Machine learning based radiomics approach for outcome prediction of meningioma – a systematic review

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Magnetic resonance imaging (MRI) is the preferred imaging modality for assessing tumor outcomes. Radiomics, an advanced imaging technique, assesses tumor heterogeneity and identifies predictive markers, offering a non-invasive alternative to biopsies. Machine learning (ML) based radiomics models enhances diagnostic and prognostic accuracy of tumors. Comprehensive review on ML-based radiomics models for predicting meningioma recurrence and survival are lacking. Hence, the aim of the study is to summarize the performance measures of ML based radiomics models in the prediction of outcomes such as progression/recurrence (P/R) and overall survival analysis of meningioma. Methods Data bases such as Scopus, Web of Science, PubMed, and Embase were used to conduct a literature search in order to find pertinent original articles that concentrated on meningioma outcome prediction. PRISMA (Preferred reporting items for systematic reviews and meta-analysis) recommendations were used to extract data from selected studies. Results Eight articles were included in the study. MRI Radiomics-based models combined with clinical and pathological data showed strong predictive performance for meningioma recurrence. A decision tree model achieved 90% accuracy, outperforming an apparent diffusion coefficient (ADC) based model (83%). A support vector machine (SVM) model reached an area under curve (AUC) of 0.80 with radiomic features, improving to 0.88 with ADC integration. A combined clinico-pathological radiomics model (CPRM) achieved an AUC of 0.88 in testing. Key predictors of recurrence include ADC values, radiomic scores, ki-67 index, and Simpson grading. For predicting overall survival analysis of meningioma, the combined clinicopathological and radiomic features achieved an AUC of 0.78. Conclusion Integrating radiomics with clinical and pathological data through ML models greatly improved the outcome prediction for meningioma. These ML models surpass conventional MRI in predicting meningioma recurrence and aggressiveness, providing crucial insights for personalized treatment and surgical planning. " } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/14-330/v1", "name": "Machine learning based radiomics approach for outcome prediction of..." } } ] } Home Browse Machine learning based radiomics approach for outcome prediction of... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article S S, Pendem S, K P et al. Machine learning based radiomics approach for outcome prediction of meningioma – a systematic review [version 1; peer review: 2 approved] . F1000Research 2025, 14 :330 ( https://doi.org/10.12688/f1000research.162306.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Systematic Review Machine learning based radiomics approach for outcome prediction of meningioma – a systematic review [version 1; peer review: 2 approved] Saroh S https://orcid.org/0009-0004-5271-9663 1 , Saikiran Pendem https://orcid.org/0000-0001-7933-1192 1 , Prakashini K 2 , [...] Shailesh Nayak S https://orcid.org/0000-0001-6548-8092 1 , Girish R Menon https://orcid.org/0000-0002-1849-0453 3 , Priyanka - https://orcid.org/0000-0002-9792-6242 1 , Divya B https://orcid.org/0000-0002-9523-5229 4 Saroh S https://orcid.org/0009-0004-5271-9663 1 , Saikiran Pendem https://orcid.org/0000-0001-7933-1192 1 , [...] Prakashini K 2 , Shailesh Nayak S https://orcid.org/0000-0001-6548-8092 1 , Girish R Menon https://orcid.org/0000-0002-1849-0453 3 , Priyanka - https://orcid.org/0000-0002-9792-6242 1 , Divya B https://orcid.org/0000-0002-9523-5229 4 PUBLISHED 25 Mar 2025 Author details Author details 1 Department of Medical Imaging Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India 2 Department of Radio Diagnosis and Imaging, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India 3 Department of Neurosurgery, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India 4 Department of Electronics and Communication Engineering, Manipal institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India Saroh S Roles: Methodology, Resources, Writing – Original Draft Preparation Saikiran Pendem Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Prakashini K Roles: Conceptualization, Supervision Shailesh Nayak S Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Project Administration, Supervision, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Girish R Menon Roles: Methodology, Supervision, Visualization Priyanka - Roles: Supervision, Writing – Original Draft Preparation Divya B Roles: Supervision OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Artificial Intelligence and Machine Learning gateway. This article is included in the Manipal Academy of Higher Education gateway. This article is included in the AI in Medicine and Healthcare collection. Abstract Introduction Meningioma is the most common brain tumor in adults. Magnetic resonance imaging (MRI) is the preferred imaging modality for assessing tumor outcomes. Radiomics, an advanced imaging technique, assesses tumor heterogeneity and identifies predictive markers, offering a non-invasive alternative to biopsies. Machine learning (ML) based radiomics models enhances diagnostic and prognostic accuracy of tumors. Comprehensive review on ML-based radiomics models for predicting meningioma recurrence and survival are lacking. Hence, the aim of the study is to summarize the performance measures of ML based radiomics models in the prediction of outcomes such as progression/recurrence (P/R) and overall survival analysis of meningioma. Methods Data bases such as Scopus, Web of Science, PubMed, and Embase were used to conduct a literature search in order to find pertinent original articles that