Three-Dimensional Radiomics Features From Multi-Parameter MRI Combined With Clinical Characteristics Predict Postoperative Cerebral Edema Exacerbation in Patients With Meningioma

Xiao, Bing; Fan, Yanghua; Zhang, Zhe; Tan, Zilong; Yang, Huan; Tu, Wei; Wu, Lei; Shen, Xiaoli; Guo, Hua; Wu, Zhen; Zhu, Xingen

doi:10.3389/fonc.2021.625220

Cited by 10 publications

(10 citation statements)

References 45 publications

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“…Radiomics is a machine-learning (ML) methodology that allows extraction of quantitative and reproducible tissue and lesion features from diagnostic images, called radiomics features [ 36 ]. It represents a new, low-cost, reliable, and promising tool in the individualized oncological management of meningioma patients [ 37 , 38 ] and provides some advantages compared to the previous qualitative radiological interpretations; in fact, by using defined algorithms, radiomics analysis could capture and reveal more specific information of the disease undetectable for the human eye and provide analysis about intensity distributions, spatial relationships, and texture heterogeneity within a region, as well as across the entire volume of the tumor [ 37 , 38 , 39 , 40 ], identifying invisible different subregions, which is not possible through biopsies, and analyzing their potential changes over time on serial imaging [ 41 , 42 , 43 ].…”

Section: Discussionmentioning

confidence: 99%

Updated Systematic Review on the Role of Brain Invasion in Intracranial Meningiomas: What, When, Why?

Brunasso

Bonosi

Costanzo

et al. 2022

Cancers

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Several recent studies are providing increasing insights into reliable markers to improve the diagnostic and prognostic assessment of meningioma patients. The evidence of brain invasion (BI) signs and its associated variables has been focused on, and currently, scientific research is investing in the study of key aspects, different methods, and approaches to recognize and evaluate BI. This paradigm shift may have significant repercussions for the diagnostic, prognostic, and therapeutic approach to higher-grade meningioma, as long as the evidence of BI may influence patients’ prognosis and inclusion in clinical trials and indirectly impact adjuvant therapy. We intended to review the current knowledge about the impact of BI in meningioma in the most updated literature and explore the most recent implications on both clinical practice and trials and future directions. According to the PRISMA guidelines, systematic research in the most updated platform was performed in order to provide a complete overview of characteristics, preoperative applications, and potential implications of BI in meningiomas. Nineteen articles were included in the present paper and analyzed according to specific research areas. The detection of brain invasion could represent a crucial factor in meningioma patients’ management, and research is flourishing and promising.

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Section: Discussionmentioning

confidence: 99%

Updated Systematic Review on the Role of Brain Invasion in Intracranial Meningiomas: What, When, Why?

Brunasso

Bonosi

Costanzo

et al. 2022

Cancers

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“…Other studies focused on predicting clinical characteristics of meningiomas, such as extent of peritumoral edema and tumor consistency. For example, Bing et al analyzed peritumoral edema in meningioma patients using an SVM-based machine learning algorithm combined with clinical data (14). Zhai et al constructed a radiomic-based signature to predict meningioma consistency with AUC of 0.94 in the validation cohort (13).…”

Section: Discussionmentioning

confidence: 99%

“…For meningiomas, algorithms have been developed in previous studies to predict WHO grade, tumor texture, peritumoral edema, and Ki-67 labels through radiomics. These models reported good performance in terms of accuracy and sensitivity ( 12 – 14 ). The status of well-known genetic changes could be accurately predicted by radiomics in several CNS tumors.…”

Section: Introductionmentioning

confidence: 98%

Application of MRI-Based Radiomics in Preoperative Prediction of NF2 Alteration in Intracranial Meningiomas

Sun

Ren²,

Zong³

et al. 2022

Front. Oncol.

