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

Clinical outcomes of patients undergoing reoperation with solitary fibrous tumors/hemangiopericytomas and malignant progression of tumors

OBJECTIVE

The purpose of this study was to analyze the clinical outcomes and malignant progression of tumors in patients who underwent reoperation for recurrent solitary fibrous tumors (SFTs) and hemangiopericytomas (HPCs).

METHODS

We identified 48 patients who underwent reoperation because of tumor recurrence at Tangdu Hospital between January 2010 and December 2021 and analyzed the clinical outcomes, namely, the rate of gross total resection (GTR), progression-free survival (PFS), overall survival (OS), malignant progression of tumors and radiotherapy. The survival curves for each group were plotted using the Kaplan‒Meier method and compared using log-rank tests.

RESULTS

Of the 48 patients (25 men and 23 women, mean age 49.5 ± 14.3 years), 25 experienced a second recurrence or metastasis, 15 of whom underwent a third surgery, and the remaining 10 patients who did not undergo surgery ultimately died after tumor progression. The median time (95% CI) to tumor recurrence was 40.0 (32.3-47.7) months after reoperation, with 3-, 5- and 10-year PFS rates of 54.6%, 29.5% and 14.8%, respectively. The median (95% CI) survival time was 70.0 (46.6-93.4) months, with 3-, 5- and 10-year survival rates of 67.9%, 55.1% and 36.7%, respectively. Among the 48 patients who underwent reoperation, 27 (56.3%) achieved GTR, and 21 (43.8%) achieved STR. Twelve patients in the GTR group (12/27, 44.4%) received radiotherapy after surgery, and 18 patients in the STR group (18/21, 85.7%) received radiotherapy. Of the 48 recurrent SFTs, 24 were classified as WHO grade 1, 14 were classified as WHO grade 2, and 10 were classified as WHO grade 3 based on 2021 WHO classification after the primary operation. After reoperation, 9 tumors developed malignant progression, including 4 WHO grade 1 tumors progressing to WHO grade 2 tumors, 1 WHO grade 1 tumor progressing to a WHO grade 3 tumor and 4 WHO grade 2 tumors progressing to WHO grade 3 tumors.

CONCLUSIONS

GTR after reoperation was associated with better PFS and OS compared to STR. However, the PFS after the third surgery was significantly shorter than that after the second surgery, and the rate of GTR also decreased. Malignant progression may occur after second or third tumor recurrence. Furthermore, compared with WHO grade 1 SFTs, WHO grade 2 and grade 3 SFTs significantly decreased PFS, but OS did not differ among the three groups. Radiotherapy did not prolong PFS or OS in patients who underwent reoperation.

Preoperative MRI-based radiomic nomogram for distinguishing solitary fibrous tumor from angiomatous meningioma: a multicenter study

Purpose

This study evaluates the efficacy of radiomics-based machine learning methodologies in differentiating solitary fibrous tumor (SFT) from angiomatous meningioma (AM).

Materials and methods

A retrospective analysis was conducted on 171 pathologically confirmed cases (94 SFT and 77 AM) spanning from January 2009 to September 2020 across four institutions. The study comprised a training set (n=137) and a validation set (n=34). All patients underwent contrast-enhanced T1-weighted (CE-T1WI) and T2-weighted(T2WI) MRI scans, from which 1166 radiomics features were extracted. Subsequently, seventeen features were selected through minimum redundancy maximum relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO). Multivariate logistic regression analysis was employed to assess the independence of these features as predictors. A clinical model, established via both univariate and multivariate logistic regression based on MRI morphological features, was integrated with the optimal radiomics model to formulate a radiomics nomogram. The performance of the models was assessed utilizing the area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), specificity (SPE), positive predictive value (PPV), and negative predictive value (NPV).

Results

The radiomics nomogram demonstrated exceptional discriminative performance in the validation set, achieving an AUC of 0.989. This outperformance was evident when compared to both the radiomics algorithm (AUC= 0.968) and the clinical model (AUC = 0.911) in the same validation sets. Notably, the radiomics nomogram exhibited impressive values for ACC, SEN, and SPE at 97.1%, 93.3%, and 100%, respectively, in the validation set.

Conclusions

The machine learning-based radiomic nomogram proves to be highly effective in distinguishing between SFT and AM.

