordance were associated with IDH2 mutations and these findings may help with precise diagnoses and treatment decisions in clinical practice.
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Affiliation(s)
- Satoshi Ikeda
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Akihiko Sakata
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan.
| | - Yoshiki Arakawa
- Department of Neurosurgery, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Yohei Mineharu
- Department of Neurosurgery, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Yasuhide Makino
- Department of Neurosurgery, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Yasuhide Takeuchi
- Department of Diagnostic Pathology, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Sachi Okuchi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Satoshi Nakajima
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Sayo Otani
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Yuji Nakamoto
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan
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Pons-Escoda A, Majos C, Smits M, Oleaga L. Presurgical diagnosis of diffuse gliomas in adults: Post-WHO 2021 practical perspectives from radiologists in neuro-oncology units. RADIOLOGIA 2024; 66:260-277. [PMID: 38908887 DOI: 10.1016/j.rxeng.2024.03.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 10/31/2023] [Indexed: 06/24/2024]
Abstract
The 2021 World Health Organization classification of CNS tumours was greeted with enthusiasm as well as an initial potential overwhelm. However, with time and experience, our understanding of its key aspects has notably improved. Using our collective expertise gained in neuro-oncology units in hospitals in different countries, we have compiled a practical guide for radiologists that clarifies the classification criteria for diffuse gliomas in adults. Its format is clear and concise to facilitate its incorporation into everyday clinical practice. The document includes a historical overview of the classifications and highlights the most important recent additions. It describes the main types in detail with an emphasis on their appearance on imaging. The authors also address the most debated issues in recent years. It will better prepare radiologists to conduct accurate presurgical diagnoses and collaborate effectively in clinical decision making, thus impacting decisions on treatment, prognosis, and overall patient care.
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Affiliation(s)
- A Pons-Escoda
- Radiology Department, Hospital Universitari de Bellvitge, Barcelona, Spain; Facultat de Medicina i Ciencies de La Salut, Universitat de Barcelona (UB), Barcelona, Spain.
| | - C Majos
- Radiology Department, Hospital Universitari de Bellvitge, Barcelona, Spain; Neuro-Oncology Unit, Institut d'Investigació Biomèdica de Bellvitge-IDIBELL, Barcelona, Spain; Diagnostic Imaging and Nuclear Medicine Research Group, Institut d'Investigació Biomèdica de Bellvitge-IDIBELL, Barcelona, Spain
| | - M Smits
- Department of Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands; Erasmus MC Cancer Institute, Erasmus MC, Rotterdam, The Netherlands; Medical Delta, Delft, The Netherlands
| | - L Oleaga
- Radiology Department, Hospital Clínic Barcelona, Barcelona, Spain
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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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Park YW, Kim S, Han K, Ahn SS, Moon JH, Kim EH, Kim J, Kang SG, Kim SH, Lee SK, Chang JH. Rethinking extent of resection of contrast-enhancing and non-enhancing tumor: different survival impacts on adult-type diffuse gliomas in 2021 World Health Organization classification. Eur Radiol 2024; 34:1376-1387. [PMID: 37608093 DOI: 10.1007/s00330-023-10125-0] [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: 04/12/2023] [Revised: 06/22/2023] [Accepted: 07/01/2023] [Indexed: 08/24/2023]
Abstract
OBJECTIVES Extent of resection (EOR) of contrast-enhancing (CE) and non-enhancing (NE) tumors may have different impacts on survival according to types of adult-type diffuse gliomas in the molecular era. This study aimed to evaluate the impact of EOR of CE and NE tumors in glioma according to the 2021 World Health Organization classification. METHODS This retrospective study included 1193 adult-type diffuse glioma patients diagnosed between 2001 and 2021 (183 oligodendroglioma, 211 isocitrate dehydrogenase [IDH]-mutant astrocytoma, and 799 IDH-wildtype glioblastoma patients) from a single institution. Patients had complete information on IDH mutation, 1p/19q codeletion, and O6-methylguanine-methyltransferase (MGMT) status. Cox survival analyses were performed within each glioma type to assess predictors of overall survival, including clinical, imaging data, histological grade, MGMT status, adjuvant treatment, and EOR of CE and NE tumors. Subgroup analyses were performed in patients with CE tumor. RESULTS Among 1193 patients, 935 (78.4%) patients had CE tumors. In entire oligodendrogliomas, gross total resection (GTR) of NE tumor was not associated with survival (HR = 0.56, p = 0.223). In 86 (47.0%) oligodendroglioma patients with CE tumor, GTR of CE tumor was the only independent predictor of survival (HR = 0.16, p = 0.004) in multivariable analysis. GTR of CE and NE tumors was independently associated with better survival in IDH-mutant astrocytoma and IDH-wildtype glioblastoma (all ps < 0.05). CONCLUSIONS GTR of both CE and NE tumors may significantly improve survival within IDH-mutant astrocytomas and IDH-wildtype glioblastomas. In oligodendrogliomas, the EOR of CE tumor may be crucial in survival; aggressive GTR of NE tumor may be unnecessary, whereas GTR of the CE tumor is recommended. CLINICAL RELEVANCE STATEMENT Surgical strategies on contrast-enhancing (CE) and non-enhancing (NE) tumors should be reassessed considering the different survival outcomes after gross total resection depending on CE and NE tumors in the 2021 World Health Organization classification of adult-type diffuse gliomas. KEY POINTS The survival impact of extent of resection of contrast-enhancing (CE) and non-enhancing (NE) tumors was evaluated in adult-type diffuse gliomas. Gross total resection of both CE and NE tumors may improve survival in isocitrate dehydrogenase (IDH)-mutant astrocytomas and IDH-wildtype glioblastomas, while only gross total resection of the CE tumor improves survival in oligodendrogliomas. Surgical strategies should be reconsidered according to types in adult-type diffuse gliomas.
