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Zhong LW, Chen KS, Yang HB, Liu SD, Zong ZT, Zhang XQ. Exploring machine learning applications in Meningioma Research (2004-2023). Heliyon 2024; 10:e32596. [PMID: 38975185 PMCID: PMC11225743 DOI: 10.1016/j.heliyon.2024.e32596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 04/19/2024] [Accepted: 06/05/2024] [Indexed: 07/09/2024] Open
Abstract
Objective This study aims to examine the trends in machine learning application to meningiomas between 2004 and 2023. Methods Publication data were extracted from the Science Citation Index Expanded (SCI-E) within the Web of Science Core Collection (WOSCC). Using CiteSpace 6.2.R6, a comprehensive analysis of publications, authors, cited authors, countries, institutions, cited journals, references, and keywords was conducted on December 1, 2023. Results The analysis included a total of 342 articles. Prior to 2007, no publications existed in this field, and the number remained modest until 2017. A significant increase occurred in publications from 2018 onwards. The majority of the top 10 authors hailed from Germany and China, with the USA also exerting substantial international influence, particularly in academic institutions. Journals from the IEEE series contributed significantly to the publications. "Deep learning," "brain tumor," and "classification" emerged as the primary keywords of focus among researchers. The developmental pattern in this field primarily involved a combination of interdisciplinary integration and the refinement of major disciplinary branches. Conclusion Machine learning has demonstrated significant value in predicting early meningiomas and tailoring treatment plans. Key research focuses involve optimizing detection indicators and selecting superior machine learning algorithms. Future efforts should aim to develop high-performance algorithms to drive further innovation in this field.
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Affiliation(s)
- Li-wei Zhong
- Jiujiang Traditional Chinese Medicine Hospital, Jiujiang, Jiangxi, China
| | - Kun-shan Chen
- The Second Affiliated Hospital of Jiujiang University, Jiujiang, Jiangxi, China
| | - Hua-biao Yang
- Jiujiang Traditional Chinese Medicine Hospital, Jiujiang, Jiangxi, China
| | - Shi-dan Liu
- Jiujiang Traditional Chinese Medicine Hospital, Jiujiang, Jiangxi, China
| | - Zhi-tao Zong
- Jiujiang Traditional Chinese Medicine Hospital, Jiujiang, Jiangxi, China
| | - Xue-qin Zhang
- Jiujiang Traditional Chinese Medicine Hospital, Jiujiang, Jiangxi, China
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Zeng Q, Tian Z, Dong F, Shi F, Xu P, Zhang J, Ling C, Guo Z. Multi-parameter MRI radiomic features may contribute to predict progression-free survival in patients with WHO grade II meningiomas. Front Oncol 2024; 14:1246730. [PMID: 39007097 PMCID: PMC11239420 DOI: 10.3389/fonc.2024.1246730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Accepted: 06/10/2024] [Indexed: 07/16/2024] Open
Abstract
Aim This study aims to investigate the potential value of radiomic features from multi-parameter MRI in predicting progression-free survival (PFS) of patients with WHO grade II meningiomas. Methods Kaplan-Meier survival curves were used for survival analysis of clinical features. A total of 851 radiomic features were extracted based on tumor region segmentation from each sequence, and Max-Relevance and Min-Redundancy (mRMR) algorithm was applied to filter and select radiomic features. Bagged AdaBoost, Stochastic Gradient Boosting, Random Forest, and Neural Network models were built based on selected features. Discriminative abilities of models were evaluated using receiver operating characteristics (ROC) and area under the curve (AUC). Results Our study enrolled 164 patients with WHO grade II meningiomas. Female gender (p=0.023), gross total resection (GTR) (p<0.001), age <68 years old (p=0.023), and edema index <2.3 (p=0.006) are protective factors for PFS in these patients. Both the Bagged AdaBoost model and the Neural Network model achieved the best performance on test set with an AUC of 0.927 (95% CI, Bagged AdaBoost: 0.834-1.000; Neural Network: 0.836-1.000). Conclusion The Bagged AdaBoost model and the Neural Network model based on radiomic features demonstrated decent predictive ability for PFS in patients with WHO grade II meningiomas who underwent operation using preoperative multi-parameter MR images, thus bringing benefit for patient prognosis prediction in clinical practice. Our study emphasizes the importance of utilizing advanced imaging techniques such as radiomics to improve personalized treatment strategies for meningiomas by providing more accurate prognostic information that can guide clinicians toward better decision-making processes when treating their patients' conditions effectively while minimizing risks associated with unnecessary interventions or treatments that may not be beneficial.
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Affiliation(s)
- Qiang Zeng
- Department of Neurosurgery, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Department of Neurosurgery, Clinical Research Center for Neurological Diseases of Zhejiang Province, Hangzhou, China
| | - Zhongyu Tian
- Department of Neurosurgery, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Department of Neurosurgery, Clinical Research Center for Neurological Diseases of Zhejiang Province, Hangzhou, China
| | - Fei Dong
- Department of Radiology, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Feina Shi
- Department of Neurology, Sir Runrun Shaw Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Penglei Xu
- Department of Neurosurgery, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Department of Neurosurgery, Clinical Research Center for Neurological Diseases of Zhejiang Province, Hangzhou, China
| | - Jianmin Zhang
- Department of Neurosurgery, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Department of Neurosurgery, Clinical Research Center for Neurological Diseases of Zhejiang Province, Hangzhou, China
| | - Chenhan Ling
- Department of Neurosurgery, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Department of Neurosurgery, Clinical Research Center for Neurological Diseases of Zhejiang Province, Hangzhou, China
| | - Zhige Guo
- Department of Neurosurgery, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Department of Neurosurgery, Clinical Research Center for Neurological Diseases of Zhejiang Province, Hangzhou, China
- Department of Neurosurgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, China
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Chen J, Xue Y, Ren L, Lv K, Du P, Cheng H, Sun S, Hua L, Xie Q, Wu R, Gong Y. Predicting meningioma grades and pathologic marker expression via deep learning. Eur Radiol 2024; 34:2997-3008. [PMID: 37853176 DOI: 10.1007/s00330-023-10258-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 07/05/2023] [Accepted: 07/15/2023] [Indexed: 10/20/2023]
Abstract
OBJECTIVES To establish a deep learning (DL) model for predicting tumor grades and expression of pathologic markers of meningioma. METHODS A total of 1192 meningioma patients from two centers who underwent surgical resection between September 2018 and December 2021 were retrospectively included. The pathological data and post-contrast T1-weight images for each patient were collected. The patients from institute I were subdivided into training, validation, and testing sets, while the patients from institute II served as the external testing cohort. The fine-tuned ResNet50 model based on transfer learning was adopted to classify WHO grade in the whole cohort and predict Ki-67 index, H3K27me3, and progesterone receptor (PR) status of grade 1 meningiomas. The predictive performance was evaluated by the accuracy and loss curve, confusion matrix, receiver operating characteristic curve (ROC), and area under curve (AUC). RESULTS The DL prediction model for each label achieved high predictive performance in two cohorts. For WHO grade prediction, the area under the curve (AUC) was 0.966 (95%CI 0.957-0.975) in the internal testing set and 0.669 (95%CI 0.643-0.695) in the external validation cohort. The AUC in predicting Ki-67 index, H3K27me3, and PR status were 0.905 (95%CI 0.895-0.915), 0.773 (95%CI 0.760-0.786), and 0.771 (95%CI 0.750-0.792) in the internal testing set and 0.591 (95%CI 0.562-0.620), 0.658 (95%CI 0.648-0.668), and 0.703 (95%CI 0.674-0.732) in the external validation cohort, respectively. CONCLUSION DL models can preoperatively predict meningioma grades and pathologic marker expression with favorable predictive performance. CLINICAL RELEVANCE STATEMENT Our DL model could predict meningioma grades and expression of pathologic markers and identify high-risk patients with WHO grade 1 meningioma, which would suggest a more aggressive operative intervention preoperatively and a more frequent follow-up schedule postoperatively. KEY POINTS WHO grades and some pathologic markers of meningioma were associated with therapeutic strategies and clinical outcomes. A deep learning-based approach was employed to develop a model for predicting meningioma grades and the expression of pathologic markers. Preoperative prediction of meningioma grades and the expression of pathologic markers was beneficial for clinical decision-making.
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Affiliation(s)
- Jiawei Chen
- Department of Neurosurgery of Huashan Hospital, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science and Institutes of Brain Science, Fudan University, Shanghai, China
| | - Yanping Xue
- Department of Neurosurgery of Huashan Hospital, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science and Institutes of Brain Science, Fudan University, Shanghai, China
| | - Leihao Ren
- Department of Neurosurgery of Huashan Hospital, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science and Institutes of Brain Science, Fudan University, Shanghai, China
| | - Kun Lv
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Peng Du
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Haixia Cheng
- Department of Pathology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Shuchen Sun
- Department of Neurosurgery, Shanghai International Hospital, Shanghai, China
- Department of Neurosurgery, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lingyang Hua
- Department of Neurosurgery of Huashan Hospital, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science and Institutes of Brain Science, Fudan University, Shanghai, China
| | - Qing Xie
- Department of Neurosurgery of Huashan Hospital, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science and Institutes of Brain Science, Fudan University, Shanghai, China.
| | - Ruiqi Wu
- Department of Neurosurgery of Huashan Hospital, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science and Institutes of Brain Science, Fudan University, Shanghai, China.
| | - Ye Gong
- Department of Neurosurgery of Huashan Hospital, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science and Institutes of Brain Science, Fudan University, Shanghai, China.
- Department of Critical Care Medicine, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
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Zhang Z, Miao Y, Wu J, Zhang X, Ma Q, Bai H, Gao Q. Deep learning and radiomics-based approach to meningioma grading: exploring the potential value of peritumoral edema regions. Phys Med Biol 2024; 69:105002. [PMID: 38593827 DOI: 10.1088/1361-6560/ad3cb1] [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: 02/09/2024] [Accepted: 04/09/2024] [Indexed: 04/11/2024]
Abstract
Objective.To address the challenge of meningioma grading, this study aims to investigate the potential value of peritumoral edema (PTE) regions and proposes a unique approach that integrates radiomics and deep learning techniques.Approach.The primary focus is on developing a transfer learning-based meningioma feature extraction model (MFEM) that leverages both vision transformer (ViT) and convolutional neural network (CNN) architectures. Additionally, the study explores the significance of the PTE region in enhancing the grading process.Main results.The proposed method demonstrates excellent grading accuracy and robustness on a dataset of 98 meningioma patients. It achieves an accuracy of 92.86%, precision of 93.44%, sensitivity of 95%, and specificity of 89.47%.Significance.This study provides valuable insights into preoperative meningioma grading by introducing an innovative method that combines radiomics and deep learning techniques. The approach not only enhances accuracy but also reduces observer subjectivity, thereby contributing to improved clinical decision-making processes.
