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Kalasauskas D, Kosterhon M, Kurz E, Schmidt L, Altmann S, Grauhan NF, Sommer C, Othman A, Brockmann MA, Ringel F, Keric N. Preoperative prediction of CNS WHO grade and tumour aggressiveness in intracranial meningioma based on radiomics and structured semantics. Sci Rep 2024; 14:20586. [PMID: 39232068 PMCID: PMC11374997 DOI: 10.1038/s41598-024-71200-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Accepted: 08/26/2024] [Indexed: 09/06/2024] Open
Abstract
Preoperative identification of intracranial meningiomas with aggressive behaviour may help in choosing the optimal treatment strategy. Radiomics is emerging as a powerful diagnostic tool with potential applications in patient risk stratification. In this study, we aimed to compare the predictive value of conventional, semantic based and radiomic analyses to determine CNS WHO grade and early tumour relapse in intracranial meningiomas. We performed a single-centre retrospective analysis of intracranial meningiomas operated between 2007 and 2018. Recurrence within 5 years after Simpson Grade I-III resection was considered as early. Preoperative T1 CE MRI sequences were analysed conventionally by two radiologists. Additionally a semantic feature score based on systematic analysis of morphological characteristics was developed and a radiomic analysis were performed. For the radiomic model, tumour volume was extracted manually, 791 radiomic features were extracted. Eight feature selection algorithms and eight machine learning methods were used. Models were analysed using test and training datasets. In total, 226 patients were included. There were 21% CNS WHO grade 2 tumours, no CNS WHO grade 3 tumour, and 25 (11%) tumour recurrences were detected in total. In ROC analysis the best radiomic models demonstrated superior performance for determination of CNS WHO grade (AUC 0.930) and early recurrence (AUC 0.892) in comparison to the semantic feature score (AUC 0.74 and AUC 0.65) and conventional radiological analysis (AUC 0.65 and 0.54). The combination of human classifiers, semantic score and radiomic analysis did not markedly increase the model performance. Radiomic analysis is a promising tool for preoperative identification of aggressive and atypical intracranial meningiomas and could become a useful tool in the future.
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Affiliation(s)
- Darius Kalasauskas
- Department of Neurosurgery, University Medical Center, Johannes Gutenberg University, Langenbeckstr. 1, 55131, Mainz, Germany
| | - Michael Kosterhon
- Department of Neurosurgery, University Medical Center, Johannes Gutenberg University, Langenbeckstr. 1, 55131, Mainz, Germany.
| | - Elena Kurz
- Department of Neurosurgery, University Medical Center, Johannes Gutenberg University, Langenbeckstr. 1, 55131, Mainz, Germany
| | - Leon Schmidt
- Department of Neurosurgery, University Medical Center, Johannes Gutenberg University, Langenbeckstr. 1, 55131, Mainz, Germany
| | - Sebastian Altmann
- Department of Neuroradiology, University Medical Center, Johannes Gutenberg University, Langenbeckstr. 1, 55131, Mainz, Germany
| | - Nils F Grauhan
- Department of Neuroradiology, University Medical Center, Johannes Gutenberg University, Langenbeckstr. 1, 55131, Mainz, Germany
| | - Clemens Sommer
- Institute of Neuropathology, University Medical Center, Johannes Gutenberg University, Langenbeckstr. 1, 55131, Mainz, Germany
| | - Ahmed Othman
- Department of Neuroradiology, University Medical Center, Johannes Gutenberg University, Langenbeckstr. 1, 55131, Mainz, Germany
| | - Marc A Brockmann
- Department of Neuroradiology, University Medical Center, Johannes Gutenberg University, Langenbeckstr. 1, 55131, Mainz, Germany
| | - Florian Ringel
- Department of Neurosurgery, University Medical Center, Johannes Gutenberg University, Langenbeckstr. 1, 55131, Mainz, Germany
| | - Naureen Keric
- Department of Neurosurgery, University Medical Center, Johannes Gutenberg University, Langenbeckstr. 1, 55131, Mainz, Germany
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Gui Y, Zhang J. Research Progress of Artificial Intelligence in the Grading and Classification of Meningiomas. Acad Radiol 2024; 31:3346-3354. [PMID: 38413314 DOI: 10.1016/j.acra.2024.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Revised: 02/02/2024] [Accepted: 02/02/2024] [Indexed: 02/29/2024]
Abstract
A meningioma is a common primary central nervous system tumor. The histological features of meningiomas vary significantly depending on the grade and subtype, leading to differences in treatment and prognosis. Therefore, early diagnosis, grading, and typing of meningiomas are crucial for developing comprehensive and individualized diagnosis and treatment plans. The advancement of artificial intelligence (AI) in medical imaging, particularly radiomics and deep learning (DL), has contributed to the increasing research on meningioma grading and classification. These techniques are fast and accurate, involve fully automated learning, are non-invasive and objective, enable the efficient and non-invasive prediction of meningioma grades and classifications, and provide valuable assistance in clinical treatment and prognosis. This article provides a summary and analysis of the research progress in radiomics and DL for meningioma grading and classification. It also highlights the existing research findings, limitations, and suggestions for future improvement, aiming to facilitate the future application of AI in the diagnosis and treatment of meningioma.
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Affiliation(s)
- Yuan Gui
- Department of Radiology, the fifth affiliated hospital of zunyi medical university, zhufengdadao No.1439, Doumen District, Zhuhai, China
| | - Jing Zhang
- Department of Radiology, the fifth affiliated hospital of zunyi medical university, zhufengdadao No.1439, Doumen District, Zhuhai, China.
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Liang X, Tang K, Ke X, Jiang J, Li S, Xue C, Deng J, Liu X, Yan C, Gao M, Zhou J, Zhao L. Development of an MRI-Based Comprehensive Model Fusing Clinical, Radiomics and Deep Learning Models for Preoperative Histological Stratification in Intracranial Solitary Fibrous Tumor. J Magn Reson Imaging 2024; 60:523-533. [PMID: 37897302 DOI: 10.1002/jmri.29098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 10/09/2023] [Accepted: 10/10/2023] [Indexed: 10/30/2023] Open
Abstract
BACKGROUND Accurate preoperative histological stratification (HS) of intracranial solitary fibrous tumors (ISFTs) can help predict patient outcomes and develop personalized treatment plans. However, the role of a comprehensive model based on clinical, radiomics and deep learning (CRDL) features in preoperative HS of ISFT remains unclear. PURPOSE To investigate the feasibility of a CRDL model based on magnetic resonance imaging (MRI) in preoperative HS in ISFT. STUDY TYPE Retrospective. POPULATION Three hundred and ninety-eight patients from Beijing Tiantan Hospital, Capital Medical University (primary training cohort) and 49 patients from Lanzhou University Second Hospital (external validation cohort) with ISFT based on histopathological findings (237 World Health Organization [WHO] tumor grade 1 or 2, and 210 WHO tumor grade 3). FIELD STRENGTH/SEQUENCE 3.0 T/T1-weighted imaging (T1) by using spin echo sequence, T2-weighted imaging (T2) by using fast spin echo sequence, and T1-weighted contrast-enhanced imaging (T1C) by using two-dimensional fast spin echo sequence. ASSESSMENT Area under the receiver operating characteristic curve (AUC) was used to assess the performance of the CRDL model and a clinical model (CM) in preoperative HS in the external validation cohort. The decision curve analysis (DCA) was used to evaluate the clinical net benefit provided by the CRDL model. STATISTICAL TESTS Cohen's kappa, intra-/inter-class correlation coefficients (ICCs), Chi-square test, Fisher's exact test, Student's t-test, AUC, DCA, calibration curves, DeLong test. A P value <0.05 was considered statistically significant. RESULTS The CRDL model had significantly better discrimination ability than the CM (AUC [95% confidence interval, CI]: 0.895 [0.807-0.912] vs. 0.810 [0.745-0.874], respectively) in the external validation cohort. The CRDL model can provide a clinical net benefit for preoperative HS at a threshold probability >20%. DATA CONCLUSION The proposed CRDL model holds promise for preoperative HS in ISFT, which is important for predicting patient outcomes and developing personalized treatment plans. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Xiaohong Liang
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Kaiqiang Tang
- Department of Orthopedics, The Third Affiliated Hospital of Beijing University of Chinese Medicine, Beijing, China
| | - Xiaoai Ke
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
| | - Jian Jiang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
| | - Shenglin Li
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
| | - Caiqiang Xue
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
| | - Juan Deng
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
| | - Xianwang Liu
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
| | - Cheng Yan
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Mingzi Gao
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
| | - Liqin Zhao
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
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Trybula SJ, Youngblood MW, Karras CL, Murthy NK, Heimberger AB, Lukas RV, Sachdev S, Kalapurakal JA, Chandler JP, Brat DJ, Horbinski CM, Magill ST. The Evolving Classification of Meningiomas: Integration of Molecular Discoveries to Inform Patient Care. Cancers (Basel) 2024; 16:1753. [PMID: 38730704 PMCID: PMC11083836 DOI: 10.3390/cancers16091753] [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: 03/20/2024] [Revised: 04/25/2024] [Accepted: 04/26/2024] [Indexed: 05/13/2024] Open
Abstract
Meningioma classification and treatment have evolved over the past eight decades. Since Bailey, Cushing, and Eisenhart's description of meningiomas in the 1920s and 1930s, there have been continual advances in clinical stratification by histopathology, radiography and, most recently, molecular profiling, to improve prognostication and predict response to therapy. Precise and accurate classification is essential to optimizing management for patients with meningioma, which involves surveillance imaging, surgery, primary or adjuvant radiotherapy, and consideration for clinical trials. Currently, the World Health Organization (WHO) grade, extent of resection (EOR), and patient characteristics are used to guide management. While these have demonstrated reliability, a substantial number of seemingly benign lesions recur, suggesting opportunities for improvement of risk stratification. Furthermore, the role of adjuvant radiotherapy for grade 1 and 2 meningioma remains controversial. Over the last decade, numerous studies investigating the molecular drivers of clinical aggressiveness have been reported, with the identification of molecular markers that carry clinical implications as well as biomarkers of radiotherapy response. Here, we review the historical context of current practices, highlight recent molecular discoveries, and discuss the challenges of translating these findings into clinical practice.