concentrated on meningioma outcome prediction. PRISMA (Preferred reporting items for systematic reviews and meta-analysis) recommendations were used to extract data from selected studies. Results Eight articles were included in the study. MRI Radiomics-based models combined with clinical and pathological data showed strong predictive performance for meningioma recurrence. A decision tree model achieved 90% accuracy, outperforming an apparent diffusion coefficient (ADC) based model (83%). A support vector machine (SVM) model reached an area under curve (AUC) of 0.80 with radiomic features, improving to 0.88 with ADC integration. A combined clinico-pathological radiomics model (CPRM) achieved an AUC of 0.88 in testing. Key predictors of recurrence include ADC values, radiomic scores, ki-67 index, and Simpson grading. For predicting overall survival analysis of meningioma, the combined clinicopathological and radiomic features achieved an AUC of 0.78. Conclusion Integrating radiomics with clinical and pathological data through ML models greatly improved the outcome prediction for meningioma. These ML models surpass conventional MRI in predicting meningioma recurrence and aggressiveness, providing crucial insights for personalized treatment and surgical planning. READ ALL READ LESS Keywords Meningioma, recurrence, outcome prediction, overall survival analysis, machine learning, radiomic features Corresponding Author(s) Saikiran Pendem ( [email protected] ) Shailesh Nayak S ( [email protected] ) Close Corresponding authors: Saikiran Pendem, Shailesh Nayak S Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2025 S S et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: S S, Pendem S, K P et al. Machine learning based radiomics approach for outcome prediction of meningioma – a systematic review [version 1; peer review: 2 approved] . F1000Research 2025, 14 :330 ( https://doi.org/10.12688/f1000research.162306.1 ) First published: 25 Mar 2025, 14 :330 ( https://doi.org/10.12688/f1000research.162306.1 ) Latest published: 25 Mar 2025, 14 :330 ( https://doi.org/10.12688/f1000research.162306.1 ) Meningioma is the most prevalent type of cancer identified in the brain which is mostly affecting adults. Approximately 7.86 individuals per 100,000 are diagnosed annually with meningioma globally, which is significantly attributed to the credibility of advancements in imaging technology for tumour diagnosis in the last three decades. 1 – 2 The World Health Organization (WHO) has classified meningiomas into three distinct grades of which the Grade I meningiomas are represented by their slow growth, while Grade II meningiomas are categorised as atypical and Grade III meningiomas represent the most fatal form, being malignant in nature. Thus, both Grade II and Grade III meningiomas display a high rate of recurrence following routine treatment, which may obviously end up with a reduced life expectancy. These facts shed light on the early detection and subsequent treatments which are quite mandatory for the precise diagnosis of meningiomas. 3 – 5 After meningioma surgery, overall survival at 5 and 10 years respectively were 92.6% and 85%. Survival is prolonged in women, younger adults and those with convexity and benign tumor. 6 Magnetic Resonance Imaging (MRI) is an emerging and most effective method for detecting meningiomas and monitoring specific medical conditions. Conventional MR imaging findings such as tumor size, bone invasion, and parasagittal location have all been identified as important imaging parameters related to progression/recurrence (P/R) in meningiomas. 7 – 9 Radiomics incorporates a range of advanced techniques used to conduct a comprehensive analysis of medical images. Radiomics is primarily intended to analyse the characteristics of tissues and lesions, with respect to their shape and heterogeneity, and monitor changes over a period through serial imaging, during treatment period. 10 – 12 Examining tissue heterogeneity is crucial, as genomic studies highlight its role as prognostic factor for survival and a challenge in cancer management. Radiomic features strongly correlate with cellular heterogeneity indices. Unlike traditional biopsies, which sample limited tumour areas, radiomics enables heterogeneity assessment across the entire tumour. Leveraging large datasets, radiomics identifies unrecognized markers and patterns related to disease progression and treatment. Additionally, integrating radiomic data with clinical, histological, genomic, and other datasets through machine learning enhances its diagnostic and prognostic utility. 13 – 18 There are no reviews conducted on evaluating the performance of machine learning (ML) based radiomic models in the prediction of recurrence and overall survival for meningioma. Hence, the aim of the study is to summarize the performance of the ML based radiomics models in the prediction of outcomes such as P/R and overall survival analysis for meningioma. Methods PRISMA 19 (Preferred reporting items for systematic reviews and meta-analysis) standards were followed in conducting the review. (PRISMA checklist- Extended data) Literature search strategy Using databases such as Scopus, Web of Science, PubMed and Embase a literature search was conducted to identify suitable original articles ( Table 1 ). MesH terms including “Radiomics”, “Machine learning”, “Meningioma”, “Outcome prediction”, “Recurrence”, “Overall survival analysis” along with Boolean operators like as “AND” “OR” (extended data). Only English language research with adult subjects having MRI examinations were included in the search criteria. Table 1. Study retrieval method from database. Database Number of studies retrieved Total PubMed 53 157 Scopus 32 Web of Science 32 Embase 40 Selection criteria Inclusion criteria The articles which included the