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Affiliation(s)
- Zhuo Zhang
- Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electronic and Information Engineering, Tiangong University, Tianjin, 300387, People's Republic of China
- College of Computer Science and Technology, National University of Defense Technology, 109 Deya Road, Changsha, 410073, People's Republic of China
| | - Ying Miao
- School of Computer Science, Qufu Normal University, RiZhao 276800, People's Republic of China
| | - JiXuan Wu
- Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electronic and Information Engineering, Tiangong University, Tianjin, 300387, People's Republic of China
| | - Xiaochen Zhang
- Tianjin Cerebral Vascular and Neural Degenerative Disease Key Laboratory, Tianjin Huanhu Hospital, Tianjin, 300350, People's Republic of China
| | - Quanfeng Ma
- Tianjin Cerebral Vascular and Neural Degenerative Disease Key Laboratory, Tianjin Huanhu Hospital, Tianjin, 300350, People's Republic of China
| | - Hua Bai
- Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electronic and Information Engineering, Tiangong University, Tianjin, 300387, People's Republic of China
| | - Qiang Gao
- Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electronic and Information Engineering, Tiangong University, Tianjin, 300387, People's Republic of China
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Gui Y, Zhang J. Research Progress of Artificial Intelligence in the Grading and Classification of Meningiomas. Acad Radiol 2024:S1076-6332(24)00073-4. [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] [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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Huang C, Tang T, Ding Z, Wang H, Zhou Z. Predicting the Probability of Tumor-Specific Survival in Patients Diagnosed With Primary Tumors in the Spinal Cord Using Nomogram Models. Global Spine J 2024:21925682241235894. [PMID: 38406860 DOI: 10.1177/21925682241235894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/27/2024] Open
Abstract
STUDY DESIGN A retrospective cohort study. OBJECTIVE The goal of this study was to develop a useful clinical prediction nomogram to accurately predict the cancer-specific survival (CSS) of patients with primary spinal cord tumor (SCT), thereby formulating scientific prevention and aiding clinical decision-making. METHODS In this study, patients with SCT diagnoses from the surveillance, epidemiology, and end results (SEER) database (2000-2018) were taken into account. Initially, a nomogram was created using the CSS-associated independent factors that were determined from both univariate and multivariable Cox regression analyses. Furthermore, the nomogram's capacity for calibration, ability to discriminate, and actual clinical effectiveness were assessed through calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA), respectively. Finally, a strategy for categorizing SCT patients' risk was developed. RESULTS This study included 909 SCT individuals. A novel nomogram was developed to forecast SCT patients' CSS, taking into account age, histological type, tumor grade, tumor stage, and radiotherapy. These factors were identified as independent prognostic indicators for CSS in SCT patients. Elderly SCT patients with distant metastasis, advanced tumor grade, received radiotherapy, and confirmed lymphoma have a poor prognosis. Meanwhile, the risk classification system could differentiate SCT patients and realize targeted management. CONCLUSIONS The developed nomogram has the ability to accurately forecast the CSS in SCT individuals, aiding in precise decision-making during clinical practice, enhancing health planning, maximizing treatment advantages, and ultimately improving patient prognosis.
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Affiliation(s)
- Chao Huang
- Department of Orthopedics, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Tingting Tang
- Department of Orthopedics, West China Hospital of Sichuan University, Chengdu, Sichuan, China
- School of Nursing, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Zichuan Ding
- Department of Orthopedics, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Haoyang Wang
- Department of Orthopedics, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Zongke Zhou
- Department of Orthopedics, West China Hospital of Sichuan University, Chengdu, Sichuan, China
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Wang Z, Li F, Wu F, Guo F, Gao W, Zhang Y, Yang Z. Environmental DNA and remote sensing datasets reveal the spatial distribution of aquatic insects in a disturbed subtropical river system. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 351:119972. [PMID: 38159308 DOI: 10.1016/j.jenvman.2023.119972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 12/04/2023] [Accepted: 12/25/2023] [Indexed: 01/03/2024]
Abstract
Biodiversity datasets with high spatial resolution are critical prerequisites for river protection and management decision-making. However, traditional morphological biomonitoring is inefficient and only provides several site estimates, and there is an urgent need for new approaches to predict biodiversity on fine spatial scales throughout the entire river systems. Here, we combined the environmental DNA (eDNA) and remote sensing (RS) technologies to develop a novel approach for predicting the spatial distribution of aquatic insects with high spatial resolution in a disturbed subtropical Dongjiang River system of southeast China. First, we screened thirteen RS-based vegetation indices that significantly correlated with the eDNA-inferred richness of aquatic insects. In particular, the green normalized difference vegetation index (GNDVI) and normalized difference red-edge2 (NDRE2) were closely related to eDNA-inferred richness. Second, using the gradient boosting decision tree, our data showed that the spatial pattern of eDNA-inferred richness could achieve a high spatial resolution to 500 m reach and accurate prediction of more than 80%, and the prediction efficiency of the headwater streams (Strahler stream order = 1) was slightly higher than the downstream (Strahler stream order >1). Third, using the random forest algorithm, the spatial distribution of aquatic insects could reach a prediction rate of over 70% for the presence or absence of specific genera. Overall, this study provides a new approach to achieving high spatial resolution prediction of the distribution of aquatic insects, which supports decision-making on river diversity protection under climate changes and human impacts.
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Affiliation(s)
- Zongyang Wang
- Guangdong Provincial Key Laboratory of Water Quality Improvement and Ecological Restoration for Watersheds, School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou, 510006, China
| | - Feilong Li
- Guangdong Provincial Key Laboratory of Water Quality Improvement and Ecological Restoration for Watersheds, School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou, 510006, China.
| | - Feifei Wu
- Guangdong Provincial Key Laboratory of Water Quality Improvement and Ecological Restoration for Watersheds, School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou, 510006, China
| | - Fen Guo
- Guangdong Provincial Key Laboratory of Water Quality Improvement and Ecological Restoration for Watersheds, School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou, 510006, China
| | - Wei Gao
- Guangdong Provincial Key Laboratory of Water Quality Improvement and Ecological Restoration for Watersheds, School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou, 510006, China
| | - Yuan Zhang
- Guangdong Provincial Key Laboratory of Water Quality Improvement and Ecological Restoration for Watersheds, School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou, 510006, China.
| | - Zhifeng Yang
- Guangdong Provincial Key Laboratory of Water Quality Improvement and Ecological Restoration for Watersheds, School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou, 510006, China; Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou, 511458, China
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Chen C, Teng Y, Tan S, Wang Z, Zhang L, Xu J. Performance Test of a Well-Trained Model for Meningioma Segmentation in Health Care Centers: Secondary Analysis Based on Four Retrospective Multicenter Data Sets. J Med Internet Res 2023; 25:e44119. [PMID: 38100181 PMCID: PMC10757229 DOI: 10.2196/44119] [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: 11/08/2022] [Revised: 06/21/2023] [Accepted: 11/22/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Convolutional neural networks (CNNs) have produced state-of-the-art results in meningioma segmentation on magnetic resonance imaging (MRI). However, images obtained from different institutions, protocols, or scanners may show significant domain shift, leading to performance degradation and challenging model deployment in real clinical scenarios. OBJECTIVE This research aims to investigate the realistic performance of a well-trained meningioma segmentation model when deployed across different health care centers and verify the methods to enhance its generalization. METHODS This study was performed in four centers. A total of 606 patients with 606 MRIs were enrolled between January 2015 and December 2021. Manual segmentations, determined through consensus readings by neuroradiologists, were used as the ground truth mask. The model was previously trained using a standard supervised CNN called Deeplab V3+ and was deployed and tested separately in four health care centers. To determine the appropriate approach to mitigating the observed performance degradation, two methods were used: unsupervised domain adaptation and supervised retraining. RESULTS The trained model showed a state-of-the-art performance in tumor segmentation in two health care institutions, with a Dice ratio of 0.887 (SD 0.108, 95% CI 0.903-0.925) in center A and a Dice ratio of 0.874 (SD 0.800, 95% CI 0.854-0.894) in center B. Whereas in the other health care institutions, the performance declined, with Dice ratios of 0.631 (SD 0.157, 95% CI 0.556-0.707) in center C and 0.649 (SD 0.187, 95% CI 0.566-0.732) in center D, as they obtained the MRI using different scanning protocols. The unsupervised domain adaptation showed a significant improvement in performance scores, with Dice ratios of 0.842 (SD 0.073, 95% CI 0.820-0.864) in center C and 0.855 (SD 0.097, 95% CI 0.826-0.886) in center D. Nonetheless, it did not overperform the supervised retraining, which achieved Dice ratios of 0.899 (SD 0.026, 95% CI 0.889-0.906) in center C and 0.886 (SD 0.046, 95% CI 0.870-0.903) in center D. CONCLUSIONS Deploying the trained CNN model in different health care institutions may show significant performance degradation due to the domain shift of MRIs. Under this circumstance, the use of unsupervised domain adaptation or supervised retraining should be considered, taking into account the balance between clinical requirements, model performance, and the size of the available data.
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Affiliation(s)
- Chaoyue Chen
- Neurosurgery Department, West China Hospital, Sichuan University, Chengdu, China
| | - Yuen Teng
- Neurosurgery Department, West China Hospital, Sichuan University, Chengdu, China
| | - Shuo Tan
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, China
| | - Zizhou Wang
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, China
- Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore, Singapore
| | - Lei Zhang
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, China
| | - Jianguo Xu
- Neurosurgery Department, West China Hospital, Sichuan University, Chengdu, China
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Park JH, Quang LT, Yoon W, Baek BH, Park I, Kim SK. Predicting Histologic Grade of Meningiomas Using a Combined Model of Radiomic and Clinical Imaging Features from Preoperative MRI. Biomedicines 2023; 11:3268. [PMID: 38137489 PMCID: PMC10741678 DOI: 10.3390/biomedicines11123268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 12/04/2023] [Accepted: 12/09/2023] [Indexed: 12/24/2023] Open
Abstract
Meningiomas are common primary brain tumors, and their accurate preoperative grading is crucial for treatment planning. This study aimed to evaluate the value of radiomics and clinical imaging features in predicting the histologic grade of meningiomas from preoperative MRI. We retrospectively reviewed patients with intracranial meningiomas from two hospitals. Preoperative MRIs were analyzed for tumor and edema volumes, enhancement patterns, margins, and tumor-brain interfaces. Radiomics features were extracted, and machine learning models were employed to predict meningioma grades. A total of 212 patients were included. In the training group (Hospital 1), significant differences were observed between low-grade and high-grade meningiomas in terms of tumor volume (p = 0.012), edema volume (p = 0.004), enhancement (p = 0.001), margin (p < 0.001), and tumor-brain interface (p < 0.001). Five radiomics features were selected for model development. The prediction model for radiomics features demonstrated an average validation accuracy of 0.74, while the model for clinical imaging features showed an average validation accuracy of 0.69. When applied to external test data (Hospital 2), the radiomics model achieved an area under the receiver operating characteristics curve (AUC) of 0.72 and accuracy of 0.69, while the clinical imaging model achieved an AUC of 0.82 and accuracy of 0.81. An improved performance was obtained from the model constructed by combining radiomics and clinical imaging features. In the combined model, the AUC and accuracy for meningioma grading were 0.86 and 0.73, respectively. In conclusion, this study demonstrates the potential value of radiomics and clinical imaging features in predicting the histologic grade of meningiomas. The combination of both radiomics and clinical imaging features achieved the highest AUC among the models. Therefore, the combined model of radiomics and clinical imaging features may offer a more effective tool for predicting clinical outcomes in meningioma patients.
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Affiliation(s)
- Jae Hyun Park
- Department of Radiology, Chonnam National University Hospital, Gwangju 61469, Republic of Korea; (J.H.P.); (W.Y.)
| | - Le Thanh Quang
- Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju 61469, Republic of Korea;
| | - Woong Yoon
- Department of Radiology, Chonnam National University Hospital, Gwangju 61469, Republic of Korea; (J.H.P.); (W.Y.)
- Department of Radiology, Chonnam National University Medical School, Gwangju 61469, Republic of Korea
| | - Byung Hyun Baek
- Department of Radiology, Chonnam National University Hospital, Gwangju 61469, Republic of Korea; (J.H.P.); (W.Y.)
- Department of Radiology, Chonnam National University Medical School, Gwangju 61469, Republic of Korea
| | - Ilwoo Park
- Department of Radiology, Chonnam National University Hospital, Gwangju 61469, Republic of Korea; (J.H.P.); (W.Y.)
- Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju 61469, Republic of Korea;
- Department of Radiology, Chonnam National University Medical School, Gwangju 61469, Republic of Korea
- Department of Data Science, Chonnam National University, Gwangju 61186, Republic of Korea
| | - Seul Kee Kim
- Department of Radiology, Chonnam National University Medical School, Gwangju 61469, Republic of Korea
- Department of Radiology, Chonnam National University Hwasun Hospital, Hwasun-gun 58128, Republic of Korea
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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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11
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Cai Z, Wong LM, Wong YH, Lee HL, Li KY, So TY. Dual-Level Augmentation Radiomics Analysis for Multisequence MRI Meningioma Grading. Cancers (Basel) 2023; 15:5459. [PMID: 38001719 PMCID: PMC10670283 DOI: 10.3390/cancers15225459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 11/07/2023] [Accepted: 11/14/2023] [Indexed: 11/26/2023] Open
Abstract
BACKGROUND Preoperative, noninvasive prediction of meningioma grade is important for therapeutic planning and decision making. In this study, we propose a dual-level augmentation strategy incorporating image-level augmentation (IA) and feature-level augmentation (FA) to tackle class imbalance and improve the predictive performance of radiomics for meningioma grading on Magnetic Resonance Imaging (MRI). METHODS This study recruited 160 consecutive patients with pathologically proven meningioma (129 low-grade (WHO grade I) tumors; 31 high-grade (WHO grade II and III) tumors) with preoperative multisequence MRI imaging. A dual-level augmentation strategy combining IA and FA was applied and evaluated in 100 repetitions in 3-, 5-, and 10-fold cross-validation. RESULTS The best area under the receiver operating characteristics curve of our method in 100 repetitions was ≥0.78 in all cross-validations. The corresponding cross-validation sensitivities (cross-validation specificity) were 0.72 (0.69), 0.76 (0.71), and 0.63 (0.82) in 3-, 5-, and 10-fold cross-validation, respectively. The proposed method achieved significantly better performance and distribution of results, outperforming single-level augmentation (IA or FA) or no augmentation in each cross-validation. CONCLUSIONS The dual-level augmentation strategy using IA and FA significantly improves the performance of the radiomics model for meningioma grading on MRI, allowing better radiomics-based preoperative stratification and individualized treatment.
