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Han T, Liu X, Zhou J. Progression/Recurrence of Meningioma: An Imaging Review Based on Magnetic Resonance Imaging. World Neurosurg 2024; 186:98-107. [PMID: 38499241 DOI: 10.1016/j.wneu.2024.03.051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 03/10/2024] [Accepted: 03/11/2024] [Indexed: 03/20/2024]
Abstract
Meningiomas are the most common primary central nervous system tumors. The preferred treatment is maximum safe resection, and the heterogeneity of meningiomas results in a variable prognosis. Progression/recurrence (P/R) can occur at any grade of meningioma and is a common adverse outcome after surgical treatment and a major cause of postoperative rehospitalization, secondary surgery, and mortality. Early prediction of P/R plays an important role in postoperative management, further adjuvant therapy, and follow-up of patients. Therefore, it is essential to thoroughly analyze the heterogeneity of meningiomas and predict postoperative P/R with the aid of noninvasive preoperative imaging. In recent years, the development of advanced magnetic resonance imaging technology and machine learning has provided new insights into noninvasive preoperative prediction of meningioma P/R, which helps to achieve accurate prediction of meningioma P/R. This narrative review summarizes the current research on conventional magnetic resonance imaging, functional magnetic resonance imaging, and machine learning in predicting meningioma P/R. We further explore the significance of tumor microenvironment in meningioma P/R, linking imaging features with tumor microenvironment to comprehensively reveal tumor heterogeneity and provide new ideas for future research.
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Affiliation(s)
- Tao Han
- Department of Radiology, Lanzhou University Second Hospita, Lanzhou, 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
| | - Xianwang Liu
- Department of Radiology, Lanzhou University Second Hospita, Lanzhou, 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 Hospita, 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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Park JH, Quang LT, Yoon W, Baek BH, Park I, Kim SK. Predicting Histologic Grade of Meningiomas Using a Combined Model of Radiomic and Clinical Imaging Features from Preoperative MRI. Biomedicines 2023; 11:3268. [PMID: 38137489 PMCID: PMC10741678 DOI: 10.3390/biomedicines11123268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 12/04/2023] [Accepted: 12/09/2023] [Indexed: 12/24/2023] Open
Abstract
Meningiomas are common primary brain tumors, and their accurate preoperative grading is crucial for treatment planning. This study aimed to evaluate the value of radiomics and clinical imaging features in predicting the histologic grade of meningiomas from preoperative MRI. We retrospectively reviewed patients with intracranial meningiomas from two hospitals. Preoperative MRIs were analyzed for tumor and edema volumes, enhancement patterns, margins, and tumor-brain interfaces. Radiomics features were extracted, and machine learning models were employed to predict meningioma grades. A total of 212 patients were included. In the training group (Hospital 1), significant differences were observed between low-grade and high-grade meningiomas in terms of tumor volume (p = 0.012), edema volume (p = 0.004), enhancement (p = 0.001), margin (p < 0.001), and tumor-brain interface (p < 0.001). Five radiomics features were selected for model development. The prediction model for radiomics features demonstrated an average validation accuracy of 0.74, while the model for clinical imaging features showed an average validation accuracy of 0.69. When applied to external test data (Hospital 2), the radiomics model achieved an area under the receiver operating characteristics curve (AUC) of 0.72 and accuracy of 0.69, while the clinical imaging model achieved an AUC of 0.82 and accuracy of 0.81. An improved performance was obtained from the model constructed by combining radiomics and clinical imaging features. In the combined model, the AUC and accuracy for meningioma grading were 0.86 and 0.73, respectively. In conclusion, this study demonstrates the potential value of radiomics and clinical imaging features in predicting the histologic grade of meningiomas. The combination of both radiomics and clinical imaging features achieved the highest AUC among the models. Therefore, the combined model of radiomics and clinical imaging features may offer a more effective tool for predicting clinical outcomes in meningioma patients.
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Affiliation(s)
- Jae Hyun Park
- Department of Radiology, Chonnam National University Hospital, Gwangju 61469, Republic of Korea; (J.H.P.); (W.Y.)
| | - Le Thanh Quang
- Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju 61469, Republic of Korea;
| | - Woong Yoon
- Department of Radiology, Chonnam National University Hospital, Gwangju 61469, Republic of Korea; (J.H.P.); (W.Y.)
- Department of Radiology, Chonnam National University Medical School, Gwangju 61469, Republic of Korea
| | - Byung Hyun Baek
- Department of Radiology, Chonnam National University Hospital, Gwangju 61469, Republic of Korea; (J.H.P.); (W.Y.)
