Prediction of Recurrence in Parasagittal and Parafalcine Meningiomas: Added Value of Diffusion-Weighted Magnetic Resonance Imaging

Progression/Recurrence of Meningioma: An Imaging Review Based on Magnetic Resonance Imaging

Meningiomas are the most common primary central nervous system tumors. The preferred treatment is maximum safe resection, and the heterogeneity of meningiomas results in a variable prognosis. Progression/recurrence (P/R) can occur at any grade of meningioma and is a common adverse outcome after surgical treatment and a major cause of postoperative rehospitalization, secondary surgery, and mortality. Early prediction of P/R plays an important role in postoperative management, further adjuvant therapy, and follow-up of patients. Therefore, it is essential to thoroughly analyze the heterogeneity of meningiomas and predict postoperative P/R with the aid of noninvasive preoperative imaging. In recent years, the development of advanced magnetic resonance imaging technology and machine learning has provided new insights into noninvasive preoperative prediction of meningioma P/R, which helps to achieve accurate prediction of meningioma P/R. This narrative review summarizes the current research on conventional magnetic resonance imaging, functional magnetic resonance imaging, and machine learning in predicting meningioma P/R. We further explore the significance of tumor microenvironment in meningioma P/R, linking imaging features with tumor microenvironment to comprehensively reveal tumor heterogeneity and provide new ideas for future research.

Evaluation of Magnetic Resonance Imaging for Microsurgical Efficacy and Relapse of Rolandic Meningioma

In this study, magnetic resonance imaging (MRI) was used to evaluate the relapse features of patients with Rolandic meningioma after the microsurgery. 53 patients with Rolandic meningioma were selected as the research objects, and they were divided into the relapse group (n = 16) and nonrelapse group (n = 37) according to whether patients had a relapse during the follow-up period. Differences in quality of life, ¹H-MRS index, vascular density, and cell proliferation between the two groups were assessed as well as imaging differences between the two groups were analyzed using MRI. The results showed that the patients' quality-of-life scores in the two groups increased notably after the surgical treatment (P < 0.05). Compared with the nonrelapse group, the proportion of irregular boundary and uneven enhancement of focal tissue in the relapse group was signally increased (P < 0.05). Compared with the nonrelapse group, cell proliferation index, vascular density and imaging index, mean tumor diameter, mean transit time (MTT), time to peak (TTP), fractional anisotropy (FA), choline (Cho)/N-acetylaspartic acid (NAA), Cho/creatine (Cr), lactic acid (Lac)/Cr, and the maximum value of relative cerebral blood volume (rCBVmax) in the relapse group were obviously increased (P < 0.05). However, the apparent dispersion coefficient, NAA/Cr, and Lac/NAA values decreased greatly (P < 0.05). To sum up, the microsurgical treatment helped improve the quality of life of patients with Rolandic meningioma, and MR imaging could be used to determine the relapse of Rolandic meningioma after microsurgical treatment.

How I do it: the surgical resection of a middle third parasagittal meningioma with venous preservation strategy

BACKGROUND

The surgical resection of the middle third parasagittal meningioma (PSM) is difficult, where the challenge is to systematically protect the eloquent parenchyma and collateral venous drainage.

METHOD

We report a case of PSM that eroded the skull, wholly occluded the superior sagittal sinus at the middle third segment, underwent radical resection with evaluation and preservation of the collateral venous drainage by preoperative venography, and intraoperative indocyanine green videoangiography (ICGVA) that aimed to avoid postoperative complications.

CONCLUSION

This case demonstrates the importance of venous preservation strategy and the value of ICGVA in the intraoperative assessment of collateral venous drainage function.

Machine Learning for Prediction of Recurrence in Parasagittal and Parafalcine Meningiomas: Combined Clinical and MRI Texture Features