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Affiliation(s)
| | | | | | | | | | - Tiffany Y. So
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong SAR, China; (Z.C.); (L.M.W.); (Y.H.W.); (H.-l.L.); (K.-y.L.)
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12
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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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13
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Li M, Liu L, Qi J, Qiao Y, Zeng H, Jiang W, Zhu R, Chen F, Huang H, Wu S. MRI-based machine learning models predict the malignant biological behavior of meningioma. BMC Med Imaging 2023; 23:141. [PMID: 37759192 PMCID: PMC10537075 DOI: 10.1186/s12880-023-01101-7] [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: 03/22/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGROUND The WHO grade and Ki-67 index are independent indices used to evaluate the malignant biological behavior of meningioma. This study aims to develop MRI-based machine learning models to predict the malignant biological behavior of meningioma from the perspective of the WHO grade, Ki-67 index, and their combination. METHODS This multicenter, retrospective study included 313 meningioma patients, of which 70 were classified as high-grade (WHO II/III) and 243 as low-grade (WHO I). The Ki-67 expression was classified into low-expression (n = 216) and high-expression (n = 97) groups with a threshold of 5%. Among them, there were 128 patients with malignant biological behavior whose WHO grade or Ki-67 index increased either or both. Data from Center A and B are were utilized for model development, while data from Center C and D were used for external validation. Radiomic features were extracted from the maximum cross-sectional area (2D) region of Interest (ROI) and the whole tumor volume (3D) ROI using different paraments from the T1, T2-weighted, and T1 contrast-enhanced sequences (T1CE), followed by five independent feature selections and eight classifiers. 240 prediction models were constructed to predict the WHO grade, Ki-67 index and their combination respectively. Models were evaluated by cross-validation in training set (n = 224). Suitable models were chosen by comparing the cross-validation (CV) area under the curves (AUC) and their relative standard deviations (RSD). Clinical and radiological features were collected and analyzed; meaningful features were combined with radiomic features to establish the clinical-radiological-radiomic (CRR) models. The receiver operating characteristic (ROC) analysis was used to evaluate those models in validation set. Radiomic models and CRR models were compared by Delong test. RESULTS 1218 and 1781 radiomic features were extracted from 2D ROI and 3D ROI of each sequence. The selected grade, Ki-67 index and their combination radiomic models were T1CE-2D-LASSO-LR, T1CE-3D-LASSO-NB, and T1CE-2D-LASSO-LR, with cross-validated AUCs on the training set were 0.857, 0.798, and 0.888, the RSDs were 0.06, 0.09, and 0.05, the validation set AUCs were 0.829, 0.752, and 0.904, respectively. Heterogeneous enhancement was found to be associated with high grade and Ki-67 status, while surrounding invasion was associated with the high grade status, peritumoral edema and cerebrospinal fluid space surrounding tumor were correlated with the high Ki-67 status. The Delong test showed that these significant radiological features did not significantly improve the predictive performance. The AUCs for CRR models predicting grade, Ki-67 index, and their combination in the validation set were 0.821, 0.753, and 0.906, respectively. CONCLUSIONS This study demonstrated that MRI-based machine learning models could effectively predict the grade, Ki-67 index of meningioma. Models considering these two indices might be valuable for improving the predictive sensitivity and comprehensiveness of prediction of malignant biological behavior of meningioma.
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Affiliation(s)
- Maoyuan Li
- Department of Radiology, Chengdu Qingbaijiang District People's Hospital, Chengdu, 610300, Sichuan, China
- Department of Radiology, The First Affiliated Hospital of Chengdu Medical College, Chengdu, 610500, Sichuan, China
| | - Luzhou Liu
- Department of Radiology, The First Affiliated Hospital of Chengdu Medical College, Chengdu, 610500, Sichuan, China
| | - Jie Qi
- Department of Radiology, The First Affiliated Hospital of Chengdu Medical College, Chengdu, 610500, Sichuan, China
| | - Ying Qiao
- Department of Radiology, The First Affiliated Hospital of Chengdu Medical College, Chengdu, 610500, Sichuan, China
| | - Hanrui Zeng
- Department of Radiology, The First Affiliated Hospital of Chengdu Medical College, Chengdu, 610500, Sichuan, China
| | - Wen Jiang
- Department of Radiology, The First Affiliated Hospital of Chengdu Medical College, Chengdu, 610500, Sichuan, China
| | - Rui Zhu
- Department of Radiology, The First Affiliated Hospital of Chengdu Medical College, Chengdu, 610500, Sichuan, China
| | - Fujian Chen
- Department of Radiology, Mianyang Central Hospital, Mianyang, 621000, Sichuan, China
| | - Huan Huang
- Department of Radiology, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China
| | - Shaoping Wu
- Department of Radiology, Chengdu Medical College, Chengdu, 610500, Sichuan, China.
- Department of Radiology, Sichuan Taikang Hospital, Chengdu, 610041, Sichuan, China.
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14
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Maiuri F, Del Basso de Caro M. Update on the Diagnosis and Management of Meningiomas. Cancers (Basel) 2023; 15:3575. [PMID: 37509238 PMCID: PMC10377680 DOI: 10.3390/cancers15143575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 06/29/2023] [Accepted: 07/10/2023] [Indexed: 07/30/2023] Open
Abstract
This series of five articles (one original article and four reviews) focuses on the most recent and interesting research studies on the biomolecular and radiological diagnosis and the surgical and medical management of meningiomas [...].
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Affiliation(s)
- Francesco Maiuri
- Department of Neurosciences and Reproductive and Odontostomatological Sciences, Neurosurgical Clinic, 80131 Naples, Italy
| | - Marialaura Del Basso de Caro
- Department of Advanced Biomedical Sciences, Section of Pathology, School of Medicine, University "Federico II" of Naples, 80131 Naples, Italy
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15
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Loken EK, Huang RY. Advanced Meningioma Imaging. Neurosurg Clin N Am 2023; 34:335-345. [PMID: 37210124 DOI: 10.1016/j.nec.2023.02.015] [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: 05/22/2023]
Abstract
Noninvasive imaging methods are used to accurately diagnose meningiomas and track their growth and location. These techniques, including computed tomography, MRI, and nuclear medicine, are also being used to gather more information about the biology of the tumors and potentially predict their grade and impact on prognosis. In this article, we will discuss the current and developing uses of these imaging techniques including additional analysis using radiomics in the diagnosis and treatment of meningiomas, including treatment planning and prediction of tumor behavior.
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Affiliation(s)
- Erik K Loken
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA.
| | - Raymond Y Huang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA
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16
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He C, Xie D, Fu LF, Yu JN, Wu FY, Qiu YG, Xu HW. A nomogram based on radiomics intermuscular adipose analysis to indicate arteriosclerosis in patients with newly diagnosed type 2 diabetes. Front Endocrinol (Lausanne) 2023; 14:1201110. [PMID: 37305059 PMCID: PMC10250635 DOI: 10.3389/fendo.2023.1201110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 05/15/2023] [Indexed: 06/13/2023] Open
Abstract
Objective Early identifying arteriosclerosis in newly diagnosed type 2 diabetes (T2D) patients could contribute to choosing proper subjects for early prevention. Here, we aimed to investigate whether radiomic intermuscular adipose tissue (IMAT) analysis could be used as a novel marker to indicate arteriosclerosis in newly diagnosed T2D patients. Methods A total of 549 patients with newly diagnosed T2D were included in this study. The clinical information of the patients was recorded and the carotid plaque burden was used to indicate arteriosclerosis. Three models were constructed to evaluate the risk of arteriosclerosis: a clinical model, a radiomics model (a model based on IMAT analysis proceeded on chest CT images), and a clinical-radiomics combined model (a model that integrated clinical-radiological features). The performance of the three models were compared using the area under the curve (AUC) and DeLong test. Nomograms were constructed to indicate arteriosclerosis presence and severity. Calibration curves and decision curves were plotted to evaluate the clinical benefit of using the optimal model. Results The AUC for indicating arteriosclerosis of the clinical-radiomics combined model was higher than that of the clinical model [0.934 (0.909, 0.959) vs. 0.687 (0.634, 0.730), P < 0.001 in the training set, 0.933 (0.898, 0.969) vs. 0.721 (0.642, 0.799), P < 0.001 in the validation set]. Similar indicative efficacies were found between the clinical-radiomics combined model and radiomics model (P = 0.5694). The AUC for indicating the severity of arteriosclerosis of the combined clinical-radiomics model was higher than that of both the clinical model and radiomics model [0.824 (0.765, 0.882) vs. 0.755 (0.683, 0.826) and 0.734 (0.663, 0.805), P < 0.001 in the training set, 0.717 (0.604, 0.830) vs. 0.620 (0.490, 0.750) and 0.698 (0.582, 0.814), P < 0.001 in the validation set, respectively]. The decision curve showed that the clinical-radiomics combined model and radiomics model indicated a better performance than the clinical model in indicating arteriosclerosis. However, in indicating severe arteriosclerosis, the clinical-radiomics combined model had higher efficacy than the other two models. Conclusion Radiomics IMAT analysis could be a novel marker for indicating arteriosclerosis in patients with newly diagnosed T2D. The constructed nomograms provide a quantitative and intuitive way to assess the risk of arteriosclerosis, which may help clinicians comprehensively analyse radiomics characteristics and clinical risk factors more confidently.
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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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Wijethilake N, MacCormac O, Vercauteren T, Shapey J. Imaging biomarkers associated with extra-axial intracranial tumors: a systematic review. Front Oncol 2023; 13:1131013. [PMID: 37182138 PMCID: PMC10167010 DOI: 10.3389/fonc.2023.1131013] [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: 12/24/2022] [Accepted: 03/27/2023] [Indexed: 05/16/2023] Open
Abstract
Extra-axial brain tumors are extra-cerebral tumors and are usually benign. The choice of treatment for extra-axial tumors is often dependent on the growth of the tumor, and imaging plays a significant role in monitoring growth and clinical decision-making. This motivates the investigation of imaging biomarkers for these tumors that may be incorporated into clinical workflows to inform treatment decisions. The databases from Pubmed, Web of Science, Embase, and Medline were searched from 1 January 2000 to 7 March 2022, to systematically identify relevant publications in this area. All studies that used an imaging tool and found an association with a growth-related factor, including molecular markers, grade, survival, growth/progression, recurrence, and treatment outcomes, were included in this review. We included 42 studies, comprising 22 studies (50%) of patients with meningioma; 17 studies (38.6%) of patients with pituitary tumors; three studies (6.8%) of patients with vestibular schwannomas; and two studies (4.5%) of patients with solitary fibrous tumors. The included studies were explicitly and narratively analyzed according to tumor type and imaging tool. The risk of bias and concerns regarding applicability were assessed using QUADAS-2. Most studies (41/44) used statistics-based analysis methods, and a small number of studies (3/44) used machine learning. Our review highlights an opportunity for future work to focus on machine learning-based deep feature identification as biomarkers, combining various feature classes such as size, shape, and intensity. Systematic Review Registration: PROSPERO, CRD42022306922.
