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

Fan, Yanghua; Li, Panpan; Li, Yiping; Liu, Feng; He, Yu; Wang, Liang; Zhang, Junting; Wu, Zhen

doi:10.3389/fonc.2021.792521

Cited by 11 publications

(5 citation statements)

References 40 publications

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“…To our knowledge, the association between radiomics features and differentiation of intracranial SFTs and ATMs has not been evaluated. However, previous studies have applied the radiomics models based on T1WI or T2WI to distinguish between SFTs and meningiomas, especially angiomatous meningiomas, achieved AUCs ranging from 0.762 to 0.918 [20,21,30]. In our sample cohort, we applied the LR method to construct three radiomics models (T1WI, T2WI, and T1&T2WI joint models)…”

Section: Discussionmentioning

confidence: 99%

“…Recently, radiomics analysis has been proven to provide valuable information to re ect the underlying pathophysiology, which is hard to acquire by visual interpretation [18]. Previous studies have illustrated that radiomics features from cMRI have powerful performance in the discrimination of intracranial SFTs and meningiomas, especially for angiomatous meningiomas [19][20][21]. However, it remains unclear whether signal intensity or radiomics features from cMRI are applicable to differentiate intracranial SFTs and ATMs.…”

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confidence: 99%

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Differentiating Intracranial Solitary Fibrous Tumors from Atypical Meningiomas Using Conventional MRI: A Comparison of Radiomics Models Versus Assessment from Radiologists, and Exploration of Correlation with Collagen Volume Fraction.

Zhang

Zhou

et al. 2023

Preprint

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Purpose: To investigate the performance of qualitative, quantitative assessment and radiomics models between solitary fibrous tumors (SFTs) and atypical meningiomas (ATMs), and the relationship between imaging parameters and collagen volume fraction (CVF). Methods: This study included 132 patients with ATMs and 50 patients with SFTs who underwent MRI. Qualitative assessment was performed using a five-point-scale visual scoring on T1WI and T2WI. Quantitative assessment was conducted by dividing the signal values of tumors by those of centrum semioval or thalamus. Radiomics models were established on T1WI, T2WI, and T1&T2WI. Spearman correlation was used to determine the correlation between CVF and imaging features. Results The AUCs and cutoff values of qualitative parameters were 0.913, > 3 on T1WI, and 0.763, ≤ 4 on T2WI in testing set. The AUCs were 0.854, 0.877, 0.942 for T1WI, T2WI and T1&T2WI models in testing set. There were significant differences between radiomics models and qualitative and quantitative assessment in training set (P < 0.05), while there were no significant differences in testing set. Among SFTs, negative correlation was obtained between CVF and qualitative and quantitative parameters on T1WI and T2WI. Conclusion The hyperintensity on T1WI and hypointensity on T2WI were valuable for discriminating SFTs and ATMs. The performance of radiomics models was superior to qualitative and quantitative parameters. The imaging parameters on T1WI and T2WI were negatively correlated with CVF among SFTs.

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

Differentiating Intracranial Solitary Fibrous Tumors from Atypical Meningiomas Using Conventional MRI: A Comparison of Radiomics Models Versus Assessment from Radiologists, and Exploration of Correlation with Collagen Volume Fraction.

Zhang

Zhou

et al. 2023

Preprint

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“…In fact, SFT/HPC have shown higher apparent diffusion coefficient (ADC) values than meningiomas [ 26 ]. In recent years, machine learning–based radiomics analysis, combining MRI-based radiomic features and clinical findings, has been proposed as a viable tool to make a differential diagnosis of HPC and meningioma with high accuracy [ 27 , 28 ].…”

Section: Discussionmentioning

confidence: 99%

Sixteen-Year Follow-Up in a Cavernous Sinus Hemangiopericytoma: Improved Outcomes over Radiotherapy Advances

Detti

Bardoscia²,

Pisani

et al. 2022

Brain Sciences

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Intracranial hemangiopericytomas are rare tumors, accounting for 1% of all central nervous system malignancies. This tumor is considered at high risk of local and also distant metastases. Surgical excision is the gold standard for treatment, but it is seldom curative by itself. Adjuvant radiotherapy is often recommended. We report an overview and update of the available literature on one such rare but aggressive mesenchymal tumor, using the case of a 46-year-old woman affected by hemangiopericytoma of the cavernous sinus surgically removed and treated with adjuvant radiotherapy at our institution. After seven years, the patient underwent a local recurrence and was treated with exeresis and Gamma Knife radiotherapy. Sixteen years after the initial diagnosis, she is still well with stable disease.

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“…This tool demonstrated a remarkable diagnostic accuracy, with an AUC of 0.917 in the validation cohort. Finally, Fan et al [ 9 ] developed a diagnostic model based on a combination of clinical and radiomics features to distinguish the two neoplasms, reporting an AUC of 0.91 in the validation set.…”

Section: Differential Diagnosismentioning

confidence: 99%

Meningioma Radiomics: At the Nexus of Imaging, Pathology and Biomolecular Characterization

Ugga

Spadarella

Pinto

et al. 2022

Cancers

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

Cited by 11 publications

References 40 publications

Differentiating Intracranial Solitary Fibrous Tumors from Atypical Meningiomas Using Conventional MRI: A Comparison of Radiomics Models Versus Assessment from Radiologists, and Exploration of Correlation with Collagen Volume Fraction.

Differentiating Intracranial Solitary Fibrous Tumors from Atypical Meningiomas Using Conventional MRI: A Comparison of Radiomics Models Versus Assessment from Radiologists, and Exploration of Correlation with Collagen Volume Fraction.

Sixteen-Year Follow-Up in a Cavernous Sinus Hemangiopericytoma: Improved Outcomes over Radiotherapy Advances

Meningioma Radiomics: At the Nexus of Imaging, Pathology and Biomolecular Characterization

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