performance of ML models in the prediction of outcomes such as P/R and overall survival analysis for meningioma were included. Exclusion criteria Exclusion criteria include the ML based radiomics studies of other brain tumors, articles without radiomics or machine learning approaches. Articles included grading of meningioma, lacking outcome prediction were excluded. Data extraction Two researchers independently reviewed each article, extracting details such as the authors name, publication year, sample size, tumour grades, classifiers used, and study outcomes. Any disagreements between the reviewers were settled by a third researcher. Quality assessment The quality of the studies was assessed using Radiomics quality score (RQS) tool. 20 This tool assesses the ML based radiomic studies based on criteria with 16 items (underlying data) focusing on methodological rigor, reproducibility, and clinical relevance of radiomic studies. Results A total of eight articles were included ( Figure 1 ). Figure 1. Flow chart for study selection. Study selection 157 studies were found in the early database search. The titles and abstracts of 94 articles were examined after 63 duplicates were removed, and 81 articles that did not fit the inclusion criteria were excluded. Twelve articles were left in the end. After evaluating the final text of these twelve articles for eligibility, four were disqualified for lacking adequate information such as comprehensive tables that measured the ML models performance. In the end, the final review contained eight articles. Characteristics of selected studies The review encompasses research conducted in five different countries: Taiwan (n=3), China (n=2), South Korea (n=1), Germany (n=1), and the United States of America (n=1). A total of five studies utilized Siemens Healthcare MRI Scanners (Magnetom, Amira, Avanto, Aera, Sonata, and United Imaging 750) operating at field strengths of 1.5 T and 3 T, while three studies employed the GE Health Care MRI Scanner (Discovery MR 750) with a field strength of 3 T. The cumulative sample size across all studies amounted to 1237 participants. Eight articles included in the review followed retrospective analysis ( Table 2 ). Table 2. showing the characteristics of selected studies. S/No Author & Year Purpose Sample size Grade Classifiers used Outcome of the study Recurrence 1 Zhang et al. 21 2019 Prognosis/recurrence 60 patients I, II and III Random forest algorithm This study demonstrated that MR radiomic analysis significantly improves P/R prediction in SBM compared to traditional ADC-based methods. Preoperative radiomic assessments offer valuable insights for treatment planning, including tumor resection, adjuvant radiotherapy, and follow-up imaging schedules. 2 Ko et al., 22 2021 Prognosis/recurrence 128 patients I SVM model The SVM score from pre-operative MR radiomic features effectively predicted P/R in meningiomas. This approach offers crucial insights for treatment planning, including tumor resection, adjuvant radiotherapy, and follow-up imaging schedules. 3 Park et al., 23 2022 Recurrence 155 patients II LASSO logistic classifier Multiparametric MRI radiomics, combined with clinicopathological profiles, aids in predicting recurrence in atypical meningiomas. Radiomics identifies high-risk patients who may benefit from intensified treatment, making it a valuable tool for imaging grade 2 meningiomas. 4 Hsieh et al. 24 2022 Prognosis/recurrence 57 patients I Light GBM, SVM, Random Forest classifier ML-derived radiomic scores offer valuable insights to refine treatment strategies, including surgical resection, adjuvant radiotherapy, and MRI follow-up planning. 5 Ren et al. 25 2024 Recurrence 224 patients I, II and III Cox Proportional Hazards Model This study created a clinico-radiomics model (CRM) to predict recurrence in atypical meningiomas using clinical and MRI radiomics data. The CRM showed strong performance across validations and effectively identified high- and low-risk patients. 6 He et al. 26 2024 Recurrence/prognosis 169 patients I, II and III LASSO and Cox Survival Analysis The study developed an ML model combining clinicopathological data and MRI radiomics to predict P/R in meningioma patients, achieving an AUC of 0.88. The CPRM model aids personalized treatment by identifying high-risk patients, optimizing surgical and radiotherapy plans, and scheduling closer follow-ups. 7 Kalasauskas et al. 27 2024 Recurrence 226 patients I and II Random Forest, SVM Neural Networks, Logistic Regression, AdaBoost Gradient Boosting, k-nearest-neighbours, Naïve Bayes This study showed that radiomic analysis effectively assesses tumor aggressiveness and CNS WHO grade, outperforming evaluations by neuroradiologists and semantic feature scores. Overall survival 8 Morin et al. 28 2019 Overall survival 218 patients I, II and III Random forest models The study showed that combining clinical, radiologic, and radiomic features improved prediction accuracy for meningioma grade, local failure, and overall survival. Key imaging markers, such as ADC hypo-intensity and tumor sphericity, help identify high-risk, aggressive tumors. Quality assessment Due to the flaws in multiple segmentations, no Phantom studies, scanning at various times, cutoff analyses, calibration statistics, no prospective data collection, and cost effectiveness analysis, all of the studies received scores below 50% ( Table 3 ). Table 3. Showing the radiomic quality scores of studies included. Study & Year Item 1 Item 2 Item 3 Item 4 Item 5 Item 6 Item 7 Item 8 Item 9 Item 10 Item 11 Item 12 Item 13 Item 14 Item 15 Item 16 RQS Total RQS Percentage Zhang et al. 21 2019 +1 0 0 0 +3 0 0 +1 +1 0 0 +2 +2 +2 0 0 14 38.88% Ko et al. 22 2021 +1 0 0 0 +3 +1 +1 +1 +1 0 0 -5 +2 +2 0 0 7 19.44% Park et al. 23 2022 +1 0 0 0 +3 +1 +1 +1 +1 0 0 +2 +2 +2 0 