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Affiliation(s)
- Navodini Wijethilake
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Oscar MacCormac
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Department of Neurosurgery, King’s College Hospital NHS Foundation Trust, London, United Kingdom
| | - Tom Vercauteren
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Jonathan Shapey
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Department of Neurosurgery, King’s College Hospital NHS Foundation Trust, London, United Kingdom
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Kong X, Luo Y, Li Y, Zhan D, Mao Y, Ma J. Preoperative prediction and histological stratification of intracranial solitary fibrous tumours by machine-learning models. Clin Radiol 2023; 78:e204-e213. [PMID: 36496260 DOI: 10.1016/j.crad.2022.10.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 09/23/2022] [Accepted: 10/22/2022] [Indexed: 12/12/2022]
Abstract
AIM To explore the effectiveness and feasibility of machine-learning models based on magnetic resonance imaging (MRI) radiomics features in differentiating intracranial solitary fibrous tumour (ISFT) from angiomatous meningioma (AM) and stratifying ISFT histologically. MATERIALS AND METHODS This study retrospectively recruited 268 patients with a histological diagnosis of ISFT (n=120) or AM (n=148), and 116 of the ISFT patients were used for stratified analysis of histological grade. The radiomics features were extracted from axial T1-weighted imaging (WI), T2WI and contrast-enhanced T1WI sequences. All patients were assigned randomly to the training group and test group in a ratio of 7:3. The models were optimised by 10-fold cross-validation in the training group, and the independent test group was used for further testing of the models. The performances of machine-learning models based on radiomics, clinical, and fusion features in predicting and stratifying ISFT were evaluated. RESULTS ISFT and AM differed significantly in terms of age, tumour shape, enhancement pattern, and margin. There was no significant difference in the clinical characteristics between World Health Organization (WHO) grade II and WHO grade III ISFT. When used to differentiate ISFT from AM, the area under the curve (AUC) values of the machine-learning models based on radiomics, clinical, and fusion features in the test group were 0.917, 0.923 and 0.950, respectively. When used for histological stratification of ISFT, the model based on the radiomics signature achieved an AUC value of 0.786 in the test group. CONCLUSIONS Machine-learning models can contribute in the prediction and histological stratification of ISFT non-invasively, which can help clinical differential diagnosis and treatment decisions.
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Affiliation(s)
- X Kong
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100071, China
| | - Y Luo
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100071, China
| | - Y Li
- Department of Radiology, Beijing Fengtai Hospital, Beijing 100071, China
| | - D Zhan
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100071, China
| | - Y Mao
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100071, China
| | - J Ma
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100071, China.
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Mori N, Mugikura S, Endo T, Endo H, Oguma Y, Li L, Ito A, Watanabe M, Kanamori M, Tominaga T, Takase K. Principal component analysis of texture features for grading of meningioma: not effective from the peritumoral area but effective from the tumor area. Neuroradiology 2023; 65:257-274. [PMID: 36044063 DOI: 10.1007/s00234-022-03045-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 08/23/2022] [Indexed: 01/25/2023]
Abstract
PURPOSE To investigate whether texture features from tumor and peritumoral areas based on sequence combinations can differentiate between low- and non-low-grade meningiomas. METHODS Consecutive patients diagnosed with meningioma by surgery (77 low-grade and 28 non-low-grade meningiomas) underwent preoperative magnetic resonance imaging including T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and contrast-enhanced T1WI (CE-T1WI). Manual segmentation of the tumor area was performed to extract texture features. Segmentation of the peritumoral area was performed for peritumoral high-signal intensity (PHSI) on T2WI. Principal component analysis was performed to fuse the texture features to principal components (PCs), and PCs of each sequence of the tumor and peritumoral areas were compared between low- and non-low-grade meningiomas. Only PCs with statistical significance were used for the model construction using a support vector machine algorithm. k-fold cross-validation with receiver operating characteristic curve analysis was used to evaluate diagnostic performance. RESULTS Two, one, and three PCs of T1WI, apparent diffusion coefficient (ADC), and CE-T1WI, respectively, for the tumor area, were significantly different between low- and non-low-grade meningiomas, while PCs of T2WI for the tumor area and PCs for the peritumoral area were not. No significant differences were observed in PHSI. Among models of sequence combination, the model with PCs of ADC and CE-T1WI for the tumor area showed the highest area under the curve (0.84). CONCLUSION The model with PCs of ADC and CE-T1WI for the tumor area showed the highest diagnostic performance for differentiating between low- and non-low-grade meningiomas. Neither PHSI nor PCs in the peritumoral area showed added value.
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Affiliation(s)
- Naoko Mori
- Department of Diagnostic Radiology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, Japan.
| | - Shunji Mugikura
- Department of Diagnostic Radiology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, Japan
| | - Toshiki Endo
- Department of Neurosurgery, Tohoku University Graduate School of Medicine, Sendai, Japan.,Department of Neurosurgery, Tohoku Medical and Pharmaceutical University, Sendai, Japan
| | - Hidenori Endo
- Department of Neurosurgery, Tohoku University Graduate School of Medicine, Sendai, Japan.,Department of Neurosurgery, Kohnan Hospital, Sendai, Japan
| | - Yo Oguma
- Department of Diagnostic Radiology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, Japan
| | - Li Li
- Department of Diagnostic Radiology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, Japan
| | - Akira Ito
- Department of Neurosurgery, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Mika Watanabe
- Department of Anatomic Pathology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Masayuki Kanamori
- Department of Neurosurgery, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Teiji Tominaga
- Department of Neurosurgery, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Kei Takase
- Department of Diagnostic Radiology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, Japan
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21
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Krähling H, Musigmann M, Akkurt BH, Sartoretti T, Sartoretti E, Henssen DJHA, Stummer W, Heindel W, Brokinkel B, Mannil M. A magnetic resonance imaging based radiomics model to predict mitosis cycles in intracranial meningioma. Sci Rep 2023; 13:969. [PMID: 36653482 PMCID: PMC9849352 DOI: 10.1038/s41598-023-28089-y] [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: 07/14/2022] [Accepted: 01/12/2023] [Indexed: 01/19/2023] Open
Abstract
The aim of this study was to develop a magnetic resonance imaging (MRI) based radiomics model to predict mitosis cycles in intracranial meningioma grading prior to surgery. Preoperative contrast-enhanced T1-weighted (T1CE) cerebral MRI data of 167 meningioma patients between 2015 and 2020 were obtained, preprocessed and segmented using the 3D Slicer software and the PyRadiomics plugin. In total 145 radiomics features of the T1CE MRI images were computed. The criterion on the basis of which the feature selection was made is whether the number of mitoses per 10 high power field (HPF) is greater than or equal to zero. Our analyses show that machine learning algorithms can be used to make accurate predictions about whether the number of mitoses per 10 HPF is greater than or equal to zero. We obtained our best model using Ridge regression for feature pre-selection, followed by stepwise logistic regression for final model construction. Using independent test data, this model resulted in an AUC (Area under the Curve) of 0.8523, an accuracy of 0.7941, a sensitivity of 0.8182, a specificity of 0.7500 and a Cohen's Kappa of 0.5576. We analyzed the performance of this model as a function of the number of mitoses per 10 HPF. The model performs well for cases with zero mitoses as well as for cases with more than one mitosis per 10 HPF. The worst model performance (accuracy = 0.6250) is obtained for cases with one mitosis per 10 HPF. Our results show that MRI-based radiomics may be a promising approach to predict the mitosis cycles in intracranial meningioma prior to surgery. Specifically, our approach may offer a non-invasive means of detecting the early stages of a malignant process in meningiomas prior to the onset of clinical symptoms.
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Affiliation(s)
- Hermann Krähling
- University Clinic for Radiology, University Hospital Muenster, Westfälische Wilhelms-University Muenster, Albert-Schweitzer-Campus 1, 48149, Muenster, Germany
| | - Manfred Musigmann
- University Clinic for Radiology, University Hospital Muenster, Westfälische Wilhelms-University Muenster, Albert-Schweitzer-Campus 1, 48149, Muenster, Germany
| | - Burak Han Akkurt
- University Clinic for Radiology, University Hospital Muenster, Westfälische Wilhelms-University Muenster, Albert-Schweitzer-Campus 1, 48149, Muenster, Germany
| | | | | | - Dylan J H A Henssen
- Department of Medical Imaging, Radboud University Medical Center, Radboud University, 6500HB, Nijmegen, The Netherlands
| | - Walter Stummer
- Department of Neurosurgery, University Hospital Muenster, Westfälische Wilhelms-University Muenster, Albert-Schweitzer-Campus 1, 48149, Muenster, Germany
| | - Walter Heindel
- University Clinic for Radiology, University Hospital Muenster, Westfälische Wilhelms-University Muenster, Albert-Schweitzer-Campus 1, 48149, Muenster, Germany
| | - Benjamin Brokinkel
- Department of Neurosurgery, University Hospital Muenster, Westfälische Wilhelms-University Muenster, Albert-Schweitzer-Campus 1, 48149, Muenster, Germany
| | - Manoj Mannil
- University Clinic for Radiology, University Hospital Muenster, Westfälische Wilhelms-University Muenster, Albert-Schweitzer-Campus 1, 48149, Muenster, Germany.
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22
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Duan C, Li N, Liu X, Cui J, Wang G, Xu W. Performance comparison of 2D and 3D MRI radiomics features in meningioma grade prediction: A preliminary study. Front Oncol 2023; 13:1157379. [PMID: 37035216 PMCID: PMC10076744 DOI: 10.3389/fonc.2023.1157379] [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: 02/02/2023] [Accepted: 03/10/2023] [Indexed: 04/11/2023] Open
Abstract
Objectives The objective of this study was to compare the predictive performance of 2D and 3D radiomics features in meningioma grade based on enhanced T1 WI images. Methods There were 170 high grade meningioma and 170 low grade meningioma were selected randomly. The 2D and 3D features were extracted from 2D and 3D ROI of each meningioma. The Spearman correlation analysis and least absolute shrinkage and selection operator (LASSO) regression were used to select the valuable features. The 2D and 3D predictive models were constructed by naive Bayes (NB), gradient boosting decision tree (GBDT), and support vector machine (SVM). The ROC curve was drawn and AUC was calculated. The 2D and 3D models were compared by Delong test of AUCs and decision curve analysis (DCA) curve. Results There were 1143 features extracted from each ROI. Six and seven features were selected. The AUC of 2D and 3D model in NB, GBDT, and SVM was 0.773 and 0.771, 0.722 and 0.717, 0.733 and 0.743. There was no significant difference in two AUCs (p=0.960, 0.913, 0.830) between 2D and 3D model. The 2D features had a better performance than 3D features in NB models and the 3D features had a better performance than 2D features in GBDT models. The 2D features and 3D features had an equal performance in SVM models. Conclusions The 2D and 3D features had a comparable performance in predicting meningioma grade. Considering the issue of time and labor, 2D features could be selected for radiomics study in meningioma. Key points There was a comparable performance between 2D and 3D features in meningioma grade prediction. The 2D features was a proper selection in meningioma radiomics study because of its time and labor saving.
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Affiliation(s)
- Chongfeng Duan
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Nan Li
- Department of Information Management, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Xuejun Liu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jiufa Cui
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Gang Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Wenjian Xu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
- *Correspondence: Wenjian Xu,
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23
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Malta TM, Snyder J, Noushmehr H, Castro AV. Advances in Central Nervous System Tumor Classification. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1416:121-135. [PMID: 37432624 DOI: 10.1007/978-3-031-29750-2_10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 07/12/2023]
Abstract
Historically, the classification of tumors of the central nervous system (CNS) relies on the histologic appearance of cells under a microscope; however, the molecular era of medicine has resulted in new diagnostic paradigms anchored in the intrinsic biology of disease. The 2021 World Health Organization (WHO) reformulated the classification of CNS tumors to incorporate molecular parameters, in addition to histology, to define many tumor types. A contemporary classification system with integrated molecular features aims to provide an unbiased tool to define tumor subtype, the risk of tumor progression, and even the response to certain therapeutic agents. Meningiomas are heterogeneous tumors as depicted by the current 15 distinct variants defined by histology in the 2021 WHO classification, which also incorporated the first moelcular critiera for meningioma grading: homozygous loss of CDKN2A/B and TERT promoter mutation as criteria for a WHO grade 3 meningioma. The proper classification and clinical management of meningioma patients requires a multidisciplinary approach, which in addition to the information on microscopic (histology) and macroscopic (Simpson grade and imaging), should also include molecular alterations. In this chapter, we present the most up-to-date knowledge in CNS tumor classification, particularly in meningioma, in the molecular era and how it could affect their future classification and clinical management of patients with these diseases.
