The Current State of Radiomics for Meningiomas: Promises and Challenges

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.

Current Status and Quality of Machine Learning-Based Radiomics Studies for Glioma Grading: A Systematic Review

“…This method can enhance the traditional imaging analysis and provide personalized medicine for patients [16]. The power of radiomics in quantifying distinct tumor types and, consequently, tumor grading and predicting different cancers' survival has been demonstrated by many experimental studies [16][17][18][19][20][21]. The preliminary investigations about the role of radiomics for glioma are promising.…”

Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review

“…Twelve articles did not focus on ML techniques [ 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 ]. Eight articles were not original reports but reviews or editorials [ 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 ]. Three articles used semi-automatic segmentation techniques [ 96 , 97 , 98 ].…”

AI-Driven Image Analysis in Central Nervous System Tumors-Traditional Machine Learning, Deep Learning and Hybrid Models

“…Another study by Kha et al examined a model based on data extracted from The Cancer Imaging Archive (TCIA), including 159 LGG patients with 1p/19q co-deletion mutation status and XGBoost as the baseline algorithm combined with SHapley Additive exPlanations (SHAP) analysis and selected the seven most optimal radiomics features to build the final predictive model which achieved accuracy of 87% and 82.8% on the training set and external test set, respectively [ 82 ]. Scenarios of highly curated data sets of more homogenous molecular and histological classification as exemplified by H3K27M mutation analysis in pediatric high-grade gliomas, radiomics applications in meningioma, and in pituitary neuroendocrine and sellar tumors are increasingly reported [ 24 , 83 , 85 – 90 ]. Wu et al employed MRI radiomics and clinical features to preoperatively predict H3K27M mutation in pediatric high-grade gliomas using 9 radiomics features to construct the radiomics signature and showed a favorable discriminatory ability in training and test sets with an AUC of 0.95 and 0.92, respectively [ 83 ].…”