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Affiliation(s)
- S. Joy Trybula
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Mark W. Youngblood
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Constantine L. Karras
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Nikhil K. Murthy
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Amy B. Heimberger
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Rimas V. Lukas
- Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Sean Sachdev
- Department of Radiation Oncology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - John A. Kalapurakal
- Department of Radiation Oncology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - James P. Chandler
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Daniel J. Brat
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Craig M. Horbinski
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Stephen T. Magill
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
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Ren L, Chen J, Deng J, Qing X, Cheng H, Wang D, Ji J, Chen H, Juratli TA, Wakimoto H, Gong Y, Hua L. The development of a combined clinico-radiomics model for predicting post-operative recurrence in atypical meningiomas: a multicenter study. J Neurooncol 2024; 166:59-71. [PMID: 38146046 DOI: 10.1007/s11060-023-04511-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 11/14/2023] [Indexed: 12/27/2023]
Abstract
PURPOSE Atypical meningiomas could manifest early recurrence after surgery and even adjuvant radiotherapy. We aimed to construct a clinico-radiomics model to predict post-operative recurrence of atypical meningiomas based on clinicopathological and radiomics features. MATERIALS AND METHODS The study cohort was comprised of 224 patients from two neurosurgical centers. 164 patients from center I were divided to the training cohort for model development and the testing cohort for internal validation. 60 patients from center II were used for external validation. Clinicopathological characteristics, radiological semantic, and radiomics features were collected. A radiomic signature was comprised of four radiomics features. A clinico-radiomics model combining the radiomics signature and clinical characteristics was constructed to predict the recurrence of atypical meningiomas. RESULTS 1920 radiomics features were extracted from the T1 Contrast and T2-FLAIR sequences of patients in center I. The radiomics signature was able to differentiate post-operative patients into low-risk and high-risk groups based on tumor recurrence (P < 0.001). A clinic-radiomics model was established by combining age, extent of resection, Ki-67 index, surgical history and the radiomics signature for recurrence prediction in atypical meningiomas. The model achieved a good prediction performance with the integrated AUC of 0.858 (0.802-0.915), 0.781 (0.649-0.912) and 0.840 (0.747-0.933) in the training, internal validation and external validation cohort, respectively. CONCLUSIONS The present study established a radiomics signature and a clinico-radiomics model with a favorable performance in predicting tumor recurrence for atypical meningiomas.
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Affiliation(s)
- Leihao Ren
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
- Institute of Neurosurgery, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Fudan University, Shanghai, China
| | - Jiawei Chen
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
- Institute of Neurosurgery, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Fudan University, Shanghai, China
| | - Jiaojiao Deng
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
- Institute of Neurosurgery, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Fudan University, Shanghai, China
| | - Xie Qing
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
- Institute of Neurosurgery, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Fudan University, Shanghai, China
| | - Haixia Cheng
- Department of Pathology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Daijun Wang
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
- Institute of Neurosurgery, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Fudan University, Shanghai, China
| | - Jing Ji
- Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Hong Chen
- Department of Pathology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Tareq A Juratli
- Department of Neurosurgery, University Hospital Carl Gustav Carus, Technische Universität Dresden, Fetscherstr. 74, 01307, Dresden, Germany
| | - Hiroaki Wakimoto
- Department of Neurosurgery, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA
| | - Ye Gong
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
- Institute of Neurosurgery, Fudan University, Shanghai, China.
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Fudan University, Shanghai, China.
- Department of Critical Care Medicine, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
| | - Lingyang Hua
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
- Institute of Neurosurgery, Fudan University, Shanghai, China.
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Fudan University, Shanghai, China.
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Han T, Liu X, Xu Z, Geng Y, Zhang B, Deng L, Jing M, Zhou J. Preoperative Prediction of Meningioma Subtype by Constructing a Clinical-Radiomics Model Nomogram Based on Magnetic Resonance Imaging. World Neurosurg 2024; 181:e203-e213. [PMID: 37813337 DOI: 10.1016/j.wneu.2023.09.119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 09/29/2023] [Indexed: 10/11/2023]
Abstract
OBJECTIVE We sought to investigate the value of a clinical-radiomics model based on magnetic resonance imaging in differentiating fibroblastic meningiomas from non-fibroblastic meningiomas. METHODS Clinical, imaging, and postoperative pathologic data of 423 patients (128 fibroblastic meningiomas and 295 non-fibroblastic meningiomas) were randomly categorized into training (n = 296) and validation (n = 127) groups at a 7:3 ratio. The Selectpercentile and LASSO were used to selected the highly correlated features from 3376 radiomics features. Different classifiers were used to train and verify the model. The receiver operating characteristic curves, accuracy (ACC), sensitivity (SEN), and specificity (SPE) were drawn to evaluate the performance. The optimal radiomics model was selected. Calibration curves and decision curve analysis were used to verify the clinical utility and consistency of the nomogram constructed from the radiomics features and clinical factors. RESULTS Thirteen radiomics features were selected from contrast-enhanced T1-weighted imaging and T2-weighted imaging after dimensionality reduction. The prediction performance of random forest radiomics model is slightly lower than that of the clinical-radiomics model. The area under the curve, SEN, SPE, and ACC of the clinical-radiomics model training set were 0.836 (95% confidence interval, 0.795-0.878), 0.922, 0.583, and 0.686, respectively. The area under the curve, SEN, SPE, and ACC of the validation set were 0.756 (95% confidence interval, 0.660-0.846), 0.816, 0.596, and 0.661, respectively. CONCLUSIONS The diagnostic efficacy of the clinical-radiomics model of fibroblastic meningioma and non-fibroblastic meningioma was better than that of the radiomics prediction model alone and can be used as a potential tool for clinical surgical planning and evaluation of patient prognosis.
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Affiliation(s)
- Tao Han
- Department of Radiology, Lanzhou University Second Hospital, 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 Gansu International Scientific and Technological Cooperation Base of Medical, Lanzhou, China
| | - Xianwang Liu
- Department of Radiology, Lanzhou University Second Hospital, 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 Gansu International Scientific and Technological Cooperation Base of Medical, Lanzhou, China
| | - Zhendong Xu
- Shukun (Beijing) Technology Co., Ltd., Beijing, China
| | - Yayuan Geng
- Shukun (Beijing) Technology Co., Ltd., Beijing, China
| | - Bin Zhang
- Department of Radiology, Lanzhou University Second Hospital, 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 Gansu International Scientific and Technological Cooperation Base of Medical, Lanzhou, China
| | - Liangna Deng
- Department of Radiology, Lanzhou University Second Hospital, 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 Gansu International Scientific and Technological Cooperation Base of Medical, Lanzhou, China
| | - Mengyuan Jing
- Department of Radiology, Lanzhou University Second Hospital, 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 Gansu International Scientific and Technological Cooperation Base of Medical, Lanzhou, China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Gansu International Scientific and Technological Cooperation Base of Medical, Lanzhou, China.
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Han T, Liu X, Long C, Xu Z, Geng Y, Zhang B, Deng L, Jing M, Zhou J. Prediction of meningioma grade by constructing a clinical radiomics model nomogram based on magnetic resonance imaging. Magn Reson Imaging 2023; 104:16-22. [PMID: 37734573 DOI: 10.1016/j.mri.2023.09.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 08/10/2023] [Accepted: 09/17/2023] [Indexed: 09/23/2023]
Abstract
PURPOSE To explore the clinical value of a clinical radiomics model nomogram based on magnetic resonance imaging (MRI) for preoperative meningioma grading. MATERIALS AND METHODS We collected retrospectively 544 patients with pathological diagnosis of meningiomas were categorized into training (n = 380) and validation (n = 164) groups at the ratio of 7∶ 3. There were 3,376 radiomics features extracted from T2WI and T1C by shukun technology platform after manual segmentation using an independent blind method by two radiologists. The Selectpercentile and Lasso are used to filter the most strongly correlated features. Random forest (RF) radiomics model and clinical radiomics model nomogram were constructed respectively. The calibration, discrimination, and clinical validity were evaluated by using the calibration curve and decision analysis curve (DCA). RESULTS The RF radiomics model based on T1C and T2WI was the most effective to predict meningioma grade before surgery among the six different classifiers. The predictive ability of clinical radiomics model was slightly higher than that of RF model alone. The AUC, SEN, SPE, and ACC of the training set were 0.949, 0.976, 0.785, and 0.826, and the AUC, SEN, SPE, and ACC of the validation set were 0.838, 0.829, 0.783, and 0.793, respectively. The calibration curve and Hosmer-Lemeshow test showed the predictive probability of the fusion model was similar to the actual differentiated LGM and HGM. The analysis of the decision curve showed that the clinical radiomics model could obtain the best clinical net profit. CONCLUSIONS The clinical radiomics model nomogram based on T1C and T2WI has high accuracy and sensitivity for predicting meningioma grade.
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Affiliation(s)
- Tao Han
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China
| | - Xianwang Liu
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China
| | - Changyou Long
- Image Center of Affiliated Hospital of Qinghai University, Xining, China
| | - Zhendong Xu
- Shukun (Beijing) Technology Co., Ltd., Jinhui Building, Qiyang Road, 100102 Beijing, China
| | - Yayuan Geng
- Shukun (Beijing) Technology Co., Ltd., Jinhui Building, Qiyang Road, 100102 Beijing, China
| | - Bin Zhang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China
| | - Liangna Deng
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China
| | - Mengyuan Jing
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China.
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Maniar KM, Lassarén P, Rana A, Yao Y, Tewarie IA, Gerstl JVE, Recio Blanco CM, Power LH, Mammi M, Mattie H, Smith TR, Mekary RA. Traditional Machine Learning Methods versus Deep Learning for Meningioma Classification, Grading, Outcome Prediction, and Segmentation: A Systematic Review and Meta-Analysis. World Neurosurg 2023; 179:e119-e134. [PMID: 37574189 DOI: 10.1016/j.wneu.2023.08.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 08/06/2023] [Indexed: 08/15/2023]
Abstract
BACKGROUND Meningiomas are common intracranial tumors. Machine learning (ML) algorithms are emerging to improve accuracy in 4 primary domains: classification, grading, outcome prediction, and segmentation. Such algorithms include both traditional approaches that rely on hand-crafted features and deep learning (DL) techniques that utilize automatic feature extraction. The aim of this study was to evaluate the performance of published traditional ML versus DL algorithms in classification, grading, outcome prediction, and segmentation of meningiomas. METHODS A systematic review and meta-analysis were conducted. Major databases were searched through September 2021 for publications evaluating traditional ML versus DL models on meningioma management. Performance measures including pooled sensitivity, specificity, F1-score, area under the receiver-operating characteristic curve, positive and negative likelihood ratios (LR+, LR-) along with their respective 95% confidence intervals (95% CIs) were derived using random-effects models. RESULTS Five hundred thirty-four records were screened, and 43 articles were included, regarding classification (3 articles), grading (29), outcome prediction (7), and segmentation (6) of meningiomas. Of the 29 studies that reported on grading, 10 could be meta-analyzed with 2 DL models (sensitivity 0.89, 95% CI: 0.74-0.96; specificity 0.91, 95% CI: 0.45-0.99; LR+ 10.1, 95% CI: 1.33-137; LR- 0.12, 95% CI: 0.04-0.59) and 8 traditional ML (sensitivity 0.74, 95% CI: 0.62-0.83; specificity 0.93, 95% CI: 0.79-0.98; LR+ 10.5, 95% CI: 2.91-39.5; and LR- 0.28, 95% CI: 0.17-0.49). The insufficient performance metrics reported precluded further statistical analysis of other performance metrics. CONCLUSIONS ML on meningiomas is mostly carried out with traditional methods. For meningioma grading, traditional ML methods generally had a higher LR+, while DL models a lower LR-.