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Affiliation(s)
- Yae Won Park
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea
| | - Sooyon Kim
- Department of Statistics and Data Science, Yonsei University, Seoul, Korea
| | - Kyunghwa Han
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea.
| | - Ju Hyung Moon
- Department of Neurosurgery, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea
| | - Eui Hyun Kim
- Department of Neurosurgery, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea
| | - Jinna Kim
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea
| | - Seok-Gu Kang
- Department of Neurosurgery, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea
| | - Se Hoon Kim
- Department of Pathology, Yonsei University College of Medicine, Seoul, Korea
| | - Seung-Koo Lee
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea
| | - Jong Hee Chang
- Department of Neurosurgery, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea.
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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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Li J, Zhang P, Qu L, Sun T, Duan Y, Wu M, Weng J, Li Z, Gong X, Liu X, Wang Y, Jia W, Su X, Yue Q, Li J, Zhang Z, Barkhof F, Huang RY, Chang K, Sair H, Ye C, Zhang L, Zhuo Z, Liu Y. Deep Learning for Noninvasive Assessment of H3 K27M Mutation Status in Diffuse Midline Gliomas Using MR Imaging. J Magn Reson Imaging 2023; 58:850-861. [PMID: 36692205 DOI: 10.1002/jmri.28606] [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: 10/21/2022] [Revised: 01/05/2023] [Accepted: 01/07/2023] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Determination of H3 K27M mutation in diffuse midline glioma (DMG) is key for prognostic assessment and stratifying patient subgroups for clinical trials. MRI can noninvasively depict morphological and metabolic characteristics of H3 K27M mutant DMG. PURPOSE This study aimed to develop a deep learning (DL) approach to noninvasively predict H3 K27M mutation in DMG using T2-weighted images. STUDY TYPE Retrospective and prospective. POPULATION For diffuse midline brain gliomas, 341 patients from Center-1 (27 ± 19 years, 184 males), 42 patients from Center-2 (33 ± 19 years, 27 males) and 35 patients (37 ± 18 years, 24 males). For diffuse spinal cord gliomas, 133 patients from Center-1 (30 ± 15 years, 80 males). FIELD STRENGTH/SEQUENCE 5T and 3T, T2-weighted turbo spin echo imaging. ASSESSMENT Conventional radiological features were independently reviewed by two neuroradiologists. H3 K27M status was determined by histopathological examination. The Dice coefficient was used to evaluate segmentation performance. Classification performance was evaluated using accuracy, sensitivity, specificity, and area under the curve. STATISTICAL TESTS Pearson's Chi-squared test, Fisher's exact test, two-sample Student's t-test and Mann-Whitney U test. A two-sided P value <0.05 was considered statistically significant. RESULTS In the testing cohort, Dice coefficients of tumor segmentation using DL were 0.87 for diffuse midline brain and 0.81 for spinal cord gliomas. In the internal prospective testing dataset, the predictive accuracies, sensitivities, and specificities of H3 K27M mutation status were 92.1%, 98.2%, 82.9% in diffuse midline brain gliomas and 85.4%, 88.9%, 82.6% in spinal cord gliomas. Furthermore, this study showed that the performance generalizes to external institutions, with predictive accuracies of 85.7%-90.5%, sensitivities of 90.9%-96.0%, and specificities of 82.4%-83.3%. DATA CONCLUSION In this study, an automatic DL framework was developed and validated for accurately predicting H3 K27M mutation using T2-weighted images, which could contribute to the noninvasive determination of H3 K27M status for clinical decision-making. EVIDENCE LEVEL 2 Technical Efficacy: Stage 2.