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Affiliation(s)
- Tathiane M Malta
- School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, São Paulo, Brazil
| | - James Snyder
- Department of Neurosurgery, Henry Ford Hospital, Detroit, MI, USA
| | - Houtan Noushmehr
- Department of Neurosurgery, Henry Ford Hospital, Detroit, MI, USA.
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Liu X, Wang Y, Han T, Liu H, Zhou J. Preoperative surgical risk assessment of meningiomas: a narrative review based on MRI radiomics. Neurosurg Rev 2022; 46:29. [PMID: 36576657 DOI: 10.1007/s10143-022-01937-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 12/08/2022] [Accepted: 12/22/2022] [Indexed: 12/29/2022]
Abstract
Meningiomas are one of the most common intracranial primary central nervous system tumors. Regardless of the pathological grading and histological subtypes, maximum safe resection is the recommended treatment option for meningiomas. However, considering tumor heterogeneity, surgical treatment options and prognosis often vary greatly among meningiomas. Therefore, an accurate preoperative surgical risk assessment of meningiomas is of great clinical importance as it helps develop surgical treatment strategies and improve patient prognosis. In recent years, an increasing number of studies have proved that magnetic resonance imaging (MRI) radiomics has wide application values in the diagnostic, identification, and prognostic evaluations of brain tumors. The vital importance of MRI radiomics in the surgical risk assessment of meningiomas must be apprehended and emphasized in clinical practice. This narrative review summarizes the current research status of MRI radiomics in the preoperative surgical risk assessment of meningiomas, focusing on the applications of MRI radiomics in preoperative pathological grading, assessment of surrounding tissue invasion, and evaluation of tumor consistency. We further analyze the prospects of MRI radiomics in the preoperative assessment of meningiomas angiogenesis and adhesion with surrounding tissues, while pointing out the current challenges of MRI radiomics research.
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Affiliation(s)
- Xianwang Liu
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Cuiyingmen No.82, Lanzhou, 730030, People's Republic of China
- Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China
| | - Yuzhu Wang
- Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China
| | - Tao Han
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Cuiyingmen No.82, Lanzhou, 730030, People's Republic of China
- Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China
| | - Hong Liu
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Cuiyingmen No.82, Lanzhou, 730030, People's Republic of China
- Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Cuiyingmen No.82, Lanzhou, 730030, People's Republic of China.
- Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China.
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China.
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China.
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Guo Z, Tian Z, Shi F, Xu P, Zhang J, Ling C, Zeng Q. Radiomic Features of the Edema Region May Contribute to Grading Meningiomas With Peritumoral Edema. J Magn Reson Imaging 2022. [PMID: 36259547 DOI: 10.1002/jmri.28494] [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: 05/21/2022] [Revised: 10/05/2022] [Accepted: 10/06/2022] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND Meningiomas are frequently accompanied by peritumoral edema (PTE). The potential value of radiomic features of edema region in meningioma grading has not been investigated. PURPOSE To investigate whether radiomic features of edema region contribute to grading meningiomas with PTE. STUDY TYPE Retrospective. POPULATION A total of 444 patients including 196 grade II and 248 WHO grade I meningiomas: 356 patients for training, 88 for validation. FIELD STRENGTH/SEQUENCE A 1.5-T/3.0-T, noncontrast T1-weighted (T1WI), T2-weighted (T2WI), contrast-enhanced T1-weighted (T1CE) spin echo sequences. ASSESSMENT A total of 851 radiomic features were extracted from each sequence on each region (tumor and edema region). These features were integrated by region respectively. Three subsets of clinical-radiomic features were constructed by joining clinical information (sex, age, tumor volume, and edema volume) and radiomic features of three regions: tumor, edema, and combined subsets. For each subset, features were filtered by the least absolute shrinkage and selection operator (LASSO) and Random Forest algorithm. Top 20 features of each subset were finally selected. STATISTICAL TESTS Stochastic Gradient Boosting, Random Forest, and Bagged AdaBoost predictive models were built based on each subset. Discriminative abilities of models were quantified using receiver operating characteristics (ROC) and the area under the curve (AUC). A P value < 0.05 was considered statistically significant. RESULTS Random Forest model based on combined subset (AUC [95% CI] = 0.880 [0.807-0.953]) had the best discriminative ability in grading meningiomas among the final models. The best model of edema subset and tumor subset were Random Forest model (AUC [95% CI] = 0.864 [0.791-0.938]) and Stochastic Gradient Boosting model (AUC [95% CI] = 0.844 [0.760-0.928]), respectively. DATA CONCLUSION Radiomic features of edema region may contribute to grading meningiomas with PTE. The Random Forest model based on combined subset surpasses the best model based on tumor or edema subset regarding grading meningiomas with PTE. EVIDENCE LEVEL 4 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Zhige Guo
- Department of Neurosurgery, Second Affiliated Hospital of Zhejiang University school of Medicine, Hangzhou, Zhejiang, China.,Department of Neurosurgery, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China
| | - Zhongyu Tian
- Department of Neurosurgery, Second Affiliated Hospital of Zhejiang University school of Medicine, Hangzhou, Zhejiang, China
| | - Feina Shi
- Department of Neurology, Sir Runrun Shaw Hospital of Zhejiang University school of Medicine, Hangzhou, Zhejiang, China
| | - Penglei Xu
- Department of Neurosurgery, Second Affiliated Hospital of Zhejiang University school of Medicine, Hangzhou, Zhejiang, China.,Clinical Research Center for Neurological Diseases of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Jianmin Zhang
- Department of Neurosurgery, Second Affiliated Hospital of Zhejiang University school of Medicine, Hangzhou, Zhejiang, China.,Clinical Research Center for Neurological Diseases of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Chenhan Ling
- Department of Neurosurgery, Second Affiliated Hospital of Zhejiang University school of Medicine, Hangzhou, Zhejiang, China.,Clinical Research Center for Neurological Diseases of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Qiang Zeng
- Department of Neurosurgery, Second Affiliated Hospital of Zhejiang University school of Medicine, Hangzhou, Zhejiang, China.,Clinical Research Center for Neurological Diseases of Zhejiang Province, Hangzhou, Zhejiang, China
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Differentiating Glioblastoma Multiforme from Brain Metastases Using Multidimensional Radiomics Features Derived from MRI and Multiple Machine Learning Models. BIOMED RESEARCH INTERNATIONAL 2022; 2022:2016006. [PMID: 36212721 PMCID: PMC9534611 DOI: 10.1155/2022/2016006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 08/06/2022] [Accepted: 09/08/2022] [Indexed: 11/18/2022]
Abstract
Due to different treatment strategies, it is extremely important to differentiate between glioblastoma multiforme (GBM) and brain metastases (MET). It often proves difficult to distinguish between GBM and MET using MRI due to their similar appearance on the imaging modalities. Surgical methods are still necessary for definitive diagnosis, despite the importance of magnetic resonance imaging in detecting, characterizing, and monitoring brain tumors. We introduced an accurate, convenient, and user-friendly method to differentiate between GBM and MET through routine MRI sequence and radiomics analyses. We collected 91 patients from one institution, including 50 with GBM and 41 with MET, which were proven pathologically. The tumors separately were segmented on all MRI images (T1-weighted imaging (T1WI), contrast-enhanced T1-weighted imaging (T1C), T2-weighted imaging (T2WI), and fluid-attenuated inversion recovery (FLAIR)) to form the volume of interest (VOI). Eight ML models and feature reduction strategies were evaluated using routine MRI sequences (T1W, T2W, T1-CE, and FLAIR) in two methods with (second model) and without wavelet transform (first model) radiomics. The optimal model was selected based on each model’s accuracy, AUC-roc, and F1-score values. In this study, we have achieved the result of 0.98, 0.99, and 0.98 percent for accuracy, AUC-roc, and F1-score, respectively, which have yielded a better result than the first model. In most investigated models, there were significant improvements in the multidimensional wavelets model compared to the non-multidimensional wavelets model. Multidimensional discrete wavelet transform can analyze hidden features of the MRI from a different perspective and generate accurate features which are highly correlated with the model accuracy.
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Diagnostic and Therapeutic Strategy in Anaplastic (Malignant) Meningioma, CNS WHO Grade 3. Cancers (Basel) 2022; 14:cancers14194689. [PMID: 36230612 PMCID: PMC9562197 DOI: 10.3390/cancers14194689] [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: 07/21/2022] [Revised: 09/12/2022] [Accepted: 09/22/2022] [Indexed: 11/21/2022] Open
Abstract
Simple Summary Only 1% of all meningioma diagnosis is classified as malignant (anaplastic) meningioma. Due to their rarity, clinical management of these tumors presents several gaps. In this review, we investigate current knowledge of anaplastic meningioma focusing on their pathological and radiological diagnosis, molecular assessment, and loco-regional and systemic management. Despite the current marginal role of systemic therapy, it is possible that the increasing knowledge of molecular altered pathways of the disease will lead to the development of novel effective systemic treatments. Abstract Background: Meningiomas are the most common primary central nervous system malignancies accounting for 36% of all intracranial tumors. However, only 1% of meningioma is classified as malignant (anaplastic) meningioma. Due to their rarity, clinical management of these tumors presents several gaps. Methods: We carried out a narrative review aimed to investigate current knowledge of anaplastic meningioma focusing on their pathological and radiological diagnosis, molecular assessment, and loco-regional and systemic management. Results: The most frequent genetic alteration occurring in meningioma is the inactivation in the neurofibromatosis 2 genes (merlin). The accumulation of copy number losses, including 1p, 6p/q, 10q, 14q, and 18p/q, and less frequently 2p/q, 3p, 4p/q, 7p, 8p/q, and 9p, compatible with instability, is restricted to NF2 mutated meningioma. Surgery and different RT approaches represent the milestone of grade 3 meningioma management, while there is a marginal role of systemic therapy. Conclusions: Anaplastic meningiomas are rare tumors, and diagnosis should be suspected and confirmed by trained radiologists and pathologists. Despite the current marginal role of systemic therapy, it is possible that the increasing knowledge of molecular altered pathways of the disease will lead to the development of novel effective systemic treatments.
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Duan C, Zhou X, Wang J, Li N, Liu F, Gao S, Liu X, Xu W. A radiomics nomogram for predicting the meningioma grade based on enhanced T1WI images. Br J Radiol 2022; 95:20220141. [PMID: 35816518 PMCID: PMC10996951 DOI: 10.1259/bjr.20220141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 06/24/2022] [Accepted: 07/05/2022] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVES The objective of this study was to develop a radiomics nomogram for predicting the meningioma grade based on enhanced T1 weighted imaging (T1WI) images. METHODS 188 patients with meningioma were analyzed retrospectively. There were 94 high-grade meningioma to form high-grade group and 94 low-grade meningioma were selected randomly to form low-grade group. Clinical data and MRI features were analyzed and compared. The clinical model was built by using the significant variables. The least absolute shrinkage and selection operator regression was used to select the most valuable radiomics feature. The radiomics signature was built and the Rad-score was calculated. The radiomics nomogram was developed by the significant variables of the clinical factors and Rad-score. The calibration curve and the Hosmer-Lemeshow test were used to evaluate the radiomics nomogram. Different models were compared by Delong test and decision curve analysis curve. RESULTS The sex, size and surrounding invasion were used to build clinical model. The area under the receiver operator characteristic curve (AUC) of clinical model was 0.870 (95% CI: 0.782-0.959). Nine features were used to construct the radiomics signature. The AUC of the radiomics signature was 0.885 (95% CI: 0.802-0.968). The AUC of radiomics nomogram was 0.952 (95% CI: 0.904-1). The AUC of radiomics nomogram was higher than that of clinical model and radiomics signature with a significant difference (p<0.05). The decision curve analysis curve showed that the radiomics nomogram had a larger net benefit than the clinical model and radiomics signature. CONCLUSION The radiomics nomogram based on enhanced T1 weighted imaging images for predicting the meningioma grade showed high predictive value and might contribute to the diagnosis and treatment of meningioma. ADVANCES IN KNOWLEDGE 1. We first constructed a radiomic nomogram to predict the meningioma grade.2. We compared the results of the clinical model, radiomics signature and radiomics nomogram.