0 14 38.88% Hsieh et al. 24 2022 +1 0 0 0 +3 +1 +1 +1 +1 0 0 +2 +2 +2 0 0 14 38.88% Ren et al. 25 2024 +1 0 0 0 +3 +1 +1 +1 +1 0 0 +2 +2 +2 0 0 14 38.88% He et al. 26 2024 +1 +1 0 0 +3 +1 +1 +1 +1 0 0 +2 +2 +2 0 0 15 41.67% Kalasauskas et al. 27 2024 +1 0 0 0 +3 +1 +1 +1 +1 0 0 +2 +2 +2 0 0 14 38.88% Morin et al. 28 2019 +1 0 0 0 +3 +1 +1 +1 +2 0 0 +3 +2 +2 0 0 16 44.44% Summary of performance measures of various models of included studies were provided in ( Table 4 ). Table 4. Showing the performance measures of various models across different studies. S.No. Author/Year Model/ML with highest accuracy/validation dataset AUC Accuracy F1 score Sensitivity Specificity Recall MCC Progression/Recurrence 1 Zang et al. 21 2019 Radiomics + ADC correlation (Decision tree) - 90 - - - - - ADC 0.88 83 2 Ko et al. 22 2021 Radiomics + ADC (SVM) 0.88 - - - - - - SVM 0.80 - - - - - - ADC 0.73 - - - - - - 3 Park et al. 23 2022 Clinicopathological 0.61 81.3 - 33.3 89.1 - - Clinicopathological +radiomics 0.77 70.3 - 66.7 70.9 - - 4 Hsieh et al. 24 2022 Clinical Data 0.78 0.86 - 0.83 - 0.67 - Radiomic features 0.79 0.88 - 0.83 - 0.63 - Clinical+ Radiomics model (Random forest) 0.88 0.91 - 0.85 - 0.83 - 5 Ren et al. 25 2024 Clinicopathological+ radiomics (External validation) 0.840 - - - - - 6 He et al. 26 2024 Clinicopathological 0.817 0784 0.853 0.647 - Combined radiomics 0.84 0.82 0.81 0.83 - Clinicopathological radiomics 0.88 0.86 0.88 0.81 - 7 Kalasauskas et al. 27 2024 Radiomics (Random Forest) 0.892 88.2 0.750 - - - 0.717 Semantic scores 0.65 - - - - - - Radiological 0.54 - - - - - - Overall survival analysis 8 Morin et al. 28 2019 Imaging models Radiologic 0.65 0.63 - - - - - Radiomics 0.75 0.67 - - - - - Combined model - Radiological + radiomics 0.74 0.67 - - - - - Demographic + therapy + grade 0.71 0.64 - - - - - Demographic + radiologic +radiomic 0.77 0.7 - - - - - Demographic + radiologic +radiomic + therapy + grade 0.77 0.69 - - - - - Performance measures of ML models for predicting meningioma recurrence and overall survival analysis For predicting meningioma P/R: A study by Zhang et al. 21 for predicting recurrence in skull base meningiomas (SBM), a radiomics-based decision tree model using preoperative MRI features demonstrated 90% accuracy, outperforming the apparent diffusion coefficient (ADC) model, which achieved accuracy of 83%. The most significant predictive features included T1 maximum probability, T1 cluster shade, and ADC correlation, selected via a random forest algorithm. The radiomics model identified 18 true positive, 36 true negative, 3 false positive, and 3 false negative cases, highlighting its strong predictive capability. Ko et al. 22 for predicting progression/recurrence in meningiomas reported that a support vector machine (SVM) based radiomics model demonstrating strong predictive performance. The model, using preoperative MRI radiomic features, achieved an area under curve (AUC) of 0.80 with an optimal SVM score cutoff of 0.224. When combined with ADC, the predictive power improved to an AUC of 0.88. Multivariate analysis identified bone (Hazard ratio (HR):7.31, p<0.05), low ADC values (HR:4.67, p<0.05), and high SVM scores (HR:8.13,p<0.05) as significant risk factors for meningioma recurrence. Patients with higher SVM scores showed shorter progression-free survival (p=0.003), highlighting the potential of radiomics-based AI models for refining risk stratification and guiding treatment decisions. Another study by park et al. 23 for predicting recurrence in WHO grade 2 meningiomas, the combined clinicopathological and radiomics model (CPRM) outperformed the clinicopathological model (CPM) alone. Compared to the CPM model (AUC:0.61, accuracy 81.3%, sensitivity: 33.3%), the combined clinic-pathological and radiomics model (CPRM) showed good performance with AUC:0.77, accuracy 70.03%, sensitivity 66.7% and specificity of 70.9%) in test data. This model also identified high-risk patients who significantly benefited from adjuvant radiotherapy (ART), with a 5-year progression free survival of 92.3% compared to 56.8% without ART. A study by Hsieh et al. 24 for predicting recurrence in parasagittal and parafalcine meningiomas, the ML model combining clinical and MRI texture features showed the highest predictive performance. The study found that high radiomic scores (AUC:0.91), low ADC values (AUC:0.82), and large tumor diameter (AUC:0.69) were significant predictors of recurrence, and multivariate analysis showed that score cut off of 0.269 was a strong risk factor of early tumor recurrence with HR of 15.73 (p<0.05). This model outperformed models based on clinical data alone (AUC:0.78) and MRI texture features alone (AUC:0.79). A study by Ren et al. 25 found that hybrid clinical-radiomics model that integrated radiomics features with clinicopathological data performed well in predicting recurrence in atypical meningiomas. In the training cohort, the model’s integrated AUC was 0.858 (0.802-0.915); in the internal validation cohort, AUC was 0.781; and in the external validation cohort it was 0.840. The ki-67 index, surgical history, radiomics signature, and degree of resection were all found to be independent predictors of atypical meningioma recurrence. In a study by He et al. 26 the greatest predictive performance was shown by clinicopathological-radiomics model (CPRM) that combined clinicopathological characteristics with radiomic features from preoperative MRI to predict meningioma recurrence. With an AUC of 0.88 in the test set, the model outperformed the radiomics-only model (AUC 0.85) and the clinicopathological model (AUC:0.86). Key predictors included radiation history, Simpson grading, WHO grading, and ki-67 index. Kalasauskas et al. 27 showed that for meningioma recurrence prediction, the best performing ML was random forest with