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Affiliation(s)
- Junjie Li
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Peng Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Liying Qu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Ting Sun
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Yunyun Duan
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Minghao Wu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Jinyuan Weng
- Department of Medical Imaging Product, Neusoft, Group Ltd., Shenyang, People's Republic of China
| | - Zhaohui Li
- BioMind Inc., Beijing, People's Republic of China
| | - Xiaodong Gong
- Department of Medical Imaging Product, Neusoft, Group Ltd., Shenyang, People's Republic of China
| | - Xing Liu
- Department of Pathology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Yongzhi Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Wenqing Jia
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Xiaorui Su
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, People's Republic of China
| | - Qiang Yue
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, People's Republic of China
| | - Jianrui Li
- Department of Diagnostic Radiology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, People's Republic of China
| | - Zhiqiang Zhang
- Department of Diagnostic Radiology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, People's Republic of China
| | - Frederik Barkhof
- UCL Institutes of Neurology and Healthcare Engineering, London, UK
- Department of Radiology & Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Raymond Y Huang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Ken Chang
- Department of Radiology, Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Haris Sair
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Chuyang Ye
- School of Information and Electronics, Beijing Institute of Technology, Beijing, People's Republic of China
| | - Liwei Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Zhizheng Zhuo
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Yaou Liu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China
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10
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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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11
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Lasocki A, Buckland ME, Molinaro T, Xie J, Whittle JR, Wei H, Gaillard F. Correlating MRI features with additional genetic markers and patient survival in histological grade 2-3 IDH-mutant astrocytomas. Neuroradiology 2023:10.1007/s00234-023-03175-0. [PMID: 37316586 DOI: 10.1007/s00234-023-03175-0] [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: 03/31/2023] [Accepted: 06/04/2023] [Indexed: 06/16/2023]
Abstract
PURPOSE The increasing importance of molecular markers for classification and prognostication of diffuse gliomas has prompted the use of imaging features to predict genotype ("radiogenomics"). CDKN2A/B homozygous deletion has only recently been added to the diagnostic paradigm for IDH[isocitrate dehydrogenase]-mutant astrocytomas; thus, associated radiogenomic literature is sparse. There is also little data on whether different IDH mutations are associated with different imaging appearances. Furthermore, given that molecular status is now generally obtained routinely, the additional prognostic value of radiogenomic features is less clear. This study correlated MRI features with CDKN2A/B status, IDH mutation type and survival in histological grade 2-3 IDH-mutant brain astrocytomas. METHODS Fifty-eight grade 2-3 IDH-mutant astrocytomas were identified, 50 with CDKN2A/B results. IDH mutations were stratified into IDH1-R132H and non-canonical mutations. Background and survival data were obtained. Two neuroradiologists independently assessed the following MRI features: T2-FLAIR mismatch (<25%, 25-50%, >50%), well-defined tumour margins, contrast-enhancement (absent, wispy, solid) and central necrosis. RESULTS 8/50 tumours with CDKN2A/B results demonstrated homozygous deletion; slightly shorter survival was not significant (p=0.571). IDH1-R132H mutations were present in 50/58 (86%). No MRI features correlated with CDKN2A/B status or IDH mutation type. T2-FLAIR mismatch did not predict survival (p=0.977), but well-defined margins predicted longer survival (HR 0.36, p=0.008), while solid enhancement predicted shorter survival (HR 3.86, p=0.004). Both correlations remained significant on multivariate analysis. CONCLUSION MRI features did not predict CDKN2A/B homozygous deletion, but provided additional positive and negative prognostic information which correlated more strongly with prognosis than CDKN2A/B status in our cohort.
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Affiliation(s)
- Arian Lasocki
- Department of Cancer Imaging, Peter MacCallum Cancer Centre, Grattan St, Melbourne, Melbourne, Victoria, 3000, Australia.
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia.
- Department of Radiology, The University of Melbourne, Parkville, Victoria, Australia.
| | - Michael E Buckland
- Department of Neuropathology, Royal Prince Alfred Hospital, Camperdown, NSW, Australia
- School of Medical Sciences, University of Sydney, Camperdown, NSW, Australia
| | - Tahlia Molinaro
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Jing Xie
- Centre for Biostatistics and Clinical Trials, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - James R Whittle
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Personalised Oncology Division, Walter and Eliza Hall Institute, Parkville, Victoria, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia
| | - Heng Wei
- Department of Neuropathology, Royal Prince Alfred Hospital, Camperdown, NSW, 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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Luo J, Pan M, Mo K, Mao Y, Zou D. Emerging role of artificial intelligence in diagnosis, classification and clinical management of glioma. Semin Cancer Biol 2023; 91:110-123. [PMID: 36907387 DOI: 10.1016/j.semcancer.2023.03.006] [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: 10/15/2022] [Revised: 03/05/2023] [Accepted: 03/08/2023] [Indexed: 03/12/2023]
Abstract
Glioma represents a dominant primary intracranial malignancy in the central nervous system. Artificial intelligence that mainly includes machine learning, and deep learning computational approaches, presents a unique opportunity to enhance clinical management of glioma through improving tumor segmentation, diagnosis, differentiation, grading, treatment, prediction of clinical outcomes (prognosis, and recurrence), molecular features, clinical classification, characterization of the tumor microenvironment, and drug discovery. A growing body of recent studies apply artificial intelligence-based models to disparate data sources of glioma, covering imaging modalities, digital pathology, high-throughput multi-omics data (especially emerging single-cell RNA sequencing and spatial transcriptome), etc. While these early findings are promising, future studies are required to normalize artificial intelligence-based models to improve the generalizability and interpretability of the results. Despite prominent issues, targeted clinical application of artificial intelligence approaches in glioma will facilitate the development of precision medicine of this field. If these challenges can be overcome, artificial intelligence has the potential to profoundly change the way patients with or at risk of glioma are provided with more rational care.