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Affiliation(s)
- Chongfeng Duan
- Department of Radiology, The Affiliated Hospital of Qingdao
University, Qingdao,
China
| | - Xiaoming Zhou
- Department of Radiology, The Affiliated Hospital of Qingdao
University, Qingdao,
China
| | - Jiachen Wang
- Department of Radiology, The Affiliated Hospital of Qingdao
University, Qingdao,
China
| | - Nan Li
- Department of Information Management, The Affiliated Hospital
of Qingdao University, Qingdao,
China
| | - Fang Liu
- Department of Radiology, The Affiliated Hospital of Qingdao
University, Qingdao,
China
| | - Song Gao
- Department of Radiology, The Affiliated Hospital of Qingdao
University, Qingdao,
China
| | - Xuejun Liu
- Department of Radiology, The Affiliated Hospital of Qingdao
University, Qingdao,
China
| | - Wenjian Xu
- Department of Radiology, The Affiliated Hospital of Qingdao
University, Qingdao,
China
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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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Meningioma Radiomics: At the Nexus of Imaging, Pathology and Biomolecular Characterization. Cancers (Basel) 2022; 14:cancers14112605. [PMID: 35681585 PMCID: PMC9179263 DOI: 10.3390/cancers14112605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 05/20/2022] [Accepted: 05/23/2022] [Indexed: 12/10/2022] Open
Abstract
Simple Summary Meningiomas are typically benign, common extra-axial tumors of the central nervous system. Routine clinical assessment by radiologists presents some limitations regarding long-term patient outcome prediction and risk stratification. Given the exponential growth of interest in radiomics and artificial intelligence in medical imaging, numerous studies have evaluated the potential of these tools in the setting of meningioma imaging. These were aimed at the development of reliable and reproducible models based on quantitative data. Although several limitations have yet to be overcome for their routine use in clinical practice, their innovative potential is evident. In this review, we present a wide-ranging overview of radiomics and artificial intelligence applications in meningioma imaging. Abstract Meningiomas are the most common extra-axial tumors of the central nervous system (CNS). Even though recurrence is uncommon after surgery and most meningiomas are benign, an aggressive behavior may still be exhibited in some cases. Although the diagnosis can be made by radiologists, typically with magnetic resonance imaging, qualitative analysis has some limitations in regard to outcome prediction and risk stratification. The acquisition of this information could help the referring clinician in the decision-making process and selection of the appropriate treatment. Following the increased attention and potential of radiomics and artificial intelligence in the healthcare domain, including oncological imaging, researchers have investigated their use over the years to overcome the current limitations of imaging. The aim of these new tools is the replacement of subjective and, therefore, potentially variable medical image analysis by more objective quantitative data, using computational algorithms. Although radiomics has not yet fully entered clinical practice, its potential for the detection, diagnostic, and prognostic characterization of tumors is evident. In this review, we present a wide-ranging overview of radiomics and artificial intelligence applications in meningioma imaging.
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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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Hsieh HP, Wu DY, Hung KC, Lim SW, Chen TY, Fan-Chiang Y, Ko CC. Machine Learning for Prediction of Recurrence in Parasagittal and Parafalcine Meningiomas: Combined Clinical and MRI Texture Features. J Pers Med 2022; 12:jpm12040522. [PMID: 35455638 PMCID: PMC9032338 DOI: 10.3390/jpm12040522] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 03/09/2022] [Accepted: 03/22/2022] [Indexed: 01/04/2023] Open
Abstract
A subset of parasagittal and parafalcine (PSPF) meningiomas may show early progression/recurrence (P/R) after surgery. This study applied machine learning using combined clinical and texture features to predict P/R in PSPF meningiomas. A total of 57 consecutive patients with pathologically confirmed (WHO grade I) PSPF meningiomas treated in our institution between January 2007 to January 2019 were included. All included patients had complete preoperative magnetic resonance imaging (MRI) and more than one year MRI follow-up after surgery. Preoperative contrast-enhanced T1WI, T2WI, T1WI, and T2 fluid-attenuated inversion recovery (FLAIR) were analyzed retrospectively. The most significant 12 clinical features (extracted by LightGBM) and 73 texture features (extracted by SVM) were combined in random forest to predict P/R, and personalized radiomic scores were calculated. Thirteen patients (13/57, 22.8%) had P/R after surgery. The radiomic score was a high-risk factor for P/R with hazard ratio of 15.73 (p < 0.05) in multivariate hazards analysis. In receiver operating characteristic (ROC) analysis, an AUC of 0.91 with cut-off value of 0.269 was observed in radiomic scores for predicting P/R. Subtotal resection, low apparent diffusion coefficient (ADC) values, and high radiomic scores were associated with shorter progression-free survival (p < 0.05). Among different data input, machine learning using combined clinical and texture features showed the best predictive performance, with an accuracy of 91%, precision of 85%, and AUC of 0.88. Machine learning using combined clinical and texture features may have the potential to predict recurrence in PSPF meningiomas.
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Affiliation(s)
- Hsun-Ping Hsieh
- Department of Electrical Engineering, National Cheng Kung University, Tainan 70101, Taiwan; (H.-P.H.); (D.-Y.W.); (Y.F.-C.)
| | - Ding-You Wu
- Department of Electrical Engineering, National Cheng Kung University, Tainan 70101, Taiwan; (H.-P.H.); (D.-Y.W.); (Y.F.-C.)
| | - Kuo-Chuan Hung
- Department of Anesthesiology, Chi Mei Medical Center, Tainan City 71004, Taiwan;
- Department of Hospital and Health Care Administration, College of Recreation and Health Management, Chia Nan University of Pharmacy and Science, Tainan 71710, Taiwan
| | - Sher-Wei Lim
- Department of Neurosurgery, Chi Mei Medical Center, Chiali, Tainan 722, Taiwan;
- Department of Nursing, Min-Hwei College of Health Care Management, Tainan 73658, Taiwan
| | - Tai-Yuan Chen
- Department of Medical Imaging, Chi Mei Medical Center, Tainan 71004, Taiwan;
- Graduate Institute of Medical Sciences, Chang Jung Christian University, Tainan 71101, Taiwan
| | - Yang Fan-Chiang
- Department of Electrical Engineering, National Cheng Kung University, Tainan 70101, Taiwan; (H.-P.H.); (D.-Y.W.); (Y.F.-C.)
| | - Ching-Chung Ko
- Department of Medical Imaging, Chi Mei Medical Center, Tainan 71004, Taiwan;
- Department of Health and Nutrition, Chia Nan University of Pharmacy and Science, Tainan 71710, Taiwan
- Institute of Biomedical Sciences, National Sun Yat-Sen University, Kaohsiung 80424, Taiwan
- Correspondence:
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Galldiks N, Angenstein F, Werner JM, Bauer EK, Gutsche R, Fink GR, Langen KJ, Lohmann P. Use of advanced neuroimaging and artificial intelligence in meningiomas. Brain Pathol 2022; 32:e13015. [PMID: 35213083 PMCID: PMC8877736 DOI: 10.1111/bpa.13015] [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: 03/29/2021] [Revised: 06/09/2021] [Accepted: 08/02/2021] [Indexed: 01/04/2023] Open
Abstract
Anatomical cross‐sectional imaging methods such as contrast‐enhanced MRI and CT are the standard for the delineation, treatment planning, and follow‐up of patients with meningioma. Besides, advanced neuroimaging is increasingly used to non‐invasively provide detailed insights into the molecular and metabolic features of meningiomas. These techniques are usually based on MRI, e.g., perfusion‐weighted imaging, diffusion‐weighted imaging, MR spectroscopy, and positron emission tomography. Furthermore, artificial intelligence methods such as radiomics offer the potential to extract quantitative imaging features from routinely acquired anatomical MRI and CT scans and advanced imaging techniques. This allows the linking of imaging phenotypes to meningioma characteristics, e.g., the molecular‐genetic profile. Here, we review several diagnostic applications and future directions of these advanced neuroimaging techniques, including radiomics in preclinical models and patients with meningioma.
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Affiliation(s)
- Norbert Galldiks
- Department of Neurology, Faculty of Medicine, University Hospital Cologne, University of Cologne, Cologne, Germany.,Institute of Neuroscience and Medicine (INM-3, -4), Research Center Juelich, Juelich, Germany.,Center for Integrated Oncology (CIO), Universities of Aachen, Cologne, Germany
| | - Frank Angenstein
- Functional Neuroimaging Group, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Magdeburg, Germany.,Leibniz Institute for Neurobiology (LIN), Magdeburg, Germany.,Medical Faculty, Otto von Guericke University, Magdeburg, Germany
| | - Jan-Michael Werner
- Department of Neurology, Faculty of Medicine, University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Elena K Bauer
- Department of Neurology, Faculty of Medicine, University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Robin Gutsche
- Institute of Neuroscience and Medicine (INM-3, -4), Research Center Juelich, Juelich, Germany
| | - Gereon R Fink
- Department of Neurology, Faculty of Medicine, University Hospital Cologne, University of Cologne, Cologne, Germany.,Institute of Neuroscience and Medicine (INM-3, -4), Research Center Juelich, Juelich, Germany
| | - Karl-Josef Langen
- Institute of Neuroscience and Medicine (INM-3, -4), Research Center Juelich, Juelich, Germany.,Center for Integrated Oncology (CIO), Universities of Aachen, Cologne, Germany.,Department of Nuclear Medicine, University Hospital Aachen, Aachen, Germany
| | - Philipp Lohmann
- Institute of Neuroscience and Medicine (INM-3, -4), Research Center Juelich, Juelich, Germany.,Department of Stereotaxy and Functional Neurosurgery, Faculty of Medicine, University Hospital Cologne, University of Cologne, Cologne, Germany
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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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Duan C, Li N, Li Y, Liu F, Wang J, Liu X, Xu W. Comparison of different radiomic models based on enhanced T1-weighted images to predict the meningioma grade. Clin Radiol 2022; 77:e302-e307. [DOI: 10.1016/j.crad.2022.01.039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 01/11/2022] [Indexed: 11/24/2022]
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Li N, Mo Y, Huang C, Han K, He M, Wang X, Wen J, Yang S, Wu H, Dong F, Sun F, Li Y, Yu Y, Zhang M, Guan X, Xu X. A Clinical Semantic and Radiomics Nomogram for Predicting Brain Invasion in WHO Grade II Meningioma Based on Tumor and Tumor-to-Brain Interface Features. Front Oncol 2021; 11:752158. [PMID: 34745982 PMCID: PMC8570084 DOI: 10.3389/fonc.2021.752158] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 10/04/2021] [Indexed: 01/06/2023] Open
Abstract
Background Brain invasion in meningioma has independent associations with increased risks of tumor progression, lesion recurrence, and poor prognosis. Therefore, this study aimed to construct a model for predicting brain invasion in WHO grade II meningioma by using preoperative MRI. Methods One hundred seventy-three patients with brain invasion and 111 patients without brain invasion were included. Three mainstream features, namely, traditional semantic features and radiomics features from tumor and tumor-to-brain interface regions, were acquired. Predictive models correspondingly constructed on each feature set or joint feature set were constructed. Results Traditional semantic findings, e.g., peritumoral edema and other four features, had comparable performance in predicting brain invasion with each radiomics feature set. By taking advantage of semantic features and radiomics features from tumoral and tumor-to-brain interface regions, an integrated nomogram that quantifies the risk factor of each selected feature was constructed and had the best performance in predicting brain invasion (area under the curve values were 0.905 in the training set and 0.895 in the test set). Conclusions This study provided a clinically available and promising approach to predict brain invasion in WHO grade II meningiomas by using preoperative MRI.