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Affiliation(s)
- Krish M Maniar
- Department of Neurosurgery, Computational Neurosciences Outcomes Center (CNOC), Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts, United States
| | - Philipp Lassarén
- Department of Neurosurgery, Computational Neurosciences Outcomes Center (CNOC), Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts, United States; Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Aakanksha Rana
- Department of Neurosurgery, Computational Neurosciences Outcomes Center (CNOC), Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts, United States; McGovern Institute for Brain Research, Massachusetts Institute of Technology, Boston, Massachusetts, United States
| | - Yuxin Yao
- Department of Pharmaceutical Business and Administrative Sciences, School of Pharmacy, Massachusetts College of Pharmacy and Health Sciences University, Boston, Massachusetts, United States
| | - Ishaan A Tewarie
- Department of Neurosurgery, Computational Neurosciences Outcomes Center (CNOC), Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts, United States; Department of Neurosurgery, Haaglanden Medical Center, The Hague, The Netherlands; Faculty of Medicine, Erasmus University Rotterdam/Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Jakob V E Gerstl
- Department of Neurosurgery, Computational Neurosciences Outcomes Center (CNOC), Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts, United States
| | - Camila M Recio Blanco
- Department of Neurosurgery, Computational Neurosciences Outcomes Center (CNOC), Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts, United States; Northeast National University, Corrientes, Argentina; Prisma Salud, Puerto San Julian, Santa Cruz, Argentina
| | - Liam H Power
- Department of Neurosurgery, Computational Neurosciences Outcomes Center (CNOC), Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts, United States; School of Medicine, Tufts University, Boston, Massachusetts, United States
| | - Marco Mammi
- Neurosurgery Unit, S. Croce e Carle Hospital, Cuneo, Italy
| | - Heather Mattie
- Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, Massachusetts, United States
| | - Timothy R Smith
- Department of Neurosurgery, Computational Neurosciences Outcomes Center (CNOC), Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts, United States; Department of Neurosurgery, Brigham and Women's Hospital, Harvard University, Boston, Massachusetts, United States
| | - Rania A Mekary
- Department of Neurosurgery, Computational Neurosciences Outcomes Center (CNOC), Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts, United States; Department of Pharmaceutical Business and Administrative Sciences, School of Pharmacy, Massachusetts College of Pharmacy and Health Sciences University, Boston, Massachusetts, United States.
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9
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Wang L, Cao Y, Zhang G, Sun D, Zhou W, Li W, Zhou J, Chen K, Zhang J. A radiomics model enables prediction venous sinus invasion in meningioma. Ann Clin Transl Neurol 2023; 10:1284-1295. [PMID: 37408500 PMCID: PMC10424646 DOI: 10.1002/acn3.51797] [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: 03/13/2023] [Revised: 04/19/2023] [Accepted: 05/10/2023] [Indexed: 07/07/2023] Open
Abstract
OBJECTIVE Preoperative prediction of meningioma venous sinus invasion would facilitate the selection of surgical approaches and predicting the prognosis. To predict venous sinus invasion in meningiomas, we used radiomic signatures to construct a model based on preoperative contrast-enhanced T1-weighted (T1C) and T2-weighted (T2) magnetic resonance imaging. METHODS In total, 599 patients with pathologically confirmed meningioma were retrospectively enrolled. For each patient enrolled in this study, 1595 radiomic signatures were extracted from T1C and T2 image sequences. Pearson correlation analysis and recursive feature elimination were used to select the most relevant signatures extracted from different image sequences, and logistic regression algorithms were used to build a radiomic model for risk prediction of meningioma sinus invasion. Furthermore, a nomogram was built by incorporating clinical characteristics and radiomic signatures, and a decision curve analysis was used to evaluate the clinical utility of the nomogram. RESULTS Twenty radiomic signatures that were significantly related to venous sinus invasion were screened from 3190 radiomic signatures. Venous sinus invasion was associated with tumor position, and the clinicoradiomic model that incorporated the above characteristics (20 radiomic signatures and tumor position) had the best discriminating ability. The areas under the curve for the training and validation cohorts were 0.857 (95% confidence interval [CI], 0.824-0.890) and 0.824 (95% CI, 0.752-0.8976), respectively. INTERPRETATION The clinicoradiomic model had good predictive performance for venous sinus invasion in meningioma, which can aid in devising surgical strategies and predicting prognosis.
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Affiliation(s)
- Limei Wang
- Department of RadiologyThe Fifth Affiliated Hospital of Zunyi Medical UniversityZhuhaiChina
| | - Yuntai Cao
- Department of RadiologyAffiliated Hospital of Qinghai UniversityXiningChina
| | - Guojin Zhang
- Department of Radiology, Sichuan Provincial People's HospitalUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Dandan Sun
- Department of RadiologyThe Fifth Affiliated Hospital of Zunyi Medical UniversityZhuhaiChina
| | - Wusheng Zhou
- Department of RadiologyThe Fifth Affiliated Hospital of Zunyi Medical UniversityZhuhaiChina
| | - Wenyi Li
- Department of RadiologyThe Fifth Affiliated Hospital of Zunyi Medical UniversityZhuhaiChina
| | - Junlin Zhou
- Department of RadiologyLanzhou University Second HospitalLanzhouChina
- Key Laboratory of Medical Imaging of Gansu ProvinceLanzhouChina
| | - Kuntao Chen
- Department of RadiologyThe Fifth Affiliated Hospital of Zunyi Medical UniversityZhuhaiChina
| | - Jing Zhang
- Department of RadiologyThe Fifth Affiliated Hospital of Zunyi Medical UniversityZhuhaiChina
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10
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Perillo T, de Giorgi M, Papace UM, Serino A, Cuocolo R, Manto A. Current role of machine learning and radiogenomics in precision neuro-oncology. EXPLORATION OF TARGETED ANTI-TUMOR THERAPY 2023; 4:545-555. [PMID: 37720347 PMCID: PMC10501892 DOI: 10.37349/etat.2023.00151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 04/20/2023] [Indexed: 09/19/2023] Open
Abstract
In the past few years, artificial intelligence (AI) has been increasingly used to create tools that can enhance workflow in medicine. In particular, neuro-oncology has benefited from the use of AI and especially machine learning (ML) and radiogenomics, which are subfields of AI. ML can be used to develop algorithms that dynamically learn from available medical data in order to automatically do specific tasks. On the other hand, radiogenomics can identify relationships between tumor genetics and imaging features, thus possibly giving new insights into the pathophysiology of tumors. Therefore, ML and radiogenomics could help treatment tailoring, which is crucial in personalized neuro-oncology. The aim of this review is to illustrate current and possible future applications of ML and radiomics in neuro-oncology.
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Affiliation(s)
- Teresa Perillo
- Department of Neuroradiology, “Umberto I” Hospital, 84014 Norcera Inferiore, Italy
| | - Marco de Giorgi
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80138 Naples, Italy
| | - Umberto Maria Papace
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80138 Naples, Italy
| | - Antonietta Serino
- Department of Neuroradiology, “Umberto I” Hospital, 84014 Norcera Inferiore, Italy
| | - Renato Cuocolo
- Department of Medicine, Surgery, and Dentistry, University of Salerno, 84084 Fisciano, Italy
| | - Andrea Manto
- Department of Neuroradiology, “Umberto I” Hospital, 84014 Norcera Inferiore, Italy
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11
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Koechli C, Zwahlen DR, Schucht P, Windisch P. Radiomics and machine learning for predicting the consistency of benign tumors of the central nervous system: A systematic review. Eur J Radiol 2023; 164:110866. [PMID: 37207398 DOI: 10.1016/j.ejrad.2023.110866] [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: 03/14/2023] [Revised: 04/28/2023] [Accepted: 05/03/2023] [Indexed: 05/21/2023]
Abstract
PURPOSE Predicting the consistency of benign central nervous system (CNS) tumors prior to surgery helps to improve surgical outcomes. This review summarizes and analyzes the literature on using radiomics and/or machine learning (ML) for consistency prediction. METHOD The Medical Literature Analysis and Retrieval System Online (MEDLINE) database was screened for studies published in English from January 1st 2000. Data was extracted according to the PRISMA guidelines and quality of the studies was assessed in compliance with the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). RESULTS Eight publications were included focusing on pituitary macroadenomas (n = 5), pituitary adenomas (n = 1), and meningiomas (n = 2) using a retrospective (n = 6), prospective (n = 1), and unknown (n = 1) study design with a total of 763 patients for the consistency prediction. The studies reported an area under the curve (AUC) of 0.71-0.99 for their respective best performing model regarding the consistency prediction. Of all studies, four articles validated their models internally whereas none validated their models externally. Two articles stated making data available on request with the remaining publications lacking information with regard to data availability. CONCLUSIONS The research on consistency prediction of CNS tumors is still at an early stage regarding the use of radiomics and different ML techniques. Best-practice procedures regarding radiomics and ML need to be followed more rigorously to facilitate the comparison between publications and, accordingly, the possible implementation into clinical practice in the future.