information gain feature selection of 25 radiomic features. This model achieved an AUC of 0.892, accuracy of 88.2% (±10.84%), an F1 score of 0.750, and a Matthews correlation coefficient (MCC) of 0.717. While the support vector machine (SVM) model achieved a slightly higher AUC of 0.896, it had lower F1-score (0.182) and MCC (0.270), making random forest the preferred choice. Compared to conventional radiological assessment (AUC:0.53-0.54) and semantic feature scores (AUC:0.65), radiomics-based models demonstrated significantly superior predictive performance. For predicting overall survival analysis A study by Morin et al. 28 for predicting local failure and overall survival in meningiomas, an integrated prognostic model combining clinical, radiologic, and radiomic features demonstrated improved predictive performance. The model achieved an AUC of 0.78 for overall survival analysis (OS). Outperforming models based on clinical features alone. Key prognostic imaging-derived factors included ADC hypo intensity (HR:1.96, p=0.005), low sphericity (HR:2.0, p=0.02), multifocality (HR:2.13, p=0.003) and venous sinus involvement (HR:1.79, p=0.04). Discussion Meningioma is the most prevalent brain tumor, primarily affecting adults, and it occurs with incidence of 7.86 per 100,000 people worldwide each year. 1 – 2 MRI is the recommended imaging modality, and recurrence is associated with traditional imaging criteria such as tumor size and bone invasion. 7 – 9 Radiomics, provides a non-invasive substitute for biopsies by evaluating tumor heterogeneity and identifying prognostic markers. Using machine learning to integrate radiomics with clinical and pathological data may improve the accuracy of diagnosis and prognosis of tumors. 10 – 12 However there is lack of systematic review of ML-base radiomics models for meningioma survival and recurrence prediction. Zhang et al. 21 and Kalasauskas et al. 27 reported the effectiveness of radiomics-based ML models in predicting tumor recurrence and aggressiveness in meningiomas. Using radiomic features from contrast enhanced T1Weighted image and ADC maps as primary predictors, Zhang et al. 21 created a random forest-based radiomics model that achieved 90% accuracy in predicting P/R in SBM. Their research showed that radiomics performed better than traditional MR imaging in objective and quantitative tumor categorization, offering useful prognostic information for decisions about surgery and treatment. Kalasauskas et al. 27 reported that radiomic analysis is more accurate than traditional neurological evaluation in predicting tumor recurrence and CNS WHO grade. The ML model performed well across heterogeneous MRI datasets, emphasizing the importance of the data preprocessing in lowering variability across varied MRI datasets. According to their study, preoperative evaluation based on radiomics may help with treatment stratification by directing regarding the extent of resection and follow-up imaging. Both the studies support the clinical promise of radiomics for better surgical planning, recurrence prediction, and tailored meningioma care. The studies by Park et al., 23 Hsieh et al., 24 and Ko et al. 22 highlighted the role of radiomics-based ML models in predicting tumor recurrence in meningiomas and refining treatment strategies. Park et al. 23 demonstrated that integrating radiomic features with clinicopathologic data significantly improved recurrence prediction in grade 2 meningiomas. Their model identified high-risk patients who benefited from ART, supporting the use of radiomics for non-invasive risk stratification and personalized treatment decisions. Hsieh et al. 24 introduced a radiomic score-based approach, combining clinical and MRI texture features, achieving an AUC of 0.88 in predicting P/R in parasagittal and parafalcine meningiomas. Their study confirmed that integrating clinical and imaging data enhanced predictive accuracy compared to using either alone. Ko et al. 22 developed an SVM-based radiomics model, identifying adjacent bone invasion, low ADC values, and high SVM scores as key high-risk factors for recurrence. Their model outperformed traditional ADC-based assessments and emphasized radiomics’ potential in preoperative risk assessment. All three studies support radiomics as a valuable prognostic tool for identifying high-risk meningioma patients, optimizing surgical and ART decisions, and guiding personalized follow-up strategies. The studies by Ren et al. 25 and He et al. 26 reinforce the predictive value of radiomics-based machine learning models in assessing tumor recurrence and progression-free survival (PFS) in meningiomas. Ren et al. 25 developed a clinicopathological-radiomics model (CPRM) incorporating radiomic features from T1Contrast and T2-FLAIR MRI alongside clinical factors, achieving an AUC of 0.858 for predicting recurrence in atypical meningiomas across multiple neurosurgical centers. Their model proved the utility of combining multimodal data for precision oncology, outperforming previous clinical and radiomics-only models. He et al. 26 developed a complete clinicopathological-radiomics model (CPRM) that integrated three radiomics signatures (T1, eT1, T2), WHO grading, Simpson grading, Ki-67 index, and radiotherapy history, achieving AUCs of 0.93 for training dataset and 0.88 for testing dataset. Their study produced a clinical normogram for personalized risk assessment and performed better than single-modality radiomics models (AUC:0.70-0.85). However, Simpson grading and Ki-67 remained the most significant predictors for progression free survival, suggesting that postoperative factors are crucial for long-term monitoring. Both studies highlighted the superior performance of integrated radiomics-clinicopathological models over traditional assessments, supporting their use in preoperative