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PurposeThis study aimed to investigate the feasibility of predicting NF2 mutation status based on the MR radiomic analysis in patients with intracranial meningioma.MethodsThis retrospective study included 105 patients with meningiomas, including 60 NF2-mutant samples and 45 wild-type samples. Radiomic features were extracted from magnetic resonance imaging scans, including T1-weighted, T2-weighted, and contrast T1-weighted images. Student’s t-test and LASSO regression were performed to select the radiomic features. All patients were randomly divided into training and validation cohorts in a 7:3 ratio. Five linear models (RF, SVM, LR, KNN, and xgboost) were trained to predict the NF2 mutational status. Receiver operating characteristic curve and precision-recall analyses were used to evaluate the model performance. Student’s t-tests were then used to compare the posterior probabilities of NF2 mut/loss prediction for patients with different NF2 statuses.ResultsNine features had nonzero coefficients in the LASSO regression model. No significant differences was observed in the clinical features. Nine features showed significant differences in patients with different NF2 statuses. Among all machine learning algorithms, SVM showed the best performance. The area under curve and accuracy of the predictive model were 0.85; the F1-score of the precision-recall curve was 0.80. The model risk was assessed by plotting calibration curves. The p-value for the H-L goodness of fit test was 0.411 (p> 0.05), which indicated that the difference between the obtained model and the perfect model was statistically insignificant. The AUC of our model in external validation was 0.83.ConclusionA combination of radiomic analysis and machine learning showed potential clinical utility in the prediction of preoperative NF2 status. These findings could aid in developing customized neurosurgery plans and meningioma management strategies before postoperative pathology.

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“…The flowchart and scheme of this study are similarly described in detail in our previous researches ( 15 , 16 ). All patients underwent preoperative brain T2WI and CE-T1WI MR imaging.…”

Section: Methodsmentioning

confidence: 99%

Non-Invasive Preoperative Imaging Differential Diagnosis of Intracranial Hemangiopericytoma and Angiomatous Meningioma: A Novel Developed and Validated Multiparametric MRI-Based Clini-Radiomic Model

Fan

et al. 2022

Front. Oncol.

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BackgroundAccurate preoperative differentiation of intracranial hemangiopericytoma and angiomatous meningioma can greatly assist operation plan making and prognosis prediction. In this study, a clini-radiomic model combining radiomic and clinical features was used to distinguish intracranial hemangiopericytoma and hemangioma meningioma preoperatively.MethodsA total of 147 patients with intracranial hemangiopericytoma and 73 patients with angiomatous meningioma from the Tiantan Hospital were retrospectively reviewed and randomly assigned to training and validation sets. Radiomic features were extracted from MR images, the elastic net and recursive feature elimination algorithms were applied to select radiomic features for constructing a fusion radiomic model. Subsequently, multivariable logistic regression analysis was used to construct a clinical model, then a clini-radiomic model incorporating the fusion radiomic model and clinical features was constructed for individual predictions. The calibration, discriminating capacity, and clinical usefulness were also evaluated.ResultsSix significant radiomic features were selected to construct a fusion radiomic model that achieved an area under the curve (AUC) value of 0.900 and 0.900 in the training and validation sets, respectively. A clini-radiomic model that incorporated the radiomic model and clinical features was constructed and showed good discrimination and calibration, with an AUC of 0.920 in the training set and 0.910 in the validation set. The analysis of the decision curve showed that the fusion radiomic model and clini-radiomic model were clinically useful.ConclusionsOur clini-radiomic model showed great performance and high sensitivity in the differential diagnosis of intracranial hemangiopericytoma and angiomatous meningioma, and could contribute to non-invasive development of individualized diagnosis and treatment for these patients.

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Three-Dimensional Radiomics Features From Multi-Parameter MRI Combined With Clinical Characteristics Predict Postoperative Cerebral Edema Exacerbation in Patients With Meningioma

Cited by 10 publications

References 45 publications

Updated Systematic Review on the Role of Brain Invasion in Intracranial Meningiomas: What, When, Why?

Updated Systematic Review on the Role of Brain Invasion in Intracranial Meningiomas: What, When, Why?

Application of MRI-Based Radiomics in Preoperative Prediction of NF2 Alteration in Intracranial Meningiomas

Non-Invasive Preoperative Imaging Differential Diagnosis of Intracranial Hemangiopericytoma and Angiomatous Meningioma: A Novel Developed and Validated Multiparametric MRI-Based Clini-Radiomic Model

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