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Affiliation(s)
- Carole Koechli
- Department of Radiation Oncology, Kantonsspital Winterthur, 8401 Winterthur, Switzerland; Universitätsklinik für Neurochirurgie, Bern University Hospital, 3010 Bern, Switzerland.
| | - Daniel R Zwahlen
- Department of Radiation Oncology, Kantonsspital Winterthur, 8401 Winterthur, Switzerland
| | - Philippe Schucht
- Universitätsklinik für Neurochirurgie, Bern University Hospital, 3010 Bern, Switzerland
| | - Paul Windisch
- Department of Radiation Oncology, Kantonsspital Winterthur, 8401 Winterthur, Switzerland
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12
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Antunes A, Winter R. Parasagittal Meningiomas: Prognostic Factors for Recurrence. Adv Tech Stand Neurosurg 2023; 48:277-289. [PMID: 37770688 DOI: 10.1007/978-3-031-36785-4_10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/30/2023]
Abstract
The term parasagittal meningioma applies to those tumors that are associated with the superior sagittal sinus (SSS), originating from the dura mater in close relation to the parasagittal wall or angle, with no intervening brain tissue, possibly extending to the dura of the convexity and/or falx cerebri.(Cushing et al., Meningiomas: their classification, regional behaviour, life history, and surgeical and results. Hafner, 1938) They make up about 20-30% of all meningiomas. There is a vast literature correlating the Simpson grade of resection with later recurrence. Frequent involvement of the superior sagittal sinus (SSS) by these tumors means that the optimal treatment recommended in the literature-complete resection, including of the dural base-is one of the most challenging.
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Affiliation(s)
- Apio Antunes
- Neurosurgical Department, University Hospital, Porto Alegre, Brazil
- Porto Alegre Medical School, UFRGS, Porto Alegre, Brazil
| | - Rafael Winter
- University Hospital, Porto Alegre, Brazil.
- Neurosurgery Department, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil.
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13
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Predicting Meningioma Resection Status: Use of Deep Learning. Acad Radiol 2022:S1076-6332(22)00518-9. [DOI: 10.1016/j.acra.2022.10.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 09/20/2022] [Accepted: 10/03/2022] [Indexed: 11/24/2022]
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14
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Sun S, Ren L, Miao Z, Hua L, Wang D, Deng J, Chen J, Liu N, Gong Y. Application of MRI-Based Radiomics in Preoperative Prediction of NF2 Alteration in Intracranial Meningiomas. Front Oncol 2022; 12:879528. [PMID: 36267986 PMCID: PMC9578175 DOI: 10.3389/fonc.2022.879528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Accepted: 06/06/2022] [Indexed: 11/13/2022] Open
Abstract
PurposeThis study aimed to investigate the feasibility of predicting NF2 mutation status based on the MR radiomic analysis in patients with intracranial meningioma.MethodsThis retrospective study included 105 patients with meningiomas, including 60 NF2-mutant samples and 45 wild-type samples. Radiomic features were extracted from magnetic resonance imaging scans, including T1-weighted, T2-weighted, and contrast T1-weighted images. Student’s t-test and LASSO regression were performed to select the radiomic features. All patients were randomly divided into training and validation cohorts in a 7:3 ratio. Five linear models (RF, SVM, LR, KNN, and xgboost) were trained to predict the NF2 mutational status. Receiver operating characteristic curve and precision-recall analyses were used to evaluate the model performance. Student’s t-tests were then used to compare the posterior probabilities of NF2 mut/loss prediction for patients with different NF2 statuses.ResultsNine features had nonzero coefficients in the LASSO regression model. No significant differences was observed in the clinical features. Nine features showed significant differences in patients with different NF2 statuses. Among all machine learning algorithms, SVM showed the best performance. The area under curve and accuracy of the predictive model were 0.85; the F1-score of the precision-recall curve was 0.80. The model risk was assessed by plotting calibration curves. The p-value for the H-L goodness of fit test was 0.411 (p> 0.05), which indicated that the difference between the obtained model and the perfect model was statistically insignificant. The AUC of our model in external validation was 0.83.ConclusionA combination of radiomic analysis and machine learning showed potential clinical utility in the prediction of preoperative NF2 status. These findings could aid in developing customized neurosurgery plans and meningioma management strategies before postoperative pathology.
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Affiliation(s)
- Shuchen Sun
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Neurosurgery, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China
| | - Leihao Ren
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Neurosurgery, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China
| | - Zong Miao
- Department of Neurosurgery, Changhai Hospital, Naval Medical University (Second Military Medical University), Shanghai, China
| | - Lingyang Hua
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Neurosurgery, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China
| | - Daijun Wang
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Neurosurgery, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China
| | - Jiaojiao Deng
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Neurosurgery, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China
| | - Jiawei Chen
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Neurosurgery, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China
| | - Ning Liu
- Department of Neurosurgery, Changhai Hospital, Naval Medical University (Second Military Medical University), Shanghai, China
| | - Ye Gong
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Neurosurgery, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China
- Department of Critical Care Medicine, Huashan Hospital, Fudan University, Shanghai, China
- *Correspondence: Ye Gong,
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15
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Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review. Cancers (Basel) 2022; 14:cancers14112676. [PMID: 35681655 PMCID: PMC9179850 DOI: 10.3390/cancers14112676] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 05/18/2022] [Accepted: 05/26/2022] [Indexed: 11/20/2022] Open
Abstract
Simple Summary Machine learning in radiology of the central nervous system has seen many interesting publications in the past few years. Since the focus has largely been on malignant tumors such as brain metastases and high-grade gliomas, we conducted a systematic review on benign tumors to summarize what has been published and where there might be gaps in the research. We found several studies that report good results, but the descriptions of methodologies could be improved to enable better comparisons and assessment of biases. Abstract Objectives: To summarize the available literature on using machine learning (ML) for the detection and segmentation of benign tumors of the central nervous system (CNS) and to assess the adherence of published ML/diagnostic accuracy studies to best practice. Methods: The MEDLINE database was searched for the use of ML in patients with any benign tumor of the CNS, and the records were screened according to PRISMA guidelines. Results: Eleven retrospective studies focusing on meningioma (n = 4), vestibular schwannoma (n = 4), pituitary adenoma (n = 2) and spinal schwannoma (n = 1) were included. The majority of studies attempted segmentation. Links to repositories containing code were provided in two manuscripts, and no manuscripts shared imaging data. Only one study used an external test set, which raises the question as to whether some of the good performances that have been reported were caused by overfitting and may not generalize to data from other institutions. Conclusions: Using ML for detecting and segmenting benign brain tumors is still in its infancy. Stronger adherence to ML best practices could facilitate easier comparisons between studies and contribute to the development of models that are more likely to one day be used in clinical practice.
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Brunasso L, Ferini G, Bonosi L, Costanzo R, Musso S, Benigno UE, Gerardi RM, Giammalva GR, Paolini F, Umana GE, Graziano F, Scalia G, Sturiale CL, Di Bonaventura R, Iacopino DG, Maugeri R. A Spotlight on the Role of Radiomics and Machine-Learning Applications in the Management of Intracranial Meningiomas: A New Perspective in Neuro-Oncology: A Review. Life (Basel) 2022; 12:life12040586. [PMID: 35455077 PMCID: PMC9026541 DOI: 10.3390/life12040586] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 04/05/2022] [Accepted: 04/06/2022] [Indexed: 12/12/2022] Open
Abstract
Background: In recent decades, the application of machine learning technologies to medical imaging has opened up new perspectives in neuro-oncology, in the so-called radiomics field. Radiomics offer new insight into glioma, aiding in clinical decision-making and patients’ prognosis evaluation. Although meningiomas represent the most common primary CNS tumor and the majority of them are benign and slow-growing tumors, a minor part of them show a more aggressive behavior with an increased proliferation rate and a tendency to recur. Therefore, their treatment may represent a challenge. Methods: According to PRISMA guidelines, a systematic literature review was performed. We included selected articles (meta-analysis, review, retrospective study, and case–control study) concerning the application of radiomics method in the preoperative diagnostic and prognostic algorithm, and planning for intracranial meningiomas. We also analyzed the contribution of radiomics in differentiating meningiomas from other CNS tumors with similar radiological features. Results: In the first research stage, 273 papers were identified. After a careful screening according to inclusion/exclusion criteria, 39 articles were included in this systematic review. Conclusions: Several preoperative features have been identified to increase preoperative intracranial meningioma assessment for guiding decision-making processes. The development of valid and reliable non-invasive diagnostic and prognostic modalities could have a significant clinical impact on meningioma treatment.
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Affiliation(s)
- Lara Brunasso
- Neurosurgical Clinic AOUP “Paolo Giaccone”, Post Graduate Residency Program in Neurologic Surgery, Department of Biomedicine Neurosciences and Advanced Diagnostics, School of Medicine, University of Palermo, 90127 Palermo, Italy; (L.B.); (R.C.); (S.M.); (U.E.B.); (R.M.G.); (G.R.G.); (F.P.); (D.G.I.); (R.M.)
- Correspondence:
| | - Gianluca Ferini
- Department of Radiation Oncology, REM Radioterapia SRL, 95125 Catania, Italy;
| | - Lapo Bonosi
- Neurosurgical Clinic AOUP “Paolo Giaccone”, Post Graduate Residency Program in Neurologic Surgery, Department of Biomedicine Neurosciences and Advanced Diagnostics, School of Medicine, University of Palermo, 90127 Palermo, Italy; (L.B.); (R.C.); (S.M.); (U.E.B.); (R.M.G.); (G.R.G.); (F.P.); (D.G.I.); (R.M.)
| | - Roberta Costanzo
- Neurosurgical Clinic AOUP “Paolo Giaccone”, Post Graduate Residency Program in Neurologic Surgery, Department of Biomedicine Neurosciences and Advanced Diagnostics, School of Medicine, University of Palermo, 90127 Palermo, Italy; (L.B.); (R.C.); (S.M.); (U.E.B.); (R.M.G.); (G.R.G.); (F.P.); (D.G.I.); (R.M.)
| | - Sofia Musso
- Neurosurgical Clinic AOUP “Paolo Giaccone”, Post Graduate Residency Program in Neurologic Surgery, Department of Biomedicine Neurosciences and Advanced Diagnostics, School of Medicine, University of Palermo, 90127 Palermo, Italy; (L.B.); (R.C.); (S.M.); (U.E.B.); (R.M.G.); (G.R.G.); (F.P.); (D.G.I.); (R.M.)
| | - Umberto E. Benigno
- Neurosurgical Clinic AOUP “Paolo Giaccone”, Post Graduate Residency Program in Neurologic Surgery, Department of Biomedicine Neurosciences and Advanced Diagnostics, School of Medicine, University of Palermo, 90127 Palermo, Italy; (L.B.); (R.C.); (S.M.); (U.E.B.); (R.M.G.); (G.R.G.); (F.P.); (D.G.I.); (R.M.)