Multimodal MRI-based radiomic nomogram for predicting telomerase reverse transcriptase promoter mutation in IDH-wildtype histological lower-grade gliomas

The presence of TERTp mutation in isocitrate dehydrogenase-wildtype (IDHwt) histologically lower-grade glioma (LGA) has been linked to a poor prognosis. In this study, we aimed to develop and validate a radiomic nomogram based on multimodal MRI for predicting TERTp mutations in IDHwt LGA. One hundred and nine IDH wildtype glioma patients (TERTp-mutant, 78; TERTp-wildtype, 31) with clinical, radiomic, and molecular information were collected and randomly divided into training and validation set. Clinical model, fusion radiomic model, and combined radiomic nomogram were constructed for the discrimination. Radiomic features were screened with 3 algorithms (Wilcoxon rank sum test, elastic net, and the recursive feature elimination) and the clinical characteristics of combined radiomic nomogram were screened by the Akaike information criterion. Finally, receiver operating characteristic curve, calibration curve, Hosmer-Lemeshow test, and decision curve analysis were utilized to assess these models. Fusion radiomic model with 4 radiomic features achieved an area under the curve value of 0.876 and 0.845 in the training and validation set. And, the combined radiomic nomogram achieved area under the curve value of 0.897 (training set) and 0.882 (validation set). Above that, calibration curve and Hosmer-Lemeshow test showed that the radiomic model and combined radiomic nomogram had good agreement between observations and predictions in the training set and the validation set. Finally, the decision curve analysis revealed that the 2 models had good clinical usefulness for the prediction of TERTp mutation status in IDHwt LGA. The combined radiomics nomogram performed great performance and high sensitivity in prediction of TERTp mutation status in IDHwt LGA, and has good clinical application.

Advanced Meningioma Imaging

Noninvasive imaging methods are used to accurately diagnose meningiomas and track their growth and location. These techniques, including computed tomography, MRI, and nuclear medicine, are also being used to gather more information about the biology of the tumors and potentially predict their grade and impact on prognosis. In this article, we will discuss the current and developing uses of these imaging techniques including additional analysis using radiomics in the diagnosis and treatment of meningiomas, including treatment planning and prediction of tumor behavior.

Radiological Differentiation Between Intracranial Meningioma and Solitary Fibrous Tumor/Hemangiopericytoma: A Systematic Literature Review

BACKGROUND

Intracranial solitary fibrous tumor (SFT) is characterized by aggressive local behavior and high post-resection recurrence rates. It is difficult to distinguish between SFT and meningiomas, which are typically benign. The goal of this study was to systematically review radiological features that differentiate meningioma and SFT.

METHODS

We performed a systematic review in accordance with PRISMA guidelines to identify studies that used imaging techniques to identify radiological differentiators of SFT and meningioma.

RESULTS

Eighteen studies with 1565 patients (SFT: 662; meningiomas: 903) were included. The most commonly used imaging modality was diffusion weighted imaging, which was reported in 11 studies. Eight studies used a combination of diffusion weighted imaging and T1- and T2-weighted sequences to distinguish between SFT and meningioma. Compared to all grades/subtypes of meningioma, SFT is associated with higher apparent diffusion coefficient, presence of narrow-based dural attachments, lack of dural tail, less peritumoral brain edema, extensive serpentine flow voids, and younger age at initial diagnosis. Tumor volume was a poor differentiator of SFT and meningioma, and overall, there were less consensus findings in studies exclusively comparing angiomatous meningiomas and SFT.

CONCLUSIONS

Clinicians can differentiate SFT from meningiomas on preoperative imaging by looking for higher apparent diffusion coefficient, lack of dural tail/narrow-based dural attachment, less peritumoral brain edema, and vascular flow voids on neuroimaging, in addition to younger age at diagnosis. Distinguishing between angiomatous meningioma and SFT is much more challenging, as both are highly vascular pathologies. Tumor volume has limited utility in differentiating between SFT and various grades/subtypes of meningioma.

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

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.

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

Simple Summary

Meningiomas are typically benign, common extra-axial tumors of the central nervous system. Routine clinical assessment by radiologists presents some limitations regarding long-term patient outcome prediction and risk stratification. Given the exponential growth of interest in radiomics and artificial intelligence in medical imaging, numerous studies have evaluated the potential of these tools in the setting of meningioma imaging. These were aimed at the development of reliable and reproducible models based on quantitative data. Although several limitations have yet to be overcome for their routine use in clinical practice, their innovative potential is evident. In this review, we present a wide-ranging overview of radiomics and artificial intelligence applications in meningioma imaging.

Abstract

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.