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Affiliation(s)
- Jiefeng Luo
- Department of Neurology, The Second Affiliated Hospital of Guangxi Medical University, Nanning 530007, Guangxi, China
| | - Mika Pan
- Department of Neurology, The Second Affiliated Hospital of Guangxi Medical University, Nanning 530007, Guangxi, China
| | - Ke Mo
- Clinical Research Center, The Second Affiliated Hospital of Guangxi Medical University, Nanning 530007, Guangxi, China
| | - Yingwei Mao
- Department of Biology, Pennsylvania State University, University Park, PA 16802, USA.
| | - Donghua Zou
- Department of Neurology, The Second Affiliated Hospital of Guangxi Medical University, Nanning 530007, Guangxi, China; Clinical Research Center, The Second Affiliated Hospital of Guangxi Medical University, Nanning 530007, Guangxi, China.
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13
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Kihira S, Derakhshani A, Leung M, Mahmoudi K, Bauer A, Zhang H, Polson J, Arnold C, Tsankova NM, Hormigo A, Salehi B, Pham N, Ellingson BM, Cloughesy TF, Nael K. Multi-Parametric Radiomic Model to Predict 1p/19q Co-Deletion in Patients with IDH-1 Mutant Glioma: Added Value to the T2-FLAIR Mismatch Sign. Cancers (Basel) 2023; 15:cancers15041037. [PMID: 36831380 PMCID: PMC9954034 DOI: 10.3390/cancers15041037] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 01/28/2023] [Accepted: 01/28/2023] [Indexed: 02/10/2023] Open
Abstract
PURPOSE The T2-FLAIR mismatch sign has shown promise in determining IDH mutant 1p/19q non-co-deleted gliomas with a high specificity and modest sensitivity. To develop a multi-parametric radiomic model using MRI to predict 1p/19q co-deletion status in patients with newly diagnosed IDH1 mutant glioma and to perform a comparative analysis to T2-FLAIR mismatch sign+. METHODS In this retrospective study, patients with diagnosis of IDH1 mutant gliomas with known 1p/19q status who had preoperative MRI were included. T2-FLAIR mismatch was evaluated independently by two board-certified neuroradiologists. Texture features were extracted from glioma segmentation of FLAIR images. eXtremeGradient Boosting (XGboost) classifiers were used for model development. Leave-one-out-cross-validation (LOOCV) and external validation performances were reported for both the training and external validation sets. RESULTS A total of 103 patients were included for model development and 18 patients for external testing validation. The diagnostic performance (sensitivity/specificity/accuracy) in the determination of the 1p/19q co-deletion status was 59%/83%/67% (training) and 62.5%/70.0%/66.3% (testing) for the T2-FLAIR mismatch sign. This was significantly improved (p = 0.04) using the radiomics model to 77.9%/82.8%/80.3% (training) and 87.5%/89.9%/88.8% (testing), respectively. The addition of radiomics as a computer-assisted tool resulted in significant (p = 0.02) improvement in the performance of the neuroradiologist with 13 additional corrected cases in comparison to just using the T2-FLAIR mismatch sign. CONCLUSION The proposed radiomic model provides much needed sensitivity to the highly specific T2-FLAIR mismatch sign in the determination of the 1p/19q non-co-deletion status and improves the overall diagnostic performance of neuroradiologists when used as an assistive tool.
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Affiliation(s)
- Shingo Kihira
- Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, 757 Westwood Plaza, Suite 1621, Los Angeles, CA 90095, USA
| | - Ahrya Derakhshani
- Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, 757 Westwood Plaza, Suite 1621, Los Angeles, CA 90095, USA
| | - Michael Leung
- Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, 757 Westwood Plaza, Suite 1621, Los Angeles, CA 90095, USA
| | - Keon Mahmoudi
- Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, 757 Westwood Plaza, Suite 1621, Los Angeles, CA 90095, USA
| | - Adam Bauer
- Department of Radiology, Kaiser Permanente Fontana Medical Center, Fontana, CA 92335, USA
| | - Haoyue Zhang
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Jennifer Polson
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Corey Arnold
- Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, 757 Westwood Plaza, Suite 1621, Los Angeles, CA 90095, USA
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Nadejda M. Tsankova
- Department of Pathology and Laboratory Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Adilia Hormigo
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Banafsheh Salehi
- Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, 757 Westwood Plaza, Suite 1621, Los Angeles, CA 90095, USA
| | - Nancy Pham
- Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, 757 Westwood Plaza, Suite 1621, Los Angeles, CA 90095, USA
| | - Benjamin M. Ellingson
- Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, 757 Westwood Plaza, Suite 1621, Los Angeles, CA 90095, USA
- UCLA Brain Tumor Imaging Laboratory, Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Timothy F. Cloughesy
- UCLA Brain Tumor Imaging Laboratory, Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA 90095, USA
- Department of Neurology, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Kambiz Nael
- Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, 757 Westwood Plaza, Suite 1621, Los Angeles, CA 90095, USA
- Correspondence: ; Tel.: +1-310-267-5932
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Park YW, Park KS, Park JE, Ahn SS, Park I, Kim HS, Chang JH, Lee SK, Kim SH. Qualitative and Quantitative Magnetic Resonance Imaging Phenotypes May Predict CDKN2A/B Homozygous Deletion Status in Isocitrate Dehydrogenase-Mutant Astrocytomas: A Multicenter Study. Korean J Radiol 2023; 24:133-144. [PMID: 36725354 PMCID: PMC9892217 DOI: 10.3348/kjr.2022.0732] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 11/22/2022] [Accepted: 12/10/2022] [Indexed: 01/28/2023] Open