| | - Rosa M. Gerardi
- Neurosurgical Clinic AOUP “Paolo Giaccone”, Post Graduate Residency Program in Neurologic Surgery, Department of Biomedicine Neurosciences and Advanced Diagnostics, School of Medicine, University of Palermo, 90127 Palermo, Italy; (L.B.); (R.C.); (S.M.); (U.E.B.); (R.M.G.); (G.R.G.); (F.P.); (D.G.I.); (R.M.)
| | - Giuseppe R. Giammalva
- Neurosurgical Clinic AOUP “Paolo Giaccone”, Post Graduate Residency Program in Neurologic Surgery, Department of Biomedicine Neurosciences and Advanced Diagnostics, School of Medicine, University of Palermo, 90127 Palermo, Italy; (L.B.); (R.C.); (S.M.); (U.E.B.); (R.M.G.); (G.R.G.); (F.P.); (D.G.I.); (R.M.)
| | - Federica Paolini
- Neurosurgical Clinic AOUP “Paolo Giaccone”, Post Graduate Residency Program in Neurologic Surgery, Department of Biomedicine Neurosciences and Advanced Diagnostics, School of Medicine, University of Palermo, 90127 Palermo, Italy; (L.B.); (R.C.); (S.M.); (U.E.B.); (R.M.G.); (G.R.G.); (F.P.); (D.G.I.); (R.M.)
| | - Giuseppe E. Umana
- Gamma Knife Center, Trauma Center, Department of Neurosurgery, Cannizzaro Hospital, 95100 Catania, Italy;
| | - Francesca Graziano
- Unit of Neurosurgery, Garibaldi Hospital, 95124 Catania, Italy; (F.G.); (G.S.)
| | - Gianluca Scalia
- Unit of Neurosurgery, Garibaldi Hospital, 95124 Catania, Italy; (F.G.); (G.S.)
| | - Carmelo L. Sturiale
- Division of Neurosurgery, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, 00100 Rome, Italy; (C.L.S.); (R.D.B.)
| | - Rina Di Bonaventura
- Division of Neurosurgery, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, 00100 Rome, Italy; (C.L.S.); (R.D.B.)
| | - Domenico G. Iacopino
- Neurosurgical Clinic AOUP “Paolo Giaccone”, Post Graduate Residency Program in Neurologic Surgery, Department of Biomedicine Neurosciences and Advanced Diagnostics, School of Medicine, University of Palermo, 90127 Palermo, Italy; (L.B.); (R.C.); (S.M.); (U.E.B.); (R.M.G.); (G.R.G.); (F.P.); (D.G.I.); (R.M.)
| | - Rosario Maugeri
- Neurosurgical Clinic AOUP “Paolo Giaccone”, Post Graduate Residency Program in Neurologic Surgery, Department of Biomedicine Neurosciences and Advanced Diagnostics, School of Medicine, University of Palermo, 90127 Palermo, Italy; (L.B.); (R.C.); (S.M.); (U.E.B.); (R.M.G.); (G.R.G.); (F.P.); (D.G.I.); (R.M.)
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Afridi M, Jain A, Aboian M, Payabvash S. Brain Tumor Imaging: Applications of Artificial Intelligence. Semin Ultrasound CT MR 2022; 43:153-169. [PMID: 35339256 PMCID: PMC8961005 DOI: 10.1053/j.sult.2022.02.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Artificial intelligence has become a popular field of research with goals of integrating it into the clinical decision-making process. A growing number of predictive models are being employed utilizing machine learning that includes quantitative, computer-extracted imaging features known as radiomic features, and deep learning systems. This is especially true in brain-tumor imaging where artificial intelligence has been proposed to characterize, differentiate, and prognostication. We reviewed current literature regarding the potential uses of machine learning-based, and deep learning-based artificial intelligence in neuro-oncology as it pertains to brain tumor molecular classification, differentiation, and treatment response. While there is promising evidence supporting the use of artificial intelligence in neuro-oncology, there are still more investigations needed on a larger, multicenter scale along with a streamlined and standardized image processing workflow prior to its introduction in routine clinical decision-making protocol.
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Affiliation(s)
- Muhammad Afridi
- School of Osteopathic Medicine, Rowan University, Stratford, NJ
| | - Abhi Jain
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT
| | - Mariam Aboian
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT
| | - Seyedmehdi Payabvash
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT.
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18
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Kalasauskas D, Kosterhon M, Keric N, Korczynski O, Kronfeld A, Ringel F, Othman A, Brockmann MA. Beyond Glioma: The Utility of Radiomic Analysis for Non-Glial Intracranial Tumors. Cancers (Basel) 2022; 14:cancers14030836. [PMID: 35159103 PMCID: PMC8834271 DOI: 10.3390/cancers14030836] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 01/30/2022] [Accepted: 02/04/2022] [Indexed: 02/05/2023] Open
Abstract
Simple Summary Tumor qualities, such as growth rate, firmness, and intrusion into healthy tissue, can be very important for operation planning and further treatment. Radiomics is a promising new method that allows the determination of some of these qualities on images performed before surgery. In this article, we provide a review of the use of radiomics in various tumors of the central nervous system, such as metastases, lymphoma, meningioma, medulloblastoma, and pituitary tumors. Abstract The field of radiomics is rapidly expanding and gaining a valuable role in neuro-oncology. The possibilities related to the use of radiomic analysis, such as distinguishing types of malignancies, predicting tumor grade, determining the presence of particular molecular markers, consistency, therapy response, and prognosis, can considerably influence decision-making in medicine in the near future. Even though the main focus of radiomic analyses has been on glial CNS tumors, studies on other intracranial tumors have shown encouraging results. Therefore, as the main focus of this review, we performed an analysis of publications on PubMed and Web of Science databases, focusing on radiomics in CNS metastases, lymphoma, meningioma, medulloblastoma, and pituitary tumors.
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Affiliation(s)
- Darius Kalasauskas
- Department of Neurosurgery, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (D.K.); (M.K.); (N.K.); (F.R.)
| | - Michael Kosterhon
- Department of Neurosurgery, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (D.K.); (M.K.); (N.K.); (F.R.)
| | - Naureen Keric
- Department of Neurosurgery, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (D.K.); (M.K.); (N.K.); (F.R.)
| | - Oliver Korczynski
- Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (O.K.); (A.K.); (A.O.)
| | - Andrea Kronfeld
- Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (O.K.); (A.K.); (A.O.)
| | - Florian Ringel
- Department of Neurosurgery, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (D.K.); (M.K.); (N.K.); (F.R.)
| | - Ahmed Othman
- Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (O.K.); (A.K.); (A.O.)
| | - Marc A. Brockmann
- Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (O.K.); (A.K.); (A.O.)
- Correspondence:
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19
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Qin C, Hu W, Wang X, Ma X. Application of Artificial Intelligence in Diagnosis of Craniopharyngioma. Front Neurol 2022; 12:752119. [PMID: 35069406 PMCID: PMC8770750 DOI: 10.3389/fneur.2021.752119] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 11/12/2021] [Indexed: 12/24/2022] Open
Abstract
Craniopharyngioma is a congenital brain tumor with clinical characteristics of hypothalamic-pituitary dysfunction, increased intracranial pressure, and visual field disorder, among other injuries. Its clinical diagnosis mainly depends on radiological examinations (such as Computed Tomography, Magnetic Resonance Imaging). However, assessing numerous radiological images manually is a challenging task, and the experience of doctors has a great influence on the diagnosis result. The development of artificial intelligence has brought about a great transformation in the clinical diagnosis of craniopharyngioma. This study reviewed the application of artificial intelligence technology in the clinical diagnosis of craniopharyngioma from the aspects of differential classification, prediction of tissue invasion and gene mutation, prognosis prediction, and so on. Based on the reviews, the technical route of intelligent diagnosis based on the traditional machine learning model and deep learning model were further proposed. Additionally, in terms of the limitations and possibilities of the development of artificial intelligence in craniopharyngioma diagnosis, this study discussed the attentions required in future research, including few-shot learning, imbalanced data set, semi-supervised models, and multi-omics fusion.
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Affiliation(s)
- Caijie Qin
- Institute of Information Engineering, Sanming University, Sanming, China
| | - Wenxing Hu
- University of New South Wales, Sydney, NSW, Australia
| | - Xinsheng Wang
- School of Information Science and Engineering, Harbin Institute of Technology at Weihai, Weihai, China
| | - Xibo Ma
- CBSR & NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
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20
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Zhang J, Zhang G, Cao Y, Ren J, Zhao Z, Han T, Chen K, Zhou J. A Magnetic Resonance Imaging-Based Radiomic Model for the Noninvasive Preoperative Differentiation Between Transitional and Atypical Meningiomas. Front Oncol 2022; 12:811767. [PMID: 35127543 PMCID: PMC8815760 DOI: 10.3389/fonc.2022.811767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 01/04/2022] [Indexed: 11/14/2022] Open
Abstract
Preoperative distinction between transitional meningioma and atypical meningioma would aid the selection of appropriate surgical techniques, as well as the prognosis prediction. Here, we aimed to differentiate between these two tumors using radiomic signatures based on preoperative, contrast-enhanced T1-weighted and T2-weighted magnetic resonance imaging. A total of 141 transitional meningioma and 101 atypical meningioma cases between January 2014 and December 2018 with a histopathologically confirmed diagnosis were retrospectively reviewed. All patients underwent magnetic resonance imaging before surgery. For each patient, 1227 radiomic features were extracted from contrast-enhanced T1-weighted and T2-weighted images each. Least absolute shrinkage and selection operator regression analysis was performed to select the most informative features of different modalities. Subsequently, stepwise multivariate logistic regression was chosen to further select strongly correlated features and build classification models that can distinguish transitional from atypical meningioma. The diagnostic abilities were evaluated by receiver operating characteristic analysis. Furthermore, a nomogram was built by incorporating clinical characteristics, radiological features, and radiomic signatures, and decision curve analysis was used to validate the clinical usefulness of the nomogram. Sex, tumor shape, brain invasion, and four radiomic features differed significantly between transitional meningioma and atypical meningioma. The clinicoradiomic model derived by fusing the above features resulted in the best discrimination ability, with areas under the curves of 0.809 (95% confidence interval, 0.743-0.874) and 0.795 (95% confidence interval, 0.692-0.899) and sensitivity values of 74.0% and 71.4% in the training and validation cohorts, respectively. The clinicoradiomic model demonstrated good performance for the differentiation between transitional and atypical meningioma. It is a quantitative tool that can potentially aid the selection of surgical techniques and the prognosis prediction and can thus be applied in patients with these two meningioma subtypes.