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Affiliation(s)
- Ning Li
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Department of Radiology, Fuyang District First People's Hospital, Hangzhou, China
| | - Yan Mo
- Deepwise AI Lab, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, China
| | - Chencui Huang
- Deepwise AI Lab, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, China
| | - Kai Han
- Deepwise AI Lab, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, China
| | - Mengna He
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaolan Wang
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jiaqi Wen
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Siyu Yang
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Haoting Wu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Fei Dong
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Fenglei Sun
- Deepwise AI Lab, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, China
| | - Yiming Li
- Deepwise AI Lab, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, China
| | - Yizhou Yu
- Deepwise AI Lab, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, China
| | - Minming Zhang
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaojun Guan
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaojun Xu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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Radiomics machine learning study with a small sample size: Single random training-test set split may lead to unreliable results. PLoS One 2021; 16:e0256152. [PMID: 34383858 PMCID: PMC8360533 DOI: 10.1371/journal.pone.0256152] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Accepted: 08/01/2021] [Indexed: 12/23/2022] Open
Abstract
This study aims to determine how randomly splitting a dataset into training and test sets affects the estimated performance of a machine learning model and its gap from the test performance under different conditions, using real-world brain tumor radiomics data. We conducted two classification tasks of different difficulty levels with magnetic resonance imaging (MRI) radiomics features: (1) "Simple" task, glioblastomas [n = 109] vs. brain metastasis [n = 58] and (2) "difficult" task, low- [n = 163] vs. high-grade [n = 95] meningiomas. Additionally, two undersampled datasets were created by randomly sampling 50% from these datasets. We performed random training-test set splitting for each dataset repeatedly to create 1,000 different training-test set pairs. For each dataset pair, the least absolute shrinkage and selection operator model was trained and evaluated using various validation methods in the training set, and tested in the test set, using the area under the curve (AUC) as an evaluation metric. The AUCs in training and testing varied among different training-test set pairs, especially with the undersampled datasets and the difficult task. The mean (±standard deviation) AUC difference between training and testing was 0.039 (±0.032) for the simple task without undersampling and 0.092 (±0.071) for the difficult task with undersampling. In a training-test set pair with the difficult task without undersampling, for example, the AUC was high in training but much lower in testing (0.882 and 0.667, respectively); in another dataset pair with the same task, however, the AUC was low in training but much higher in testing (0.709 and 0.911, respectively). When the AUC discrepancy between training and test, or generalization gap, was large, none of the validation methods helped sufficiently reduce the generalization gap. Our results suggest that machine learning after a single random training-test set split may lead to unreliable results in radiomics studies especially with small sample sizes.
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Han X, Yang J, Luo J, Chen P, Zhang Z, Alu A, Xiao Y, Ma X. Application of CT-Based Radiomics in Discriminating Pancreatic Cystadenomas From Pancreatic Neuroendocrine Tumors Using Machine Learning Methods. Front Oncol 2021; 11:606677. [PMID: 34367940 PMCID: PMC8339967 DOI: 10.3389/fonc.2021.606677] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 07/05/2021] [Indexed: 02/05/2023] Open
Abstract
Objectives The purpose of this study aimed at investigating the reliability of radiomics features extracted from contrast-enhanced CT in differentiating pancreatic cystadenomas from pancreatic neuroendocrine tumors (PNETs) using machine-learning methods. Methods In this study, a total number of 120 patients, including 66 pancreatic cystadenomas patients and 54 PNETs patients were enrolled. Forty-eight radiomic features were extracted from contrast-enhanced CT images using LIFEx software. Five feature selection methods were adopted to determine the appropriate features for classifiers. Then, nine machine learning classifiers were employed to build predictive models. The performance of the forty-five models was evaluated with area under the curve (AUC), accuracy, sensitivity, specificity, and F1 score in the testing group. Results The predictive models exhibited reliable ability of differentiating pancreatic cystadenomas from PNETs when combined with suitable selection methods. A combination of DC as the selection method and RF as the classifier, as well as Xgboost+RF, demonstrated the best discriminative ability, with the highest AUC of 0.997 in the testing group. Conclusions Radiomics-based machine learning methods might be a noninvasive tool to assist in differentiating pancreatic cystadenomas and PNETs.
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Affiliation(s)
- Xuejiao Han
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Jing Yang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Melanoma and Sarcoma Medical Oncology Unit, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Jingwen Luo
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Pengan Chen
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Zilong Zhang
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Aqu Alu
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Yinan Xiao
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Xuelei Ma
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
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Ko CC, Zhang Y, Chen JH, Chang KT, Chen TY, Lim SW, Wu TC, Su MY. Pre-operative MRI Radiomics for the Prediction of Progression and Recurrence in Meningiomas. Front Neurol 2021; 12:636235. [PMID: 34054688 PMCID: PMC8160291 DOI: 10.3389/fneur.2021.636235] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 03/29/2021] [Indexed: 02/06/2023] Open
Abstract
Objectives: A subset of meningiomas may show progression/recurrence (P/R) after surgical resection. This study applied pre-operative MR radiomics based on support vector machine (SVM) to predict P/R in meningiomas. Methods: From January 2007 to January 2018, 128 patients with pathologically confirmed WHO grade I meningiomas were included. Only patients who had undergone pre-operative MRIs and post-operative follow-up MRIs for more than 1 year were studied. Pre-operative T2WI and contrast-enhanced T1WI were analyzed. On each set of images, 32 first-order features and 75 textural features were extracted. The SVM classifier was utilized to evaluate the significance of extracted features, and the most significant four features were selected to calculate SVM score for each patient. Results: Gross total resection (Simpson grades I–III) was performed in 93 (93/128, 72.7%) patients, and 19 (19/128, 14.8%) patients had P/R after surgery. Subtotal tumor resection, bone invasion, low apparent diffusion coefficient (ADC) value, and high SVM score were more frequently encountered in the P/R group (p < 0.05). In multivariate Cox hazards analysis, bone invasion, ADC value, and SVM score were high-risk factors for P/R (p < 0.05) with hazard ratios of 7.31, 4.67, and 8.13, respectively. Using the SVM score, an AUC of 0.80 with optimal cutoff value of 0.224 was obtained for predicting P/R. Patients with higher SVM scores were associated with shorter progression-free survival (p = 0.003). Conclusions: Our preliminary results showed that pre-operative MR radiomic features may have the potential to offer valuable information in treatment planning for meningiomas.
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Affiliation(s)
- Ching-Chung Ko
- Department of Medical Imaging, Chi-Mei Medical Center, Tainan, Taiwan.,Department of Health and Nutrition, Chia Nan University of Pharmacy and Science, Tainan, Taiwan
| | - Yang Zhang
- Department of Radiological Sciences, University of California, Irvine, Irvine, CA, United States
| | - Jeon-Hor Chen
- Department of Radiological Sciences, University of California, Irvine, Irvine, CA, United States.,Department of Radiology, E-DA Hospital, I-Shou University, Kaohsiung, Taiwan
| | - Kai-Ting Chang
- Department of Radiological Sciences, University of California, Irvine, Irvine, CA, United States
| | - 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
| | - Min-Ying Su
- Department of Radiological Sciences, University of California, Irvine, Irvine, CA, United States
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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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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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Kunimatsu A, Yasaka K, Akai H, Sugawara H, Kunimatsu N, Abe O. Texture Analysis in Brain Tumor MR Imaging. Magn Reson Med Sci 2021; 21:95-109. [PMID: 33692222 PMCID: PMC9199980 DOI: 10.2463/mrms.rev.2020-0159] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Texture analysis, as well as its broader category radiomics, describes a variety of techniques for image analysis that quantify the variation in surface intensity or patterns, including some that are imperceptible to the human visual system. Cerebral gliomas have been most rigorously studied in brain tumors using MR-based texture analysis (MRTA) to determine the correlation of various clinical measures with MRTA features. Promising results in cerebral gliomas have been shown in the previous MRTA studies in terms of the correlation with the World Health Organization grades, risk stratification in gliomas, and the differentiation of gliomas from other brain tumors. Multiple MRTA studies in gliomas have repeatedly shown high performance of entropy, a measure of the randomness in image intensity values, of either histogram- or gray-level co-occurrence matrix parameters. Similarly, researchers have applied MRTA to other brain tumors, including meningiomas and pediatric posterior fossa tumors. However, the value of MRTA in the clinical use remains undetermined, probably because previous studies have shown only limited reproducibility of the result in the real world. The low-to-modest generalizability may be attributed to variations in MRTA methods, sampling bias that originates from single-institution studies, and overfitting problems to a limited number of samples. To enhance the reliability and reproducibility of MRTA studies, researchers have realized the importance of standardizing methods in the field of radiomics. Another advancement is the recent development of a comprehensive assessment system to ensure the quality of a radiomics study. These two-way approaches will secure the validity of upcoming MRTA studies. The clinical use of texture analysis in brain MRI will be accelerated by these continuous efforts.
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Affiliation(s)
- Akira Kunimatsu
- Department of Radiology, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo.,Department of Radiology, The University of Tokyo Hospital
| | - Koichiro Yasaka
- Department of Radiology, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo.,Department of Radiology, The University of Tokyo Hospital
| | - Hiroyuki Akai
- Department of Radiology, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo.,Department of Radiology, The University of Tokyo Hospital
| | - Haruto Sugawara
- Department of Radiology, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo.,Department of Radiology, The University of Tokyo Hospital
| | - Natsuko Kunimatsu
- Department of Radiology, International University of Health and Welfare, Mita Hospital
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo
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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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Zhang T, Zhang Y, Liu X, Xu H, Chen C, Zhou X, Liu Y, Ma X. Application of Radiomics Analysis Based on CT Combined With Machine Learning in Diagnostic of Pancreatic Neuroendocrine Tumors Patient's Pathological Grades. Front Oncol 2021; 10:521831. [PMID: 33643890 PMCID: PMC7905094 DOI: 10.3389/fonc.2020.521831] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Accepted: 12/11/2020] [Indexed: 02/05/2023] Open
Abstract
Purpose To evaluate the value of multiple machine learning methods in classifying pathological grades (G1,G2, and G3), and to provide the best machine learning method for the identification of pathological grades of pancreatic neuroendocrine tumors (PNETs) based on radiomics. Materials and Methods A retrospective study was conducted on 82 patients with Pancreatic Neuroendocrine tumors. All patients had definite pathological diagnosis and grading results. Using Lifex software to extract the radiomics features from CT images manually. The sensitivity, specificity, area under the curve (AUC) and accuracy were used to evaluate the performance of the classification model. Result Our analysis shows that the CT based radiomics features combined with multi algorithm machine learning method has a strong ability to identify the pathological grades of pancreatic neuroendocrine tumors. DC + AdaBoost, DC + GBDT, and Xgboost+RF were very valuable for the differential diagnosis of three pathological grades of PNET. They showed a strong ability to identify the pathological grade of pancreatic neuroendocrine tumors. The validation set AUC of DC + AdaBoost is 0.82 (G1 vs G2), 0.70 (G2 vs G3), and 0.85 (G1 vs G3), respectively. Conclusion In conclusion, based on enhanced CT radiomics features could differentiate between different pathological grades of pancreatic neuroendocrine tumors. Feature selection method Distance Correlation + classifier method Adaptive Boosting show a good application prospect.
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Affiliation(s)
- Tao Zhang
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.,State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - YueHua Zhang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Xinglong Liu
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Hanyue Xu
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Chaoyue Chen
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Xuan Zhou
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Yichun Liu
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Xuelei Ma
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.,State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
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Liu Z, Zhu G, Jiang X, Zhao Y, Zeng H, Jing J, Ma X. Survival Prediction in Gallbladder Cancer Using CT Based Machine Learning. Front Oncol 2020; 10:604288. [PMID: 33330105 PMCID: PMC7729190 DOI: 10.3389/fonc.2020.604288] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 11/02/2020] [Indexed: 02/05/2023] Open
Abstract
Objective To establish a classifier for accurately predicting the overall survival of gallbladder cancer (GBC) patients by analyzing pre-treatment CT images using machine learning technology. Methods This retrospective study included 141 patients with pathologically confirmed GBC. After obtaining the pre-treatment CT images, manual segmentation of the tumor lesion was performed and LIFEx package was used to extract the tumor signature. Next, LASSO and Random Forest methods were used to optimize and model. Finally, the clinical information was combined to accurately predict the survival outcomes of GBC patients. Results Fifteen CT features were selected through LASSO and random forest. On the basis of relative importance GLZLM-HGZE, GLCM-homogeneity and NGLDM-coarseness were included in the final model. The hazard ratio of the CT-based model was 1.462(95% CI: 1.014–2.107). According to the median of risk score, all patients were divided into high and low risk groups, and survival analysis showed that high-risk groups had a poor survival outcome (P = 0.012). After inclusion of clinical factors, we used multivariate COX to classify patients with GBC. The AUC values in the test set and validation set for 3 years reached 0.79 and 0.73, respectively. Conclusion GBC survival outcomes could be predicted by radiomics based on LASSO and Random Forest.