- Department of Radiology, Chonnam National University Medical School, Gwangju 61469, Republic of Korea
| | - Ilwoo Park
- Department of Radiology, Chonnam National University Hospital, Gwangju 61469, Republic of Korea; (J.H.P.); (W.Y.)
- Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju 61469, Republic of Korea;
- Department of Radiology, Chonnam National University Medical School, Gwangju 61469, Republic of Korea
- Department of Data Science, Chonnam National University, Gwangju 61186, Republic of Korea
| | - Seul Kee Kim
- Department of Radiology, Chonnam National University Medical School, Gwangju 61469, Republic of Korea
- Department of Radiology, Chonnam National University Hwasun Hospital, Hwasun-gun 58128, Republic of Korea
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He M, Wang X, Huang C, Peng X, Li N, Li F, Dong H, Wang Z, Zhao L, Wu F, Zhang M, Guan X, Xu X. Development of a Clinicopathological-Radiomics Model for Predicting Progression and Recurrence in Meningioma Patients. Acad Radiol 2023:S1076-6332(23)00614-1. [PMID: 37993304 DOI: 10.1016/j.acra.2023.10.059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 10/28/2023] [Accepted: 10/30/2023] [Indexed: 11/24/2023]
Abstract
RATIONALE AND OBJECTIVES Tumor progression and recurrence(P/R)after surgical resection are common in meningioma patients and can indicate poor prognosis. This study aimed to investigate the values of clinicopathological information and preoperative magnetic resonance imaging (MRI) radiomics in predicting P/R and progression-free survival (PFS) in meningioma patients. METHODS AND MATERIALS A total of 169 patients with pathologically confirmed meningioma were included in this study, 54 of whom experienced P/R. Clinicopathological information, including age, gender, Simpson grading, World Health Organization (WHO) grading, Ki-67 index, and radiotherapy history, as well as preoperative traditional radiographic findings and radiomics features for each MRI modality (T1-weighted, T2-weighted, and enhanced T1-weighted images) were initially extracted. After feature selection, the optimal performance was estimated among the models established using different feature sets. Finally, Cox survival analysis was further used to predict PFS. RESULTS Ki-67 index, Simpson grading, WHO grading, and radiotherapy history were found to be independent predictors for P/R in the multivariate regression analysis. This clinicopathological model had an area under the curve (AUC) of 0.865 and 0.817 in the training and testing sets, respectively. The performance of the combined radiomics model reached 0.85 and 0.84, respectively. A clinicopathological-radiomics model was then established, which significantly improved the prediction of meningioma P/R (AUC = 0.93 and 0.88, respectively). Finally, the risk ratio was estimated for each selected feature, and the C-index of 0.749 was obtained. CONCLUSION Radiomics signatures of preoperative MRI have the ability to predict meningioma at the risk of P/R. By integrating clinicopathological information, the best performance was achieved.
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Affiliation(s)
- Mengna He
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China (M.H., X.W., X.P., Z.W., L.Z., F.W., M.Z., X.G., X.X.); Department of Radiology, Shaoxing No. 2 Hospital Medical Community General Hospital, Shaoxing, China (M.H.)
| | - Xiaolan Wang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China (M.H., X.W., X.P., Z.W., L.Z., F.W., M.Z., X.G., X.X.)
| | - Chencui Huang
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co, Ltd, Beijing, China (C.H., F.L., H.D.)
| | - Xiting Peng
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China (M.H., X.W., X.P., Z.W., L.Z., F.W., M.Z., X.G., X.X.)
| | - Ning Li
- Department of Radiology, Fuyang District First People's Hospital, Hangzhou, China (N.L.)
| | - Feng Li
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co, Ltd, Beijing, China (C.H., F.L., H.D.)
| | - Hao Dong
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co, Ltd, Beijing, China (C.H., F.L., H.D.)
| | - Zhengyang Wang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China (M.H., X.W., X.P., Z.W., L.Z., F.W., M.Z., X.G., X.X.)
| | - Lingli Zhao
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China (M.H., X.W., X.P., Z.W., L.Z., F.W., M.Z., X.G., X.X.)
| | - Fengping Wu
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China (M.H., X.W., X.P., Z.W., L.Z., F.W., M.Z., X.G., X.X.)
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China (M.H., X.W., X.P., Z.W., L.Z., F.W., M.Z., X.G., X.X.).
| | - Xiaojun Guan
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China (M.H., X.W., X.P., Z.W., L.Z., F.W., M.Z., X.G., X.X.)
| | - Xiaojun Xu
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China (M.H., X.W., X.P., Z.W., L.Z., F.W., M.Z., X.G., X.X.)