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Affiliation(s)
- Jing Zhang
- Department of Radiology, The Fifth Affiliated Hospital of Zunyi Medical University, Zhuhai, China
| | - Guojin Zhang
- Department of Radiology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Yuntai Cao
- Department of Radiology, Affiliated Hospital of Qinghai University, Xining, China
| | - Jialiang Ren
- Department of Pharmaceuticals Diagnosis, GE Healthcare, Beijing, China
| | - Zhiyong Zhao
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
| | - Tao Han
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
| | - Kuntao Chen
- Department of Radiology, The Fifth Affiliated Hospital of Zunyi Medical University, Zhuhai, China
- *Correspondence: Junlin Zhou, ; Kuntao Chen,
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- *Correspondence: Junlin Zhou, ; Kuntao Chen,
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21
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Nomogram based on MRI can preoperatively predict brain invasion in meningioma. Neurosurg Rev 2022; 45:3729-3737. [PMID: 36180806 PMCID: PMC9663361 DOI: 10.1007/s10143-022-01872-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 09/03/2022] [Accepted: 09/17/2022] [Indexed: 02/02/2023]
Abstract
Predicting brain invasion preoperatively should help to guide surgical decision-making and aid the prediction of meningioma grading and prognosis. However, only a few imaging features have been identified to aid prediction. This study aimed to develop and validate an MRI-based nomogram to predict brain invasion by meningioma. In this retrospective study, 658 patients were examined via routine MRI before undergoing surgery and were diagnosed with meningioma by histopathology. Least absolute shrinkage and selection operator (LASSO) regularization was used to determine the optimal combination of clinical characteristics and MRI features for predicting brain invasion by meningiomas. Logistic regression and receiver operating characteristic (ROC) curve analyses were used to determine the discriminatory ability. Furthermore, a nomogram was constructed using the optimal MRI features, and decision curve analysis was used to validate the clinical usefulness of the nomogram. Eighty-one patients with brain invasion and 577 patients without invasion were enrolled. According to LASSO regularization, tumour shape, tumour boundary, peritumoral oedema, and maximum diameter were independent predictors of brain invasion. The model showed good discriminatory ability for predicting brain invasion in meningiomas, with an AUC of 0.905 (95% CI, 0.871-0.940) vs 0.898 (95% CI, 0.849-0.947) and sensitivity of 93.0% vs 92.6% in the training vs validation cohorts. Our predictive model based on MRI features showed good performance and high sensitivity for predicting the risk of brain invasion in meningiomas and can be applied in the clinical setting.
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22
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Abdel Razek AAK, Alksas A, Shehata M, AbdelKhalek A, Abdel Baky K, El-Baz A, Helmy E. Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging. Insights Imaging 2021; 12:152. [PMID: 34676470 PMCID: PMC8531173 DOI: 10.1186/s13244-021-01102-6] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 09/26/2021] [Indexed: 12/15/2022] Open
Abstract
This article is a comprehensive review of the basic background, technique, and clinical applications of artificial intelligence (AI) and radiomics in the field of neuro-oncology. A variety of AI and radiomics utilized conventional and advanced techniques to differentiate brain tumors from non-neoplastic lesions such as inflammatory and demyelinating brain lesions. It is used in the diagnosis of gliomas and discrimination of gliomas from lymphomas and metastasis. Also, semiautomated and automated tumor segmentation has been developed for radiotherapy planning and follow-up. It has a role in the grading, prediction of treatment response, and prognosis of gliomas. Radiogenomics allowed the connection of the imaging phenotype of the tumor to its molecular environment. In addition, AI is applied for the assessment of extra-axial brain tumors and pediatric tumors with high performance in tumor detection, classification, and stratification of patient's prognoses.
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Affiliation(s)
| | - Ahmed Alksas
- Biomaging Lab, Department of Bioengineering, University of Louisville, Louisville, KY, 40292, USA
| | - Mohamed Shehata
- Biomaging Lab, Department of Bioengineering, University of Louisville, Louisville, KY, 40292, USA
| | - Amr AbdelKhalek
- Internship at Mansoura University Hospital, Mansoura Faculty of Medicine, Mansoura, Egypt
| | - Khaled Abdel Baky
- Department of Diagnostic Radiology, Faculty of Medicine, Port Said University, Port Said, Egypt
| | - Ayman El-Baz
- Biomaging Lab, Department of Bioengineering, University of Louisville, Louisville, KY, 40292, USA
| | - Eman Helmy
- Department of Diagnostic Radiology, Faculty of Medicine, Mansoura University, Elgomheryia Street, Mansoura, 3512, Egypt.
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23
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Khanna O, Fathi Kazerooni A, Farrell CJ, Baldassari MP, Alexander TD, Karsy M, Greenberger BA, Garcia JA, Sako C, Evans JJ, Judy KD, Andrews DW, Flanders AE, Sharan AD, Dicker AP, Shi W, Davatzikos C. Machine Learning Using Multiparametric Magnetic Resonance Imaging Radiomic Feature Analysis to Predict Ki-67 in World Health Organization Grade I Meningiomas. Neurosurgery 2021; 89:928-936. [PMID: 34460921 DOI: 10.1093/neuros/nyab307] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 06/09/2021] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Although World Health Organization (WHO) grade I meningiomas are considered "benign" tumors, an elevated Ki-67 is one crucial factor that has been shown to influence tumor behavior and clinical outcomes. The ability to preoperatively discern Ki-67 would confer the ability to guide surgical strategy. OBJECTIVE In this study, we develop a machine learning (ML) algorithm using radiomic feature analysis to predict Ki-67 in WHO grade I meningiomas. METHODS A retrospective analysis was performed for a cohort of 306 patients who underwent surgical resection of WHO grade I meningiomas. Preoperative magnetic resonance imaging was used to perform radiomic feature extraction followed by ML modeling using least absolute shrinkage and selection operator wrapped with support vector machine through nested cross-validation on a discovery cohort (n = 230), to stratify tumors based on Ki-67 <5% and ≥5%. The final model was independently tested on a replication cohort (n = 76). RESULTS An area under the receiver operating curve (AUC) of 0.84 (95% CI: 0.78-0.90) with a sensitivity of 84.1% and specificity of 73.3% was achieved in the discovery cohort. When this model was applied to the replication cohort, a similar high performance was achieved, with an AUC of 0.83 (95% CI: 0.73-0.94), sensitivity and specificity of 82.6% and 85.5%, respectively. The model demonstrated similar efficacy when applied to skull base and nonskull base tumors. CONCLUSION Our proposed radiomic feature analysis can be used to stratify WHO grade I meningiomas based on Ki-67 with excellent accuracy and can be applied to skull base and nonskull base tumors with similar performance achieved.
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Affiliation(s)
- Omaditya Khanna
- Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, USA
| | - Anahita Fathi Kazerooni
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Christopher J Farrell
- Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, USA
| | - Michael P Baldassari
- Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, USA
| | - Tyler D Alexander
- Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, USA
| | - Michael Karsy
- Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, USA
| | - Benjamin A Greenberger
- Department of Radiation Oncology, Sidney Kimmel Medical College & Cancer Center, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Jose A Garcia
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Chiharu Sako
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - James J Evans
- Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, USA
| | - Kevin D Judy
- Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, USA
| | - David W Andrews
- Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, USA
| | - Adam E Flanders
- Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, USA
| | - Ashwini D Sharan
- Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, USA
| | - Adam P Dicker
- Department of Radiation Oncology, Sidney Kimmel Medical College & Cancer Center, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Wenyin Shi
- Department of Radiation Oncology, Sidney Kimmel Medical College & Cancer Center, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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24
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Maggio I, Franceschi E, Tosoni A, Nunno VD, Gatto L, Lodi R, Brandes AA. Meningioma: not always a benign tumor. A review of advances in the treatment of meningiomas. CNS Oncol 2021; 10:CNS72. [PMID: 34015955 PMCID: PMC8162186 DOI: 10.2217/cns-2021-0003] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 03/30/2021] [Indexed: 11/21/2022] Open
Abstract
Meningiomas are the most common primary intracranial tumors. The majority of meningiomas are benign, but they can present different grades of dedifferentiation from grade I to grade III (anaplastic/malignant) that are associated with different outcomes. Radiological surveillance is a valid option for low-grade asymptomatic meningiomas. In other cases, the treatment is usually surgical, aimed at achieving a complete resection. The use of adjuvant radiotherapy is the gold standard for grade III, is debated for grade II and is not generally indicated for radically resected grade I meningiomas. The use of systemic treatments is not standardized. Here we report a review of the literature on the clinical, radiological and molecular characteristics of meningiomas, available treatment strategies and ongoing clinical trials.
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Affiliation(s)
- Ilaria Maggio
- Medical Oncology Department, Azienda USL, Via Altura 3, 40139, Bologna, Italy
| | - Enrico Franceschi
- Medical Oncology Department, Azienda USL, Via Altura 3, 40139, Bologna, Italy
| | - Alicia Tosoni
- Medical Oncology Department, Azienda USL, Via Altura 3, 40139, Bologna, Italy
| | - Vincenzo Di Nunno
- Medical Oncology Department, Azienda USL, Via Altura 3, 40139, Bologna, Italy
| | - Lidia Gatto
- Medical Oncology Department, Azienda USL, Via Altura 3, 40139, Bologna, Italy
| | - Raffaele Lodi
- IRCSS Istituto di Scienze Neurologiche di Bologna, Bologna 40139, Italy
| | - Alba A Brandes
- Medical Oncology Department, Azienda USL, Via Altura 3, 40139, Bologna, Italy
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25
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Value of MRI Radiomics Based on Enhanced T1WI Images in Prediction of Meningiomas Grade. Acad Radiol 2021; 28:687-693. [PMID: 32418785 DOI: 10.1016/j.acra.2020.03.034] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Revised: 03/12/2020] [Accepted: 03/21/2020] [Indexed: 01/03/2023]
Abstract
OBJECTIVE Different grades of meningiomas require different treatment strategies and have a different prognosis; thus, the noninvasive classification of meningiomas before surgery is of great importance. The purpose of this study was to explore the application value of magnetic resonance imaging (MRI) radiomics based on enhanced-T1-weighted (T1WI) images in the prediction of meningiomas grade. MATERIALS AND METHODS A total of 98 patients with meningiomas who were confirmed by surgical pathology and underwent preoperative routine MRI between January 2017 and December 2019 were analyzed. There were 82 cases of low-grade meningiomas (WHO grade I) and 16 cases of high-grade meningiomas (7 cases of WHO grade II and 9 cases of WHO grade III). These patients were randomly divided into a training group and test group according to 7:3 ratio. The lesions were manually delineated using ITK-SNAP software, and radiomics analysis were performed using the Analysis Kit (AK) software. A total of 396 tumor texture features were extracted. Subsequently, the LASSO algorithm was used to reduce the feature dimensions. Next, a prediction model was constructed using the Logistic Regression method and receiver operator characteristic was used to evaluate the prediction performance of the model. RESULTS A radiomics prediction model was constructed based on the selected nine characteristic parameters, which performed well in predicting the meningiomas grade. The accuracy rates in the training group and the test group were respectively 94.3% and 92.9%, the sensitivities were respectively 94.8%, and 91.7%, the specificities were respectively 91.7% and 100%, and the area under the curve values were respectively 0.958 and 0.948. CONCLUSION The MRI radiomics method based on enhanced-T1WI images has a good predictive effect on the classification of meningiomas and can provide a basis for planning clinical treatment protocols.