risk stratification and treatment planning for meningiomas. The study by Morin et al. 28 highlighted the prognostic significance of radiologic and radiomic features in predicting meningioma grade, local failure (LF), and overall survival (OS). Their integrated model, combining preoperative MRI features with clinical variables, demonstrated significant predictive performance. ADC hypointensity and low tumor sphericity were identified as key radiomic predictors associated with high-grade meningiomas, poor local control, and reduced OS. Conventional radiological imaging features such as peritumoral edema, indistinct tumor margins, and absence of a CSF cleft sign, were associated with higher tumor grade and worse outcomes. The review has few limitations. Firstly, meta-analysis was not conducted due to heterogeneity of data, including different MRI protocols and patient populations. Secondly, studies included had small sample sizes, limiting statistical power. Thirdly, most studies were retrospective, introducing potential biases. Conclusion Our review suggests that radiomics greatly improved meningioma diagnostic and prognostic accuracy when combined with clinical and pathological data through machine learning. ML models based on radiomics performed better than traditional MRI in predicting tumor aggressiveness and recurrence, offering important information for surgical planning and individualized treatment. The combination of radiomics and clinicopathological data offers a non-invasive substitute for biopsies helping in risk stratification and guiding decisions on adjuvant treatment and follow-up plans. Overall, radiomics holds great potential for improving the management and outcomes of meningioma patients. Ethics and consent Ethical approval and consent were not required Reporting guidelines Fig share: https://doi.org/10.6084/m9.figshare.28557743.v1 . Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). Data availability Underlying data No data are associated with this article Extended data Figshare: Meningioma Recurrence. https://doi.org/10.6084/m9.figshare.28557743.v1 . 29 This project contains the following extended data: 1. Supplementary File 1 (PRISMA_2020_check list) 2. Supplementary File 2 (MeSH terms used) 3. Supplementary File 3 (Radiomics quality score questions) 4. Supplementary File 4 (PRISMA Flow diagram) Acknowledgements There are no acknowledgments to be made by the authors References 1. Gittleman HR, Ostrom QT, Rouse CD, et al. : Trends in central nervous system tumor incidence relative to other common cancers in adults, adolescents, and children in the United States, 2000 to 2010. Cancer. 2015; 121 (1): 102–112. PubMed Abstract | Publisher Full Text | Free Full Text 2. Ostrom QT, Gittleman H, Liao P, et al. : CBTRUS Statistical Report: primary brain and other central nervous system tumors diagnosed in the United States in 2010–2014. 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Publisher Full Text Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 25 Mar 2025 ADD YOUR COMMENT Comment Author details Author details 1 Department of Medical Imaging Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India 2 Department of Radio Diagnosis and Imaging, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India 3 Department of Neurosurgery, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India 4 Department of Electronics and Communication Engineering, Manipal institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India Saroh S Roles: Methodology, Resources, Writing – Original Draft Preparation Saikiran Pendem Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Prakashini K Roles: Conceptualization, Supervision Shailesh Nayak S Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Project Administration, Supervision, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Girish R Menon Roles: Methodology, Supervision, Visualization Priyanka - Roles: Supervision, Writing – Original Draft Preparation Divya B Roles: Supervision Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (1) version 1 Published: 25 Mar 2025, 14:330 https://doi.org/10.12688/f1000research.162306.1 Copyright © 2025 S S et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article S S, Pendem S, K P et al. Machine learning based radiomics approach for outcome prediction of meningioma – a systematic review [version 1; peer review: 2 approved] . F1000Research 2025, 14 :330 ( https://doi.org/10.12688/f1000research.162306.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 25 Mar 2025 Views 0 Cite How to cite this report: Barde M. Reviewer Report For: Machine learning based radiomics approach for outcome prediction of meningioma – a systematic review [version 1; peer review: 2 approved] . F1000Research 2025, 14 :330 ( https://doi.org/10.5256/f1000research.178486.r373394 ) The direct URL for this report is: https://f1000research.com/articles/14-330/v1#referee-response-373394 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 08 Apr 2025 Mustapha Barde , Bayero University, Kano, Kano, Nigeria Approved VIEWS 0 https://doi.org/10.5256/f1000research.178486.r373394 An impressive systematic review was conducted by the authors, to evaluate the performance of machine learning based radiomics approach in the prediction of meningiomas. It was reported that based on the studies been reviewed MRI based machine learning, based on ... Continue reading READ ALL An impressive systematic review was conducted by the authors, to evaluate the performance of machine learning based radiomics approach in the prediction of meningiomas. It was reported that based on the studies been reviewed MRI based machine learning, based on decision tree models have more accuracy (90%) in the prediction of meningiomas reoccurrence. Surpassing apparent diffusion coefficient-based models (83%). from MRI studies. It was reported that radiomics combined with clinicopathological data via Machine learning gives improved results in meningioma prediction compared to conventional MRI results. Also based on the review the meningioma survival analysis prediction, the radiomics and clinicopathological features attained an area under the curve of 0.78. General comment . The review is commendable. However, addressing the below minor corrections will enhance clarity and comprehension. Minor corrections The below points should be addressed please for clarity 1. The total number of search articles from the databases should be stated in the abstract methodology, and the number excluded. 