Abstract
OBJECTIVE Cyclin-dependent kinase inhibitor (CDKN)2A/B homozygous deletion is a key molecular marker of isocitrate dehydrogenase (IDH)-mutant astrocytomas in the 2021 World Health Organization. We aimed to investigate whether qualitative and quantitative MRI parameters can predict CDKN2A/B homozygous deletion status in IDH-mutant astrocytomas. MATERIALS AND METHODS Preoperative MRI data of 88 patients (mean age ± standard deviation, 42.0 ± 11.9 years; 40 females and 48 males) with IDH-mutant astrocytomas (76 without and 12 with CDKN2A/B homozygous deletion) from two institutions were included. A qualitative imaging assessment was performed. Mean apparent diffusion coefficient (ADC), 5th percentile of ADC, mean normalized cerebral blood volume (nCBV), and 95th percentile of nCBV were assessed via automatic tumor segmentation. Logistic regression was performed to determine the factors associated with CDKN2A/B homozygous deletion in all 88 patients and a subgroup of 47 patients with histological grades 3 and 4. The discrimination performance of the logistic regression models was evaluated using the area under the receiver operating characteristic curve (AUC). RESULTS In multivariable analysis of all patients, infiltrative pattern (odds ratio [OR] = 4.25, p = 0.034), maximal diameter (OR = 1.07, p = 0.013), and 95th percentile of nCBV (OR = 1.34, p = 0.049) were independent predictors of CDKN2A/B homozygous deletion. The AUC, accuracy, sensitivity, and specificity of the corresponding model were 0.83 (95% confidence interval [CI], 0.72-0.91), 90.4%, 83.3%, and 75.0%, respectively. On multivariable analysis of the subgroup with histological grades 3 and 4, infiltrative pattern (OR = 10.39, p = 0.012) and 95th percentile of nCBV (OR = 1.24, p = 0.047) were independent predictors of CDKN2A/B homozygous deletion, with an AUC accuracy, sensitivity, and specificity of the corresponding model of 0.76 (95% CI, 0.60-0.88), 87.8%, 80.0%, and 58.1%, respectively. CONCLUSION The presence of an infiltrative pattern, larger maximal diameter, and higher 95th percentile of the nCBV may be useful MRI biomarkers for CDKN2A/B homozygous deletion in IDH-mutant astrocytomas.
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Affiliation(s)
- Yae Won Park
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Ki Sung Park
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, Korea
| | - Ji Eun Park
- Department of Radiology, Ulsan University College of Medicine, Seoul, Korea
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Inho Park
- Center for Precision Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
- Department of Pathology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Ho Sung Kim
- Department of Radiology, Ulsan University College of Medicine, Seoul, Korea
| | - Jong Hee Chang
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea
| | - Seung-Koo Lee
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Se Hoon Kim
- Department of Pathology, Yonsei University College of Medicine, Seoul, Korea.
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Jang EB, Kim HS, Park JE, Park SY, Nam YK, Nam SJ, Kim YH, Kim JH. Diffuse glioma, not otherwise specified: imaging-based risk stratification achieves histomolecular-level prognostication. Eur Radiol 2022; 32:7780-7788. [PMID: 35587830 DOI: 10.1007/s00330-022-08850-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 04/20/2022] [Accepted: 04/27/2022] [Indexed: 01/03/2023]
Abstract
OBJECTIVES To determine whether imaging-based risk stratification enables prognostication in diffuse glioma, NOS (not otherwise specified). METHODS Data from 220 patients classified as diffuse glioma, NOS, between January 2011 and December 2020 were retrospectively included. Two neuroradiologists analyzed pre-surgical CT and MRI to assign gliomas to the three imaging-based risk types considering well-known imaging phenotypes (e.g., T2/FLAIR mismatch). According to the 2021 World Health Organization classification, the three risk types included (1) low-risk, expecting oligodendroglioma, isocitrate dehydrogenase (IDH)-mutant, and 1p/19q-codeleted; (2) intermediate-risk, expecting astrocytoma, IDH-mutant; and (3) high-risk, expecting glioblastoma, IDH-wildtype. Progression-free survival (PFS) and overall survival (OS) were estimated for each risk type. Time-dependent receiver operating characteristic analysis using 10-fold cross-validation with 100-fold bootstrapping was used to compare the performance of an imaging-based survival model with that of a historical molecular-based survival model published in 2015, created using The Cancer Genome Archive data. RESULTS Prognostication according to the three imaging-based risk types was achieved for both PFS and OS (log-rank test, p < 0.001). The imaging-based survival model showed high prognostic value, with areas under the curves (AUCs) of 0.772 and 0.650 for 1-year PFS and OS, respectively, similar to the historical molecular-based survival model (AUC = 0.74 for PFS and 0.87 for OS). The imaging-based survival model achieved high long-term performance in both 3-year PFS (AUC = 0.806) and 5-year OS (AUC = 0.812). CONCLUSION Imaging-based risk stratification achieved histomolecular-level prognostication in diffuse glioma, NOS, and could aid in guiding patient referral for insufficient or unsuccessful molecular diagnosis. KEY POINTS • Three imaging-based risk types enable distinct prognostication in diffuse glioma, NOS (not otherwise specified). • The imaging-based survival model achieved similar prognostic performance as a historical molecular-based survival model. • For long-term prognostication of 3 and 5 years, the imaging-based survival model showed high performance.