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Iglseder S, Iglseder A, Beliveau V, Heugenhauser J, Gizewski ER, Kerschbaumer J, Stockhammer G, Uprimny C, Virgolini I, Dudas J, Nevinny-Stickel M, Nowosielski M, Scherfler C. Somatostatin receptor subtype expression and radiomics from DWI-MRI represent SUV of [68Ga]Ga-DOTATOC PET in patients with meningioma. J Neurooncol 2023; 164:711-720. [PMID: 37707754 PMCID: PMC10589159 DOI: 10.1007/s11060-023-04414-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 08/03/2023] [Indexed: 09/15/2023]
Abstract
OBJECTIVE This retrospective study aimed to analyse the correlation between somatostatin receptor subtypes (SSTR 1-5) and maximum standardized uptake value (SUVmax) in meningioma patients using Gallium-68 DOTA-D-Phe1-Tyr3-octreotide Positron Emission Tomography ([68Ga]Ga-DOTATOC PET). Secondly, we developed a radiomic model based on apparent diffusion coefficient (ADC) maps derived from diffusion weighted magnetic resonance images (DWI MRI) to reproduce SUVmax. METHOD The study included 51 patients who underwent MRI and [68Ga]Ga-DOTATOC PET before meningioma surgery. SUVmax values were quantified from PET images and tumour areas were segmented on post-contrast T1-weighted MRI and mapped to ADC maps. A total of 1940 radiomic features were extracted from the tumour area on each ADC map. A random forest regression model was trained to predict SUVmax and the model's performance was evaluated using repeated nested cross-validation. The expression of SSTR subtypes was quantified in 18 surgical specimens and compared to SUVmax values. RESULTS The random forest regression model successfully predicted SUVmax values with a significant correlation observed in all 100 repeats (p < 0.05). The mean Pearson's r was 0.42 ± 0.07 SD, and the root mean square error (RMSE) was 28.46 ± 0.16. SSTR subtypes 2A, 2B, and 5 showed significant correlations with SUVmax values (p < 0.001, R2 = 0.669; p = 0.001, R2 = 0.393; and p = 0.012, R2 = 0.235, respectively). CONCLUSION SSTR subtypes 2A, 2B, and 5 correlated significantly with SUVmax in meningioma patients. The developed radiomic model based on ADC maps effectively reproduces SUVmax using [68Ga]Ga-DOTATOC PET.
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Affiliation(s)
- Sarah Iglseder
- Department of Neurology, Innsbruck Medical University, Innsbruck, Austria
| | - Anna Iglseder
- Department of Geodesy and Geoinformation, Technical University Vienna, Vienna, Austria
| | - Vincent Beliveau
- Department of Neurology, Innsbruck Medical University, Innsbruck, Austria
- Neuroimaging Research Core Facility, Innsbruck Medical University, Innsbruck, Austria
| | | | - Elke R Gizewski
- Neuroimaging Research Core Facility, Innsbruck Medical University, Innsbruck, Austria
- Department of Neuroradiology, Innsbruck Medical University, Innsbruck, Austria
| | | | | | - Christian Uprimny
- Department of Nuclear Medicine, Innsbruck Medical University, Innsbruck, Austria
| | - Irene Virgolini
- Department of Nuclear Medicine, Innsbruck Medical University, Innsbruck, Austria
| | - Jozsef Dudas
- Department of Otorhinolaryngology, Innsbruck Medical University, Innsbruck, Austria
| | - Meinhard Nevinny-Stickel
- Department of Therapeutic Radiology and Oncology, Innsbruck Medical University, Innsbruck, Austria
| | - Martha Nowosielski
- Department of Neurology, Innsbruck Medical University, Innsbruck, Austria.