2. In figure 1, an asterisk was used in records identified and records excluded. A key will guide in noting the rationale for the asterisk or ignoring the asterisk if not crucial. 3. In the exclusion criteria, it was stated that articles that report grading were excluded. However, in Table 2. The grade column was included, with the relevant grading based on the studies reviewed. I suggest the exclusion criteria be restructured to align with the relevant search articles. It can be paraphrased as “also, articles lacking predictive outcome were excluded.” 4. The study year in Table 3 should follow a uniform order (arrange in ascending order) Are the rationale for, and objectives of, the Systematic Review clearly stated? Yes Are sufficient details of the methods and analysis provided to allow replication by others? Yes Is the statistical analysis and its interpretation appropriate? Yes Are the conclusions drawn adequately supported by the results presented in the review? Yes If this is a Living Systematic Review, is the ‘living’ method appropriate and is the search schedule clearly defined and justified? (‘Living Systematic Review’ or a variation of this term should be included in the title.) Not applicable Competing Interests: No competing interests were disclosed. Reviewer Expertise: image analysis and processing I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Barde M. Reviewer Report For: Machine learning based radiomics approach for outcome prediction of meningioma – a systematic review [version 1; peer review: 2 approved] . F1000Research 2025, 14 :330 ( https://doi.org/10.5256/f1000research.178486.r373394 ) The direct URL for this report is: https://f1000research.com/articles/14-330/v1#referee-response-373394 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Chandrasekhar P. Reviewer Report For: Machine learning based radiomics approach for outcome prediction of meningioma – a systematic review [version 1; peer review: 2 approved] . F1000Research 2025, 14 :330 ( https://doi.org/10.5256/f1000research.178486.r373393 ) The direct URL for this report is: https://f1000research.com/articles/14-330/v1#referee-response-373393 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 07 Apr 2025 Priyanka Chandrasekhar , Department of Allied Health Sciences, The Apollo University, Chittoor, Andhra Pradesh, India Approved VIEWS 0 https://doi.org/10.5256/f1000research.178486.r373393 Major comments: The review article provides thorough information of machine learning (ML) based radiomic models for predicting meningioma outcomes. It effectively summarizes the performance measures of various models, highlighting their strengths and limitations. The ... Continue reading READ ALL Major comments: The review article provides thorough information of machine learning (ML) based radiomic models for predicting meningioma outcomes. It effectively summarizes the performance measures of various models, highlighting their strengths and limitations. The results from eight studies were explained clearly. The comparison of different machine learning models was insightful. The quality assessment using the Radiomics quality score (RQS) is a strong point. The discussion highlighted the superior performance of ML models over traditional MRI predicting the tumor recurrence and aggressiveness, emphasizing their value in personalized treatment and planning. The conclusion effectively summarizes the findings and reaffirms the promise of integrating radiomics with clinical and pathological data in improving the management and outcomes of meningioma patients. Minor comments: The article is well written, the sentence “Radiomics incorporates a range of advanced techniques used to conduct a comprehensive analysis of medical images” could be rephrased for clarity. Are the rationale for, and objectives of, the Systematic Review clearly stated? Yes Are sufficient details of the methods and analysis provided to allow replication by others? Yes Is the statistical analysis and its interpretation appropriate? Not applicable Are the conclusions drawn adequately supported by the results presented in the review? Yes If this is a Living Systematic Review, is the ‘living’ method appropriate and is the search schedule clearly defined and justified? (‘Living Systematic Review’ or a variation of this term should be included in the title.) Not applicable Competing Interests: No competing interests were disclosed. Reviewer Expertise: Advances in Magnetic Resonance Imaging , Artificial intelligence in Healthcare I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Chandrasekhar P. Reviewer Report For: Machine learning based radiomics approach for outcome prediction of meningioma – a systematic review [version 1; peer review: 2 approved] . F1000Research 2025, 14 :330 ( https://doi.org/10.5256/f1000research.178486.r373393 ) The direct URL for this report is: https://f1000research.com/articles/14-330/v1#referee-response-373393 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 25 Mar 2025 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 Version 1 25 Mar 25 read read Priyanka Chandrasekhar , The Apollo University, Chittoor, India Mustapha Barde , Bayero University, Kano, Nigeria Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Barde M. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 08 Apr 2025 | for Version 1 Mustapha Barde , Bayero University, Kano, Kano, Nigeria 0 Views copyright © 2025 Barde M. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions An impressive systematic review was conducted by the authors, to evaluate the performance of machine learning based radiomics approach in the prediction of meningiomas. It was reported that based on the studies been reviewed MRI based machine learning, based on decision tree models have more accuracy (90%) in the prediction of meningiomas reoccurrence. Surpassing apparent diffusion coefficient-based models (83%). from MRI studies. It was reported that radiomics combined with clinicopathological data via Machine learning gives improved results in meningioma prediction compared to conventional MRI results. Also based on the review the meningioma survival analysis prediction, the radiomics and clinicopathological features attained an area under the curve of 0.78. General comment . The review is commendable. However, addressing the below minor corrections will enhance clarity and comprehension. Minor corrections The below points should be addressed please for clarity 1. The total number of search articles from the databases should be stated in the abstract methodology, and the number excluded. 2. In figure 1, an asterisk was used in records identified and records excluded. A key will guide in noting the rationale for the asterisk or ignoring the asterisk if not crucial. 3. In the exclusion criteria, it was stated that articles that report grading were excluded. However, in Table 2. The grade column was included, with the relevant grading based on the studies reviewed. I suggest the exclusion criteria be restructured to align with the relevant search articles. It can be paraphrased as “also, articles lacking predictive outcome were excluded.” 4. The study year in Table 3 should follow a uniform order (arrange in ascending order) Are the rationale for, and objectives of, the Systematic Review clearly stated? Yes Are sufficient details of the methods and analysis provided to allow replication by others? Yes Is the statistical analysis and its interpretation appropriate? Yes Are the conclusions drawn adequately supported by the results presented in the review? Yes If this is a Living Systematic Review, is the ‘living’ method appropriate and is the search schedule clearly defined and justified? (‘Living Systematic Review’ or a variation of this term should be included in the title.) Not applicable Competing Interests No competing interests were disclosed. Reviewer Expertise image analysis and processing I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Barde M. Peer Review Report For: Machine learning based radiomics approach for outcome prediction of meningioma – a systematic review [version 1; peer review: 2 approved] . F1000Research 2025, 14 :330 ( https://doi.org/10.5256/f1000research.178486.r373394) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-330/v1#referee-response-373394 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Chandrasekhar P. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 07 Apr 2025 | for Version 1 Priyanka Chandrasekhar , Department of Allied Health Sciences, The Apollo University, Chittoor, Andhra Pradesh, India 0 Views copyright © 2025 Chandrasekhar P. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Major comments: The review article provides thorough information of machine learning (ML) based radiomic models for predicting meningioma outcomes. It effectively summarizes the performance measures of various models, highlighting their strengths and limitations. The results from eight studies were explained clearly. The comparison of different machine learning models was insightful. The quality assessment using the Radiomics quality score (RQS) is a strong point. The discussion highlighted the superior performance of ML models over traditional MRI predicting the tumor recurrence and aggressiveness, emphasizing their value in personalized treatment and planning. The conclusion effectively summarizes the findings and reaffirms the promise of integrating radiomics with clinical and pathological data in improving the management and outcomes of meningioma patients. Minor comments: The article is well written, the sentence “Radiomics incorporates a range of advanced techniques used to conduct a comprehensive analysis of medical images” could be rephrased for clarity. Are the rationale for, and objectives of, the Systematic Review clearly stated? Yes Are sufficient details of the methods and analysis provided to allow replication by others? Yes Is the statistical analysis and its interpretation appropriate? Not applicable Are the conclusions drawn adequately supported by the results presented in the review? Yes If this is a Living Systematic Review, is the ‘living’ method appropriate and is the search schedule clearly defined and justified? (‘Living Systematic Review’ or a variation of this term should be included in the title.) Not applicable Competing Interests No competing interests were disclosed. Reviewer Expertise Advances in Magnetic Resonance Imaging , Artificial intelligence in Healthcare I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Chandrasekhar P. Peer Review Report For: Machine learning based radiomics approach for outcome prediction of meningioma – a systematic review [version 1; peer review: 2 approved] . F1000Research 2025, 14 :330 ( https://doi.org/10.5256/f1000research.178486.r373393) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-330/v1#referee-response-373393 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. 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