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Affiliation(s)
- Zefan Liu
- Laboratory of Tumor Targeted and Immune Therapy, Clinical Research Center for Breast, West China Hospital, Sichuan University, Chengdu, China
| | - Guannan Zhu
- Laboratory of Tumor Targeted and Immune Therapy, Clinical Research Center for Breast, West China Hospital, Sichuan University, Chengdu, China
| | - Xian Jiang
- Laboratory of Tumor Targeted and Immune Therapy, Clinical Research Center for Breast, West China Hospital, Sichuan University, Chengdu, China
| | - Yunuo Zhao
- Laboratory of Tumor Targeted and Immune Therapy, Clinical Research Center for Breast, West China Hospital, Sichuan University, Chengdu, China
| | - Hao Zeng
- Laboratory of Tumor Targeted and Immune Therapy, Clinical Research Center for Breast, West China Hospital, Sichuan University, Chengdu, China
| | - Jing Jing
- Laboratory of Tumor Targeted and Immune Therapy, Clinical Research Center for Breast, West China Hospital, Sichuan University, Chengdu, China
| | - Xuelei Ma
- Laboratory of Tumor Targeted and Immune Therapy, Clinical Research Center for Breast, West China Hospital, Sichuan University, Chengdu, China.,State Key Laboratory of Biotherapy, Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
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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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Ni M, Wang L, Yu H, Wen X, Yang Y, Liu G, Hu Y, Li Z. Radiomics Approaches for Predicting Liver Fibrosis With Nonenhanced T 1 -Weighted Imaging: Comparison of Different Radiomics Models. J Magn Reson Imaging 2020; 53:1080-1089. [PMID: 33043991 DOI: 10.1002/jmri.27391] [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: 07/13/2020] [Revised: 09/24/2020] [Accepted: 09/25/2020] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Liver fibrosis is a common process resulting from various etiologies. Sustained progression of liver fibrosis leads to cirrhosis, even hepatocellular carcinoma. Thus, noninvasive staging of liver fibrosis is of clinical importance. Radiomics is an emerging approach for staging liver fibrosis. However, the feature selection methods and classifier models are complicated, and may result in a discrepancy of diagnostic performance owing to different radiomics models. PURPOSE To identify the optimal feature selection and classifier methods for predicting liver fibrosis by using nonenhanced T1 -weighted imaging. STUDY TYPE Prospective. ANIMAL MODEL Wistar rats, total 97. FIELD STRENGTH/SEQUENCE 3T, 3D T1 -weighted images with fast-spoiled gradient echo (FSPGR). ASSESSMENT Liver fibrosis rats were induced via subcutaneous injection of a mixture of carbon tetrachloride. Rats in the control group were injected with saline. Segmentation and feature extraction were performed by 3D slicer and the image biomarker explorer (IBEX) software package. Data preprocessing, feature selection, model building, and model comparative evaluation were conducted with Python. The liver fibrosis stage was determined by pathological examination. STATISTICAL TESTS Receiver operating characteristic curve, fuzzy comprehensive evaluation. RESULTS For discriminating between F0 and F1-2, F0 and F3-4, F0 and F1-4, F0-1 and F2-4, F0-2 and F3-4, and F0-3 and F4, the accuracies of 12 radiomics models were 77.27-90.91%, 73.33-86.67%, 80.56-91.67%, 74.07-88.89%, 76.47-88.24%, and 79.49-92.31%, respectively. The AUCs of the radiomics models were 0.86-0.97, 0.85-0.95, 0.89-0.97, 0.81-0.96, 0.82-0.93, and 0.85-0.96, respectively. The least absolute shrinkage and selection operator / support vector machine (LASSO-SVM) model had high AUCs of 0.93-0.97. For discriminating between F0 and F1-2, F0 and F3-4, F0 and F1-4, F0-1 and F2-4, and F0-2 and F3-4, the fuzzy comprehensive evaluation showed that the LASSO-SVM model had a high fuzzy score/order of 0.087-0.091/1. DATA CONCLUSION LASSO-SVM appears to be the optimal model for predicting liver fibrosis by using nonenhanced T1 -weighted imaging in a rodent model of liver fibrosis. LEVEL OF EVIDENCE 2. TECHNICAL EFFICACY STAGE 2.
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Affiliation(s)
- Ming Ni
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Lili Wang
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Haiyang Yu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Xiaoyi Wen
- Department of Statistics and Actuarial Science, University of Hong Kong, Hong Kong, China
| | - Yinghua Yang
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Guangzhen Liu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Yabin Hu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Zhiming Li
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
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Neromyliotis E, Kalamatianos T, Paschalis A, Komaitis S, Fountas KN, Kapsalaki EZ, Stranjalis G, Tsougos I. Machine Learning in Meningioma MRI: Past to Present. A Narrative Review. J Magn Reson Imaging 2020; 55:48-60. [PMID: 33006425 DOI: 10.1002/jmri.27378] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 09/10/2020] [Accepted: 09/10/2020] [Indexed: 12/28/2022] Open
Abstract
Meningioma is one of the most frequent primary central nervous system tumors. While magnetic resonance imaging (MRI), is the standard radiologic technique for provisional diagnosis and surveillance of meningioma, it nevertheless lacks the prima facie capacity in determining meningioma biological aggressiveness, growth, and recurrence potential. An increasing body of evidence highlights the potential of machine learning and radiomics in improving the consistency and productivity and in providing novel diagnostic, treatment, and prognostic modalities in neuroncology imaging. The aim of the present article is to review the evolution and progress of approaches utilizing machine learning in meningioma MRI-based sementation, diagnosis, grading, and prognosis. We provide a historical perspective on original research on meningioma spanning over two decades and highlight recent studies indicating the feasibility of pertinent approaches, including deep learning in addressing several clinically challenging aspects. We indicate the limitations of previous research designs and resources and propose future directions by highlighting areas of research that remain largely unexplored. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 2.
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Affiliation(s)
- Eleftherios Neromyliotis
- Departent of Neurosurgery, University of Athens Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Theodosis Kalamatianos
- Departent of Neurosurgery, University of Athens Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Athanasios Paschalis
- Department of Neurosurgery, School of Medicine, University of Thessaly, Larisa, Greece
| | - Spyridon Komaitis
- Departent of Neurosurgery, University of Athens Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Konstantinos N Fountas
- Department of Clinical and Laboratory Research, School of Medicine, University of Thessaly, Larisa, Greece
| | - Eftychia Z Kapsalaki
- Department of Clinical and Laboratory Research, School of Medicine, University of Thessaly, Larisa, Greece
| | - George Stranjalis
- Departent of Neurosurgery, University of Athens Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Ioannis Tsougos
- Department of Medical Physics, School of Medicine, University of Thessaly, Larisa, Greece
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Presurgical detection of brain invasion status in meningiomas based on first-order histogram based texture analysis of contrast enhanced imaging. Clin Neurol Neurosurg 2020; 198:106205. [PMID: 32932028 DOI: 10.1016/j.clineuro.2020.106205] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Revised: 08/29/2020] [Accepted: 09/01/2020] [Indexed: 01/12/2023]
Abstract
OBJECTIVE Invasion of brain parenchyma by meningioma can be a critical factor in surgical planning. The aim of this study was to determine the diagnostic utility of first-order texture parameters derived from both whole tumor and single largest slice of T1-contrast enhanced (T1-CE) images in differentiating meningiomas with and without brain invasion based on histopathology demonstration. METHODS T1-CE images of a total of 56 cases of grade II meningiomas with brain invasion (BI) and 52 meningiomas (37 grade I and 15 grade II) with no brain invasion (NBI) were analyzed. Filtration-based first-order histogram derived texture parameters were calculated both for whole tumor volume and largest axial cross-section. Random forest models were constructed both for whole tumor volume and largest axial cross-section individually and were assessed using a 5-fold cross validation with 100 repeats. RESULTS In detection of brain invasion, random forest model based on whole tumor segmentation had an AUC of 0.988 (95 % CI 0.976-1.00) with a cross validated value of 0.74 (95 % CI 0.45-0.96). For differentiation of grade I meningiomas from grade II meningiomas with brain invasion, the AUC was 0.999 (95 % CI 0.995-1.00) and 0.81 (95 % CI 0.61-0.99) in the training and validation cohorts, respectively. Similarly, when using only the single largest slice, the cross-validated AUC to distinguish BI versus NBI and BI versus grade I meningiomas was 0.67 (95 % CI 0.47, 0.92 and 0.78 (95 % CI 0.52, 0.95) respectively. CONCLUSION Radiomics based feature analysis applied on routine MRI post-contrast images may be helpful to predict presence of brain invasion in meningioma, possibly with better performance when comparing BI versus grade I meningiomas.
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Sun C, Dou Z, Wu J, Jiang B, Iranmanesh Y, Yu X, Li J, Zhou H, Zhong C, Peng Y, Zhuang J, Yu Q, Wu X, Yan F, Xie Q, Chen G. The Preferred Locations of Meningioma According to Different Biological Characteristics Based on Voxel-Wise Analysis. Front Oncol 2020; 10:1412. [PMID: 32974148 PMCID: PMC7472960 DOI: 10.3389/fonc.2020.01412] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2020] [Accepted: 07/06/2020] [Indexed: 12/12/2022] Open
Abstract
Objective: Meningiomas presented preferred intracranial distribution, which may reflect potential biological natures. This study aimed to analyze the preferred locations of meningioma according to different biological characteristics. Method: A total of 1,107 patients pathologically diagnosed with meningiomas between January 2012 and December 2016 were retrospectively analyzed. Preoperative MRI were normalized, and lesions were semiautomatically segmented. The stereospecific frequency and p value heatmaps were constructed to compare two biological phenotypes using two-tailed Fisher's exact test. Age, sex, WHO grades, extent of resection (EOR), recurrence, and immunohistochemical markers including p53, Ki67, epithelial membrane antigen (EMA), progesterone receptor (PR), and CD34 were statistically analyzed. Recurrence-free survival (RFS) were analyzed by Kaplan-Meier method. Result: Of 1,107 cases, convexity (20.8%), parasagittal (16.1%), and falx (11.4%) were the most predominant loci of meningiomas. The p-value heatmap suggested lesion predominance in the left frontal and occipital convexity among older patients while in the left sphenoid wing, and right falx, parasellar/cavernous sinus, and middle fossa among younger patients. Lesions located at anterior fossa and frontal structures were more frequently seen in the male while left parietal falx and tentorial regions, and right cerebellopontine angle in the female. Grades II and III lesions presented predominance in the frontal structures compared with grade I ones. Meningiomas at the left parasagittal sinus and falx, tentorium, intraventricular regions, and skull-base structures were significantly to receive subtotal resection. Lesions with p53 positivity were statistically located at the left frontal regions and parasellar/cavernous sinus, higher Ki67 index at the left frontal and bilateral parietal convexity and right parasellar/cavernous sinus, EMA negativity at the right olfactory groove and left middle fossa, and CD34 positivity at the sellar regions and right sphenoid wing. Tumor recurrence rates for grades I, II, and III were 2.8, 7.9, and 53.8%, respectively. Inferior RFS, higher Ki67 index, grades II and III, and a larger preoperative volume were observed in older patients. Recurrent meningiomas were more frequently found at the occipital convexity, tentorium, sellar regions, parasagittal sinus, and left sphenoid wing. Conclusion: The preferred locations of meningioma could be observed according to different biological characteristics, which might be helpful for clinical decisions.
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Affiliation(s)
- Chongran Sun
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Zhangqi Dou
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Jiawei Wu
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Biao Jiang
- Department of Radiology, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Yasaman Iranmanesh
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Xiaobo Yu
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Jianru Li
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Hang Zhou
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Chen Zhong
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Yucong Peng
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Jianfeng Zhuang
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Qian Yu
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Xinyan Wu
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Feng Yan
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Qi Xie
- School of Life Science, Westlake University, Hangzhou, China
| | - Gao Chen
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
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