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26
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Won SY, Park YW, Ahn SS, Moon JH, Kim EH, Kang SG, Chang JH, Kim SH, Lee SK. Quality assessment of meningioma radiomics studies: Bridging the gap between exploratory research and clinical applications. Eur J Radiol 2021; 138:109673. [PMID: 33774441 DOI: 10.1016/j.ejrad.2021.109673] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 03/16/2021] [Accepted: 03/18/2021] [Indexed: 02/08/2023]
Abstract
PURPOSE To evaluate the quality of radiomics studies on meningiomas, using a radiomics quality score (RQS), Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD), and the Image Biomarker Standardization Initiative (IBSI). METHODS PubMed MEDLINE and Embase were searched to identify radiomics studies on meningiomas. Of 138 identified articles, 25 relevant original research articles were included. Studies were scored according to the RQS, TRIPOD guidelines, and items in IBSI. RESULTS Only four studies (16 %) performed external validation. The mean RQS was 5.6 out of 36 (15.4 %), and the basic adherence rate was 26.8 %. The adherence rate was low for stating biological correlation (4%), conducting calibration statistics (12 %), multiple segmentation (16 %), and stating potential clinical utility (16 %). None of the studies conducted a test‒retest or phantom study, stated a comparison to a 'gold standard', conducted prospective studies or cost-effectivity analysis, or opened code and data to the public, resulting in low RQS. The overall adherence rate for TRIPOD was 54.1 %, with low scores for reporting the title (4%), abstract (0%), blind assessment of the outcome (8%), and explaining the sample size (0%). According to IBSI items, only 6 (24 %), 6 (24 %), and 3 (12 %) studies performed N4 bias-field correction, isovoxel resampling, and grey-level discretization, respectively. No study performed skull stripping. CONCLUSION The quality of radiomics studies for meningioma is insufficient. Acknowledgement of RQS, TRIPOD, and IBSI reporting guidelines may improve the quality of meningioma radiomics studies and enable their clinical application.
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Affiliation(s)
- So Yeon Won
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - 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, Republic of 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, Republic of Korea
| | - Ju Hyung Moon
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Eui Hyun Kim
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Seok-Gu Kang
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jong Hee Chang
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Se Hoon Kim
- Department of Pathology, Yonsei University College of Medicine, Seoul, Republic of 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, Republic of Korea
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27
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Meningioma MRI radiomics and machine learning: systematic review, quality score assessment, and meta-analysis. Neuroradiology 2021; 63:1293-1304. [PMID: 33649882 PMCID: PMC8295153 DOI: 10.1007/s00234-021-02668-0] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 02/03/2021] [Indexed: 02/07/2023]
Abstract
Purpose To systematically review and evaluate the methodological quality of studies using radiomics for diagnostic and predictive purposes in patients with intracranial meningioma. To perform a meta-analysis of machine learning studies for the prediction of intracranial meningioma grading from pre-operative brain MRI. Methods Articles published from the year 2000 on radiomics and machine learning applications in brain imaging of meningioma patients were included. Their methodological quality was assessed by three readers with the radiomics quality score, using the intra-class correlation coefficient (ICC) to evaluate inter-reader reproducibility. A meta-analysis of machine learning studies for the preoperative evaluation of meningioma grading was performed and their risk of bias was assessed with the Quality Assessment of Diagnostic Accuracy Studies tool. Results In all, 23 studies were included in the systematic review, 8 of which were suitable for the meta-analysis. Total (possible range, −8 to 36) and percentage radiomics quality scores were respectively 6.96 ± 4.86 and 19 ± 13% with a moderate to good inter-reader reproducibility (ICC = 0.75, 95% confidence intervals, 95%CI = 0.54–0.88). The meta-analysis showed an overall AUC of 0.88 (95%CI = 0.84–0.93) with a standard error of 0.02. Conclusions Machine learning and radiomics have been proposed for multiple applications in the imaging of meningiomas, with promising results for preoperative lesion grading. However, future studies with adequate standardization and higher methodological quality are required prior to their introduction in clinical practice. Supplementary Information The online version contains supplementary material available at 10.1007/s00234-021-02668-0.
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28
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Gu H, Zhang X, di Russo P, Zhao X, Xu T. The Current State of Radiomics for Meningiomas: Promises and Challenges. Front Oncol 2020; 10:567736. [PMID: 33194649 PMCID: PMC7653049 DOI: 10.3389/fonc.2020.567736] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2020] [Accepted: 09/28/2020] [Indexed: 12/18/2022] Open
Abstract
Meningiomas are the most common primary tumors of the central nervous system. Given the fact that the majority of meningiomas are benign, the preoperative risk stratification and treatment strategy decision-making highly rely on the conventional subjective radiologic evaluation. However, this traditional diagnostic and treatment modality may not be effective in patients with aggressive-growing tumors or symptomatic patients with potential risk of recurrence after surgical resection or radiotherapy, as this passive “wait and see” strategy could miss the optimal opportunity of intervention. Radiomics, a new rising discipline, translates high-dimensional image information into abundant mathematical data by multiple computational algorithms. It provides an objective and quantitative approach to interpret the imaging data, rather than the subjective and qualitative interpretation from relatively limited human visual observation. In fact, the enormous amount of information generated by radiomics analyses provides radiological to histopathological tumor information, which are visually imperceptible, and offers technological basis to its applications amid diagnosis, treatment, and prognosis. Here, we review the latest advancements of radiomics and its applications in the prediction of the pathological grade, pathological subtype, recurrence possibility, and differential diagnosis of meningiomas, and the potential and challenges in general clinical applications. In this review, we highlight the generalization of shared radiomic features among different studies and compare different performances of popular algorithms. At last, we discuss several possible aspects of challenges and future directions in the development of radiomic applications in meningiomas.
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Affiliation(s)
- Hao Gu
- Department of Neurosurgery, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Xu Zhang
- Department of Neurosurgery, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Paolo di Russo
- Department of Neurosurgery, I.R.C.C.S. Neuromed, Pozzilli, Italy
| | - Xiaochun Zhao
- Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
| | - Tao Xu
- Department of Neurosurgery, Changzheng Hospital, Naval Medical University, Shanghai, China
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29
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Identification of High-Risk Atypical Meningiomas According to Semantic and Radiomic Features. Cancers (Basel) 2020; 12:cancers12102942. [PMID: 33053798 PMCID: PMC7599676 DOI: 10.3390/cancers12102942] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2020] [Revised: 10/01/2020] [Accepted: 10/10/2020] [Indexed: 01/16/2023] Open
Abstract
Simple Summary More than 50% of atypical meningiomas recur within 5 years. Identification of high-risk tumors might be helpful for pre-operative planning. The aim of our retrospective study was to assess the value of radiomic and semantic magnetic resonance imaging (MRI) characteristics for the prediction of tumor relapse. Our findings suggest that the semantic characteristic of cystic component and the radiomic feature of cluster prominence are associated with tumor recurrence. A combination of semantic and radiomic characteristics is a promising tool for identifying patients with high-risk atypical meningiomas. Abstract Up to 60% of atypical meningiomas (World Health Organization (WHO) grade II) reoccur within 5 years after resection. However, no clear radiological criteria exist to identify tumors with higher risk of relapse. In this study, we aimed to assess the association of certain radiomic and semantic features of atypical meningiomas in MRI with tumor recurrence. We identified patients operated on primary atypical meningiomas in our department from 2007 to 2017. An analysis of 13 quantitatively defined radiomic and 11 qualitatively defined semantic criteria was performed based on preoperative MRI scans. Imaging characteristics were assessed along with clinical and survival data. The analysis included 76 patients (59% women, mean age 59 years). Complete tumor resection was achieved in 65 (86%) cases, and tumor relapse occurred in 17 (22%) cases. Mean follow-up time was 41.6 (range 3–168) months. Cystic component was significantly associated with tumor recurrence (odds ratio (OR) 21.7, 95% confidence interval (CI) 3.8–124.5) and shorter progression-free survival (33.2 vs. 80.7 months, p < 0.001), whereas radiomic characteristics had no predictive value in univariate analysis. However, multivariate analysis demonstrated significant predictive value of high cluster prominence (hazard ratio (HR) 5.89 (1.03–33.73) and cystic component (HR 20.21 (2.46–166.02)) for tumor recurrence. The combination of radiomic and semantic features might be an effective tool for identifying patients with high-risk atypical meningiomas. The presence of a cystic component in these tumors is associated with a high risk of tumor recurrence.
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Radiomic features of magnetic resonance images as novel preoperative predictive factors of bone invasion in meningiomas. Eur J Radiol 2020; 132:109287. [PMID: 32980725 DOI: 10.1016/j.ejrad.2020.109287] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 09/11/2020] [Accepted: 09/13/2020] [Indexed: 01/11/2023]
Abstract
PURPOSE Bone invasion in meningiomas is a prognostic determinant, and a priori knowledge may alter surgical techniques. Here, we aim to predict bone invasion in meningiomas using radiomic signatures based on preoperative, contrast-enhanced T1-weighted (T1C) and T2-weighted (T2) magnetic resonance imaging (MRI). METHODS In this retrospective study, 490 patients diagnosed with meningiomas, including WHO grade I (448cases), grade II (38cases), and grade III (4cases), were enrolled and 213 out of 490 cases (43.5 %) had bone invasion. The patients were randomly divided into training (n = 343) and test (n = 147) datasets at a 7:3 ratio. For each patient, 1227 radiomic features were extracted from T1C and T2, respectively. Spearman's correlation and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to select the most informative features. Subsequently, a 5-fold cross-validation was used to compare the performance of different classification algorithms, and logistic regression was chosen to predict the risk of bone invasion. RESULTS Eight radiomic features were selected from T1C and T2 respectively, and three models were built using radiomic features. The radiomic models derived from T1C alone or a combination of T1C and T2 had the best performance in predicting risk of bone invasion, with areas under the curve in the training dataset of 0.714 [95 % CI, 0.660-0.768] and 0.722 [95 % CI, 0.668-0.776] and in the test datasets of 0.715 [95 % CI, 0.632-0.798] and 0.713 [95 % CI, 0.628-0.798], respectively. CONCLUSIONS The radiomic model may aid clinicians with preoperative prediction of bone invasion by meningiomas, which can help in predicting prognosis and devising surgical strategies.