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Affiliation(s)
- Eun Bee Jang
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea
| | - Ho Sung Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea.
| | - Ji Eun Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea
| | - Seo Young Park
- Department of Statistics and Data Science, Korea National Open University, Seoul, Republic of Korea
| | - Yeo Kyung Nam
- Department of Radiology, Shinchon Yonsei Hospital, Seoul, Republic of Korea
| | - Soo Jung Nam
- Department of Pathology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Young-Hoon Kim
- Department of Neurosurgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Jeong Hoon Kim
- Department of Neurosurgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
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Kanimozhi VS, Balasubramani M, Anuradha R. Hierarchal Bayes model with AlexNet for characterization of M-FISH chromosome images. Med Biol Eng Comput 2021; 59:1529-1544. [PMID: 34232447 DOI: 10.1007/s11517-021-02384-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Accepted: 05/14/2021] [Indexed: 10/20/2022]
Abstract
The analysis of chromosomes is a significant and challenging task for clinical diagnosis and biological research. The technique based on color imaging is a multiplex fluorescent in situ hybridization (M-FISH), which was implemented to ease the exploration of the chromosomes. Thus, in this paper, we propose a novel quasi-Newton-based K-means clustering for the M-FISH image segmentation. Then, we use the expectation-maximization-based hierarchical Bayes model to characterize the M-FISH images. The contextual-based classification and region merging of chromosomal images is made to avoid any misclassification, and we made use of AlexNet, by modifying the activation functions of the sigmoid and softmax layer and for the optimum classification between the autosomal chromosomes and the sex chromosome. Finally, we conducted a performance analysis by measuring accuracy, recall, sensitivity, specificity, PPV, NPV, F-score, kappa, Jaccard, and Dice coefficient and compared with other existing methods and found that our proposed methodology can achieve more percentage of accuracy (6.96%) than the state of the art methods.
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Affiliation(s)
- V S Kanimozhi
- Department of Electronics and Communication Engineering, Dhanalakshmi Srinivasan College of Engineering, Coimbatore, Tamil Nadu, India.
| | - M Balasubramani
- Department of Electronics and Communication Engineering, Info Institute of Engineering, Coimbatore, Tamil Nadu, India
| | - R Anuradha
- Department of Computer Science and Engineering, Sri Ramakrishna Engineering College, Coimbatore, Tamil Nadu, India
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Park YW, Park JE, Ahn SS, Kim EH, Kang SG, Chang JH, Kim SH, Choi SH, Kim HS, Lee SK. Magnetic Resonance Imaging Parameters for Noninvasive Prediction of Epidermal Growth Factor Receptor Amplification in Isocitrate Dehydrogenase-Wild-Type Lower-Grade Gliomas: A Multicenter Study. Neurosurgery 2021; 89:257-265. [PMID: 33913501 DOI: 10.1093/neuros/nyab136] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 02/21/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND The epidermal growth factor receptor (EGFR) amplification status of isocitrate dehydrogenase-wild-type (IDHwt) lower-grade gliomas (LGGs; grade II/III) is one of the key markers for diagnosing molecular glioblastoma. However, the association between EGFR status and imaging parameters is unclear. OBJECTIVE To identify noninvasive imaging parameters from diffusion-weighted and dynamic susceptibility contrast imaging for predicting the EGFR amplification status of IDHwt LGGs. METHODS A total of 86 IDHwt LGG patients with known EGFR amplification status (62 nonamplified and 24 amplified) from 3 tertiary institutions were included. Qualitative and quantitative imaging features, including histogram parameters from apparent diffusion coefficient (ADC), normalized cerebral blood volume (nCBV), and normalized cerebral blood flow (nCBF), were assessed. Univariable and multivariable logistic regression models were constructed. RESULTS On multivariable analysis, multifocal/multicentric distribution (odds ratio [OR] = 11.77, P = .006), mean ADC (OR = 0.01, P = .044), 5th percentile of ADC (OR = 0.01, P = .046), and 95th percentile of nCBF (OR = 1.24, P = .031) were independent predictors of EGFR amplification. The diagnostic performance of the model with qualitative imaging parameters increased significantly when quantitative imaging parameters were added, with areas under the curves of 0.81 and 0.93, respectively (P = .004). CONCLUSION The presence of multifocal/multicentric distribution patterns, lower mean ADC, lower 5th percentile of ADC, and higher 95th percentile of nCBF may be useful imaging biomarkers for EGFR amplification in IDHwt LGGs. Moreover, quantitative imaging biomarkers may add value to qualitative imaging parameters.