| | - Christoph Scherfler
- Department of Neurology, Innsbruck Medical University, Innsbruck, Austria
- Department of Neuroradiology, Innsbruck Medical University, Innsbruck, Austria
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Moon CM, Lee YY, Kim DY, Yoon W, Baek BH, Park JH, Heo SH, Shin SS, Kim SK. Preoperative prediction of Ki-67 and p53 status in meningioma using a multiparametric MRI-based clinical-radiomic model. Front Oncol 2023; 13:1138069. [PMID: 37287921 PMCID: PMC10241997 DOI: 10.3389/fonc.2023.1138069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 02/08/2023] [Indexed: 06/09/2023] Open
Abstract
Purpose To investigate the utility of preoperative multiparametric magnetic resonance imaging (mpMRI)-based clinical-radiomic analysis combined with machine learning (ML) algorithms in predicting the expression of the Ki-67 proliferative index and p53 tumor suppressor protein in patients with meningioma. Methods This multicenter retrospective study included 483 and 93 patients from two centers. The Ki-67 index was classified into high (Ki-67≥5%) and low (Ki-67<5%)-expressed groups, and the p53 index was classified into positive (p53≥5%) and negative (p53<5%)-expressed groups. Clinical and radiological features were analyzed using univariate and multivariate statistical analyses. Six ML models were performed with different types of classifiers to predict Ki-67 and p53 status. Results In the multivariate analysis, larger tumor volumes (p<0.001), irregular tumor margin (p<0.001), and unclear tumor-brain interface (p<0.001) were independently associated with a high Ki-67 status, whereas the presence of both necrosis (p=0.003) and the dural tail sign (p=0.026) were independently associated with a positive p53 status. A relatively better performance was yielded from the model constructed by combined clinical and radiological features. The area under the curve (AUC) and accuracy of high Ki-67 were 0.820 and 0.867 in the internal test, and 0.666 and 0.773 in the external test, respectively. Regarding p53 positivity, the AUC and accuracy were 0.858 and 0.857 in the internal test, and 0.684 and 0.718 in the external test. Conclusion The present study developed clinical-radiomic ML models to non-invasively predict Ki-67 and p53 expression in meningioma using mpMRI features, and provides a novel non-invasive strategy for assessing cell proliferation.
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Affiliation(s)
- Chung-Man Moon
- Research Institute of Medical Sciences, Chonnam National University, Gwangju, Republic of Korea
| | - Yun Young Lee
- Department of Radiology, Chonnam National University Hospital, Gwangju, Republic of Korea
| | - Doo-Young Kim
- Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju, Republic of Korea
| | - Woong Yoon
- Department of Radiology, Chonnam National University Hospital, Gwangju, Republic of Korea
- Department of Radiology, Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Byung Hyun Baek
- Department of Radiology, Chonnam National University Hospital, Gwangju, Republic of Korea
- Department of Radiology, Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Jae-Hyun Park
- Department of Radiology, Chonnam National University Hospital, Gwangju, Republic of Korea
| | - Suk-Hee Heo
- Department of Radiology, Chonnam National University Medical School, Gwangju, Republic of Korea
- Department of Radiology, Chonnam National University Hwasun Hospital, Hwasun, Republic of Korea
| | - Sang-Soo Shin
- Department of Radiology, Chonnam National University Hospital, Gwangju, Republic of Korea
- Department of Radiology, Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Seul Kee Kim
- Department of Radiology, Chonnam National University Medical School, Gwangju, Republic of Korea
- Department of Radiology, Chonnam National University Hwasun Hospital, Hwasun, Republic of Korea
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Abstract
The term biomarker is used to describe a biological measure of the disease behavior. The existing imaging biomarkers are associated with the known tissue biological characteristics and follow a well-established roadmap to be implemented in routine clinical practice. Recently, a new quantitative imaging analysis approach named radiomics has emerged. It refers to the extraction of a large number of advanced imaging features with high-throughput computing. Extensive research has demonstrated its value in predicting disease behavior, progression, and response to therapeutic options. However, there are numerous challenges to establishing it as a clinically viable solution, including lack of reproducibility and transparency. The data-driven nature also does not offer insights into the underpinning biology of the observed relationships. As such, additional effort is needed to establish it as a qualified biomarker to inform clinical decisions. Here we review the technical difficulties encountered in the clinical applications of radiomics and current effort in addressing some of these challenges in clinical trial designs. By addressing these challenges, the true potential of radiomics can be unleashed.
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Affiliation(s)
- Ke Nie
- Rutgers-Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, Department of Radiation Oncology, New Brunswick, NJ, 08901, United States of America
| | - Ying Xiao
- University of Pennsylvania, Department of Radiation Oncology, 3400 Civic Center Blvd, TRC-2 West Philadelphia, PA 19104, United States of America
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Im C. Medical student's artificial intelligence education and research experiences. Korean J Med Educ 2022; 34:341-344. [PMID: 36464905 PMCID: PMC9726235 DOI: 10.3946/kjme.2022.243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 11/07/2022] [Accepted: 11/07/2022] [Indexed: 06/17/2023]
Affiliation(s)
- Chaeyeong Im
- College of Medicine, Chonnam National University Medical School, Gwangju, Korea
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