Machine Learning in Meningioma MRI: Past to Present. A Narrative Review

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

Texture Analysis in Brain Tumor MR Imaging

“…[55][56][57] In this regard, however, the real radiological challenge in the differential diagnosis of meningioma would be its distinction from dura-based tumors, such as dural metastases and solitary fibrous tumors/hemangiopericytomas. 58,59 Meningioma grading is another attractive topic. Previous studies have reported AUC values of 0.63-0.97 for the prediction of WHO grade or for the differentiation of low-and high-grade meningiomas, with the reflection of various types of machine learning algorithms and either single-parametric or multiparametric MRI, and the types of images they used.…”

“…Common limitations in the previous MRTA studies in meningioma are the retrospective design and the fact that meningioma datasets predominantly comprised grade I lesions (i.e., there was an imbalance in categories). 59 Other than meningioma, the classification performance with texture analysis was reported in pediatric patients with brain tumors arising in the posterior cranial fossa. [68][69][70] In an earlier study using first-order and GLCM features, Rodriguez Gutierrez et al suggested that first-order features in ADC yielded the best tumor classification accuracy (78.9%-91.4%).…”

Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging

“…Meningiomas may exhibit intratumoral heterogeneity with variable degrees of vascularity, necrosis, infiltration, and rarely transformation from low to high grade. Conventional MRI remains the standard imaging modality for provisional diagnosis and follow-ups of meningiomas despite lacking the ability to determine the biological behavior and recurrence potential of the tumor [ 91 , 92 ]. Advanced MRI techniques like DWI and PWI have been applied previously in the diagnosis and grading of meningiomas but with overlapping results [ 93 ].…”

Molecular Biology in Treatment Decision Processes—Neuro-Oncology Edition

“…Data collected is represented by direct data entry fields (manual or automatic) in the treatment planning system (e.g., tumor site, treatment intent, ready to treat dates, patient setup and desired technique employed in treatment planning), as well as tumor and normal tissue volumes delineated by the clinician and RT dose delivered using multiple DVH (Dose Volume Histogram) (Figure 1). These large-scale datasets can be employed for administrative purposes, such as capturing the number of patients on treatment that share a common histology or planning technique, but are also most relevant to computational approaches that involve artificial intelligence (AI) [3,4,[9][10][11][12][13], machine learning (ML) [2,[4][5][6][7]9,[13][14][15][16][17][18][19][20][21][22], deep learning (DL) [2,3,11,[23][24][25][26][27], ground truth [7,13] and radiogenomics [2,13,14,[28][29][30][31][32][33][34] (See Table…”

“…Most studies have focused on diagnosis in the context of CNS tumors where tissue acquisition is challenging or impossible such as pediatric posterior fossa tumors [19], rare histologies or histologies that present difficult diagnostic interpretation (ependymoma, pilocytic astrocytoma, medulloblastoma, craniopharyngioma) [10,20,27,78,79] and very few studies examined computational avenues to optimise RT [80], Zhu [81]. In meningioma attempts have focused on diagnosis and grading especially in the context of radiomics and surgical resection [21] and linked analysis to extent of tumor and brain or bone invasion [82][83][84][85]. If analysed in conjunction with biomarkers and RT dosimetry, these endeavors could prove highly relevant to RT volumes and dose as well patterns of recurrence [86][87][88] but clinical applications are only starting to emerge [89].…”