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Affiliation(s)
- Yae Won Park
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Ji Eun Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Seoul, Korea
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Eui Hyun Kim
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, South Korea
| | - Seok-Gu Kang
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, South Korea
| | - Jong Hee Chang
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, South Korea
| | - Se Hoon Kim
- Department of Pathology, Yonsei University College of Medicine, Seoul, South Korea
| | - Seung Hong Choi
- Department of Radiology, Seoul National University Hospital, Seoul, South Korea
| | - Ho Sung Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Seoul, Korea
| | - Seung-Koo Lee
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea
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Patel SH, Batchala PP, Muttikkal TJE, Ferrante SS, Patrie JT, Fadul CE, Schiff D, Lopes MB, Jain R. Fluid attenuation in non-contrast-enhancing tumor (nCET): an MRI Marker for Isocitrate Dehydrogenase (IDH) mutation in Glioblastoma. J Neurooncol 2021; 152:523-531. [PMID: 33661425 DOI: 10.1007/s11060-021-03720-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 02/12/2021] [Accepted: 02/15/2021] [Indexed: 11/29/2022]
Abstract
PURPOSE The WHO 2016 update classifies glioblastomas (WHO grade IV) according to isocitrate dehydrogenase (IDH) gene mutation status. We aimed to determine MRI-based metrics for predicting IDH mutation in glioblastoma. METHODS This retrospective study included glioblastoma cases (n = 199) with known IDH mutation status and pre-operative MRI (T1WI, T2WI, FLAIR, contrast-enhanced T1W1 at minimum). Two neuroradiologists determined the following MRI metrics: (1) primary lobe of involvement (frontal or non-frontal); (2) presence/absence of contrast-enhancement; (3) presence/absence of necrosis; (4) presence/absence of fluid attenuation in the non-contrast-enhancing tumor (nCET); (5) maximum width of peritumoral edema (cm); (6) presence/absence of multifocal disease. Inter-reader agreement was determined. After resolving discordant measurements, multivariate association between consensus MRI metrics/patient age and IDH mutation status was determined. RESULTS Among 199 glioblastomas, 16 were IDH-mutant. Inter-reader agreement was calculated for contrast-enhancement (ĸ = 0.49 [- 0.11-1.00]), necrosis (ĸ = 0.55 [0.34-0.76]), fluid attenuation in nCET (ĸ = 0.83 [0.68-0.99]), multifocal disease (ĸ = 0.55 [0.39-0.70]), and primary lobe (ĸ = 0.85 [0.80-0.91]). Mean difference for peritumoral edema width between readers was 0.3 cm [0.2-0.5], p < 0.001. Multivariate analysis uncovered significant associations between IDH-mutation and fluid attenuation in nCET (OR 82.9 [19.22, ∞], p < 0.001), younger age (OR 0.93 [0.86, 0.98], p = 0.009), frontal lobe location (OR 11.08 [1.14, 352.97], p = 0.037), and less peritumoral edema (OR 0.15 [0, 0.65], p = 0.044). CONCLUSIONS Conventional MRI metrics and patient age predict IDH-mutation status in glioblastoma. Among MRI markers, fluid attenuation in nCET represents a novel marker with high inter-reader agreement that is strongly associated with Glioblastoma, IDH-mutant.
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Affiliation(s)
- Sohil H Patel
- Department of Radiology and Medical Imaging, University of Virginia Health System, PO Box 800170, Charlottesville, VA, 22908, USA.
| | - Prem P Batchala
- Department of Radiology and Medical Imaging, University of Virginia Health System, PO Box 800170, Charlottesville, VA, 22908, USA
| | - Thomas J Eluvathingal Muttikkal
- Department of Radiology and Medical Imaging, University of Virginia Health System, PO Box 800170, Charlottesville, VA, 22908, USA
| | - Sergio S Ferrante
- Department of Radiology and Medical Imaging, University of Virginia Health System, PO Box 800170, Charlottesville, VA, 22908, USA
| | - James T Patrie
- Department of Public Health Sciences, University of Virginia Health System, Charlottesville, VA, USA
| | - Camilo E Fadul
- Division of Neuro-Oncology, Department of Neurology, University of Virginia Health System, Charlottesville, VA, USA
| | - David Schiff
- Division of Neuro-Oncology, Department of Neurology, University of Virginia Health System, Charlottesville, VA, USA
| | - M Beatriz Lopes
- Department of Pathology, Divisions of Neuropathology and Molecular Diagnostics, University of Virginia Health System, Charlottesville, VA, USA
| | - Rajan Jain
- Department of Radiology, New York University School of Medicine, 550 1st Avenue, New York, NY, 10016, USA.,Department of Neurosurgery, New York University School of Medicine, 550 1st Avenue, New York, NY, 10016, USA
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Automated apparent diffusion coefficient analysis for genotype prediction in lower grade glioma: association with the T2-FLAIR mismatch sign. J Neurooncol 2020; 149:325-335. [PMID: 32909115 DOI: 10.1007/s11060-020-03611-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 08/31/2020] [Indexed: 12/24/2022]
Abstract
PURPOSE The prognosis of lower grade glioma (LGG) patients depends (in large part) on both isocitrate dehydrogenase (IDH) gene mutation and chromosome 1p/19q codeletion status. IDH-mutant LGG without 1p/19q codeletion (IDHmut-Noncodel) often exhibit a unique imaging appearance that includes high apparent diffusion coefficient (ADC) values not observed in other subtypes. The purpose of this study was to develop an ADC analysis-based approach that can automatically identify IDHmut-Noncodel LGG. METHODS Whole-tumor ADC metrics, including fractional tumor volume with ADC > 1.5 × 10-3mm2/s (VADC>1.5), were used to identify IDHmut-Noncodel LGG in a cohort of N = 134 patients. Optimal threshold values determined in this dataset were then validated using an external dataset containing N = 93 cases collected from The Cancer Imaging Archive. Classifications were also compared with radiologist-identified T2-FLAIR mismatch sign and evaluated concurrently to identify added value from a combined approach. RESULTS VADC>1.5 classified IDHmut-Noncodel LGG in the internal cohort with an area under the curve (AUC) of 0.80. An optimal threshold value of 0.35 led to sensitivity/specificity = 0.57/0.93. Classification performance was similar in the validation cohort, with VADC>1.5 ≥ 0.35 achieving sensitivity/specificity = 0.57/0.91 (AUC = 0.81). Across both groups, 37 cases exhibited positive T2-FLAIR mismatch sign-all of which were IDHmut-Noncodel. Of these, 32/37 (86%) also exhibited VADC>1.5 ≥ 0.35, as did 23 additional IDHmut-Noncodel cases which were negative for T2-FLAIR mismatch sign. CONCLUSION Tumor subregions with high ADC were a robust indicator of IDHmut-Noncodel LGG, with VADC>1.5 achieving > 90% classification specificity in both internal and validation cohorts. VADC>1.5 exhibited strong concordance with the T2-FLAIR mismatch sign and the combination of both parameters improved sensitivity in detecting IDHmut-Noncodel LGG.