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Radiomics-Based Prediction of Overall Survival in Lung Cancer Using Different Volumes-Of-Interest. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10186425] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Lung cancer accounts for the largest amount of deaths worldwide with respect to the other oncological pathologies. To guarantee the most effective cure to patients for such aggressive tumours, radiomics is increasing as a novel and promising research field that aims at extracting knowledge from data in terms of quantitative measures that are computed from diagnostic images, with prognostic and predictive ends. This knowledge could be used to optimize current treatments and to maximize their efficacy. To this end, we hereby study the use of such quantitative biomarkers computed from CT images of patients affected by Non-Small Cell Lung Cancer to predict Overall Survival. The main contributions of this work are two: first, we consider different volumes of interest for the same patient to find out whether the volume surrounding the visible lesions can provide useful information; second, we introduce 3D Local Binary Patterns, which are texture measures scarcely explored in radiomics. As further validation, we show that the proposed signature outperforms not only the features automatically computed by a deep learning-based approach, but also another signature at the state-of-the-art using other handcrafted features.
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Forghani R. Precision Digital Oncology: Emerging Role of Radiomics-based Biomarkers and Artificial Intelligence for Advanced Imaging and Characterization of Brain Tumors. Radiol Imaging Cancer 2020; 2:e190047. [PMID: 33778721 DOI: 10.1148/rycan.2020190047] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Revised: 02/21/2020] [Accepted: 03/04/2020] [Indexed: 12/22/2022]
Abstract
Advances in computerized image analysis and the use of artificial intelligence-based approaches for image-based analysis and construction of prediction algorithms represent a new era for noninvasive biomarker discovery. In recent literature, it has become apparent that radiologic images can serve as mineable databases that contain large amounts of quantitative features with potential clinical significance. Extraction and analysis of these quantitative features is commonly referred to as texture or radiomic analysis. Numerous studies have demonstrated applications for texture and radiomic characterization methods for assessing brain tumors to improve noninvasive predictions of tumor histologic characteristics, molecular profile, distinction of treatment-related changes, and prediction of patient survival. In this review, the current use and future potential of texture or radiomic-based approaches with machine learning for brain tumor image analysis and prediction algorithm construction will be discussed. This technology has the potential to advance the value of diagnostic imaging by extracting currently unused information on medical scans that enables more precise, personalized therapy; however, significant barriers must be overcome if this technology is to be successfully implemented on a wide scale for routine use in the clinical setting. Keywords: Adults and Pediatrics, Brain/Brain Stem, CNS, Computer Aided Diagnosis (CAD), Computer Applications-General (Informatics), Image Postprocessing, Informatics, Neural Networks, Neuro-Oncology, Oncology, Treatment Effects, Tumor Response Supplemental material is available for this article. © RSNA, 2020.
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Affiliation(s)
- Reza Forghani
- Department of Radiology, McGill University Health Centre, 1001 Decarie Blvd, Room C02.5821, Montreal, QC, Canada H4A 3J1; Augmented Intelligence & Precision Health Laboratory (AIPHL), Research Institute of the McGill University Health Centre, Montreal, Canada; Segal Cancer Centre and Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Canada; Gerald Bronfman Department of Oncology, McGill University, Montreal, Canada; and Department of Otolaryngology-Head and Neck Surgery, McGill University, Montreal, Canada
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Zhang J, Yao K, Liu P, Liu Z, Han T, Zhao Z, Cao Y, Zhang G, Zhang J, Tian J, Zhou J. A radiomics model for preoperative prediction of brain invasion in meningioma non-invasively based on MRI: A multicentre study. EBioMedicine 2020; 58:102933. [PMID: 32739863 PMCID: PMC7393568 DOI: 10.1016/j.ebiom.2020.102933] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2020] [Revised: 07/16/2020] [Accepted: 07/16/2020] [Indexed: 12/15/2022] Open
Abstract
Background Prediction of brain invasion pre-operatively rather than postoperatively would contribute to the selection of surgical techniques, predicting meningioma grading and prognosis. Here, we aimed to predict the risk of brain invasion in meningioma pre-operatively using a nomogram by incorporating radiomic and clinical features. Methods In this case-control study, 1728 patients from Beijing Tiantan Hospital (training cohort: n = 1070) and Lanzhou University Second Hospital (external validation cohort: n = 658) were diagnosed with meningiomas by histopathology. Radiomic features were extracted from the T1-weighted post-contrast and T2-weighted magnetic resonance imaging. The least absolute shrinkage and selection operator was used to select the most informative features of different modalities. The support vector machine algorithm was used to predict the risk of brain invasion. Furthermore, a nomogram was constructed by incorporating radiomics signature and clinical risk factors, and decision curve analysis was used to validate the clinical usefulness of the nomogram. Findings Sixteen features were significantly correlated with brain invasion. The clinicoradiomic model derived from the fusing MRI sequences and sex resulted in the best discrimination ability for risk prediction of brain invasion, with areas under the curves (AUCs) of 0•857 (95% CI, 0•831–0•887) and 0•819 (95% CI, 0•775–0•863) and sensitivities of 72•8% and 90•1% in the training and validation cohorts, respectively. Interpretation Our clinicoradiomic model showed good performance and high sensitivity for risk prediction of brain invasion in meningioma, and can be applied in patients with meningiomas. Funding This work was supported by the 10.13039/501100001809National Natural Science Foundation of China (81772006, 81922040); the 10.13039/501100004739Youth Innovation Promotion Association CAS (grant numbers 2019136); special fund project for doctoral training program of 10.13039/100012899Lanzhou University Second Hospital (grant numbers YJS-BD-33).
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Affiliation(s)
- Jing Zhang
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, , China
| | - Kuan Yao
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, , China; School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Panpan Liu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Nansihuan Xilu 119, Fengtai District, Beijing, China; Department of Neurosurgery, The Municipal Hospital of Weihai, China
| | - Zhenyu Liu
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, , China; CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100080, China
| | - Tao Han
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
| | - Zhiyong Zhao
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China
| | - Yuntai Cao
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
| | - Guojin Zhang
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
| | - Junting Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Nansihuan Xilu 119, Fengtai District, Beijing, China.
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, , China; CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100080, China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, 100191, China; Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China; Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology, Beijing, 100191, China.
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China.
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Zhang Y, Chen JH, Chen TY, Lim SW, Wu TC, Kuo YT, Ko CC, Su MY. Radiomics approach for prediction of recurrence in skull base meningiomas. Neuroradiology 2019; 61:1355-1364. [PMID: 31324948 DOI: 10.1007/s00234-019-02259-0] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Accepted: 07/04/2019] [Indexed: 12/15/2022]
Abstract
PURPOSE A subset of skull base meningiomas (SBM) may show early progression/recurrence (P/R) as a result of incomplete resection. The purpose of this study is the implementation of MR radiomics to predict P/R in SBM. METHODS From October 2006 to December 2017, 60 patients diagnosed with pathologically confirmed SBM (WHO grade I, 56; grade II, 3; grade III, 1) were included in this study. Preoperative MRI including T2WI, diffusion-weighted imaging (DWI), and contrast-enhanced T1WI were analyzed. On each imaging modality, 13 histogram parameters and 20 textural gray level co-occurrence matrix (GLCM) features were extracted. Random forest algorithms were utilized to evaluate the importance of these parameters, and the most significant three parameters were selected to build a decision tree for prediction of P/R in SBM. Furthermore, ADC values obtained from manually placed ROI in tumor were also used to predict P/R in SBM for comparison. RESULTS Gross-total resection (Simpson Grades I-III) was performed in 33 (33/60, 55%) patients, and 27 patients received subtotal resection. Twenty-one patients had P/R (21/60, 35%) after a postoperative follow-up period of at least 12 months. The three most significant parameters included in the final radiomics model were T1 max probability, T1 cluster shade, and ADC correlation. In the radiomics model, the accuracy for prediction of P/R was 90%; by comparison, the accuracy was 83% using ADC values measured from manually placed tumor ROI. CONCLUSIONS The results show that the radiomics approach in preoperative MRI offer objective and valuable clinical information for treatment planning in SBM.
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Affiliation(s)
- Yang Zhang
- Department of Radiological Sciences, University of California, Irvine, CA, USA
| | - Jeon-Hor Chen
- Department of Radiological Sciences, University of California, Irvine, CA, USA.,Department of Radiology, E-DA Hospital, E-DA Cancer Hospital, I-Shou University, Kaohsiung, Taiwan
| | - Tai-Yuan Chen
- Department of Medical Imaging, Chi-Mei Medical Center, Tainan, Taiwan.,Graduate Institute of Medical Sciences, Chang Jung Christian University, Tainan, Taiwan
| | - Sher-Wei Lim
- Department of Neurosurgery, Chi-Mei Medical Center, Chiali, Tainan, Taiwan.,Department of Nursing, Min-Hwei College of Health Care, Management, Tainan, Taiwan
| | - Te-Chang Wu
- Department of Medical Imaging, Chi-Mei Medical Center, Tainan, Taiwan.,Graduate Institute of Medical Sciences, Chang Jung Christian University, Tainan, Taiwan.,Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan
| | - Yu-Ting Kuo
- Department of Medical Imaging, Chi-Mei Medical Center, Tainan, Taiwan.,Department of of Medical Imaging, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan
| | - Ching-Chung Ko
- Department of Medical Imaging, Chi-Mei Medical Center, Tainan, Taiwan. .,Center of General Education, Chia Nan University of Pharmacy and Science, Tainan, Taiwan.
| | - Min-Ying Su
- Department of Radiological Sciences, University of California, Irvine, CA, USA
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