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Chu JP, Song YK, Tian YS, Qiu HS, Huang XH, Wang YL, Huang YQ, Zhao J. Diffusion kurtosis imaging in evaluating gliomas: different region of interest selection methods on time efficiency, measurement repeatability, and diagnostic ability. Eur Radiol 2020; 31:729-739. [PMID: 32857204 DOI: 10.1007/s00330-020-07204-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Revised: 07/05/2020] [Accepted: 08/18/2020] [Indexed: 12/25/2022]
Abstract
OBJECTIVES Comparing the diagnostic efficacy of diffusion kurtosis imaging (DKI) derived from different region of interest (ROI) methods in tumor parenchyma for grading and predicting IDH-1 mutation and 1p19q co-deletion status of glioma patients and correlating with their survival data. METHODS Sixty-six patients (29 females; median age, 45 years) with pathologically proved gliomas (low-grade gliomas, 36; high-grade gliomas, 30) were prospectively included, and their clinical data were collected. All patients underwent DKI examination. DKI maps of each metric were derived. Three groups of ROIs (ten spots, ROI-10s; three biggest tumor slices, ROI-3s; and whole-tumor parenchyma, ROI-whole) were manually drawn by two independent radiologists. The interobserver consistency, time spent, diagnostic efficacy, and survival analysis of DKI metrics based on these three ROI methods were analyzed. RESULTS The intraexaminer reliability for all parameters among these three ROI methods was good, and the time spent on ROI-10s was significantly less than that of the other two methods (p < 0.001). DKI based on ROI-10s demonstrated a slightly better diagnostic value than the other two ROI methods for grading and predicting the IDH-1 mutation status of glioma, whereas DKI metrics derived from ROI-10s performed much better than those of the ROI-3s and ROI-whole in identifying 1p19q co-deletion. In survival analysis, the model based on ROI-10s that included patient age and mean diffusivity showed the highest prediction value (C-index, 0.81). CONCLUSIONS Among the three ROI methods, the ROI-10s method had the least time spent and the best diagnostic value for a comprehensive evaluation of glioma. It is an effective way to process DKI data and has important application value in the clinical evaluation of glioma. KEY POINTS • The intraexaminer reliability for all DKI parameters among different ROI methods was good, and the time spent on ROI-10 spots was significantly less than the other two ROI methods. • DKI metrics derived from ROI-10 spots performed the best in ROI selection methods (ROI-10s, ten-spot ROIs; ROI-3s, three biggest tumor slices ROI; and ROI-whole, whole-tumor parenchyma ROI) for a comprehensive evaluation of glioma. • The ROI-10 spots method is an effective way to process DKI data and has important application value in the clinical evaluation of glioma.
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Affiliation(s)
- Jian-Ping Chu
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58, The Second Zhongshan Road, Guangzhou, 510080, Guangdong, China
| | - Yu-Kun Song
- Department of Radiology, The First Affiliated Hospital of Xiamen University, Xiamen, 361003, China
| | - Yi-Su Tian
- Department of Radiology, SICHUAN Cancer Hospital and Research Institute, Chengdu, 610041, China
| | - Hai-Shan Qiu
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58, The Second Zhongshan Road, Guangzhou, 510080, Guangdong, China
| | - Xia-Hua Huang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58, The Second Zhongshan Road, Guangzhou, 510080, Guangdong, China
| | - Yu-Liang Wang
- Department of Radiology, Shenzhen City Nanshan District People's Hospital, Shenzhen, 518000, China
| | - Ying-Qian Huang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58, The Second Zhongshan Road, Guangzhou, 510080, Guangdong, China
| | - Jing Zhao
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58, The Second Zhongshan Road, Guangzhou, 510080, Guangdong, China.
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