A subset of parasagittal and parafalcine (PSPF) meningiomas may show early progression/recurrence (P/R) after surgery. This study applied machine learning using combined clinical and texture features to predict P/R in PSPF meningiomas. A total of 57 consecutive patients with pathologically confirmed (WHO grade I) PSPF meningiomas treated in our institution between January 2007 to January 2019 were included. All included patients had complete preoperative magnetic resonance imaging (MRI) and more than one year MRI follow-up after surgery. Preoperative contrast-enhanced T1WI, T2WI, T1WI, and T2 fluid-attenuated inversion recovery (FLAIR) were analyzed retrospectively. The most significant 12 clinical features (extracted by LightGBM) and 73 texture features (extracted by SVM) were combined in random forest to predict P/R, and personalized radiomic scores were calculated. Thirteen patients (13/57, 22.8%) had P/R after surgery. The radiomic score was a high-risk factor for P/R with hazard ratio of 15.73 (p < 0.05) in multivariate hazards analysis. In receiver operating characteristic (ROC) analysis, an AUC of 0.91 with cut-off value of 0.269 was observed in radiomic scores for predicting P/R. Subtotal resection, low apparent diffusion coefficient (ADC) values, and high radiomic scores were associated with shorter progression-free survival (p < 0.05). Among different data input, machine learning using combined clinical and texture features showed the best predictive performance, with an accuracy of 91%, precision of 85%, and AUC of 0.88. Machine learning using combined clinical and texture features may have the potential to predict recurrence in PSPF meningiomas.

Pre-operative MRI Radiomics for the Prediction of Progression and Recurrence in Meningiomas

Objectives: A subset of meningiomas may show progression/recurrence (P/R) after surgical resection. This study applied pre-operative MR radiomics based on support vector machine (SVM) to predict P/R in meningiomas.

Methods: From January 2007 to January 2018, 128 patients with pathologically confirmed WHO grade I meningiomas were included. Only patients who had undergone pre-operative MRIs and post-operative follow-up MRIs for more than 1 year were studied. Pre-operative T2WI and contrast-enhanced T1WI were analyzed. On each set of images, 32 first-order features and 75 textural features were extracted. The SVM classifier was utilized to evaluate the significance of extracted features, and the most significant four features were selected to calculate SVM score for each patient.

Results: Gross total resection (Simpson grades I–III) was performed in 93 (93/128, 72.7%) patients, and 19 (19/128, 14.8%) patients had P/R after surgery. Subtotal tumor resection, bone invasion, low apparent diffusion coefficient (ADC) value, and high SVM score were more frequently encountered in the P/R group (p < 0.05). In multivariate Cox hazards analysis, bone invasion, ADC value, and SVM score were high-risk factors for P/R (p < 0.05) with hazard ratios of 7.31, 4.67, and 8.13, respectively. Using the SVM score, an AUC of 0.80 with optimal cutoff value of 0.224 was obtained for predicting P/R. Patients with higher SVM scores were associated with shorter progression-free survival (p = 0.003).

Conclusions: Our preliminary results showed that pre-operative MR radiomic features may have the potential to offer valuable information in treatment planning for meningiomas.

Postoperative diffusion-weighted imaging and neurological outcome after convexity meningioma resection

OBJECTIVE

Convexity meningiomas are commonly managed with resection. Motor outcomes and predictors of new deficits after surgery are poorly studied. The objective of this study was to determine whether postoperative diffusion-weighted imaging (DWI) was associated with neurological deficits after convexity meningioma resection and to identify the risk factors for postoperative DWI restriction.

METHODS

A retrospective review of patients who had undergone convexity meningioma resection from 2014 to 2018 was performed. Univariate and multivariate logistic regressions were performed to identify variables associated with postoperative neurological deficits and a DWI signal. The amount of postoperative DWI signal was measured and was correlated with low apparent diffusion coefficient maps to confirm ischemic injury.

RESULTS

The authors identified 122 patients who had undergone a total of 125 operations for convexity meningiomas. The median age at surgery was 57 years, and 70% of the patients were female. The median follow-up was 26 months. The WHO grade was I in 62% of cases, II in 36%, and III in 2%. The most common preoperative deficits were seizures (24%), extremity weakness/paralysis (16%), cognitive/language/memory impairment (16%), and focal neurological deficit (16%). Following resection, 89% of cases had no residual deficit. Postoperative DWI showed punctate or no diffusion restriction in 78% of cases and restriction > 1 cm in 22% of cases. An immediate postoperative neurological deficit was present in 14 patients (11%), but only 8 patients (7%) had a deficit at 3 months postoperatively. Univariate analysis identified DWI signal > 1 cm (p < 0.0001), tumor diameter (p < 0.0001), preoperative motor deficit (p = 0.0043), older age (p = 0.0113), and preoperative embolization (p = 0.0171) as risk factors for an immediate postoperative deficit, whereas DWI signal > 1 cm (p < 0.0001), tumor size (p < 0.0001), and older age (p = 0.0181) were risk factors for deficits lasting more than 3 months postoperatively. Multivariate analysis revealed a DWI signal > 1 cm to be the only significant risk factor for deficits at 3 months postoperatively (OR 32.42, 95% CI 3.3-320.1, p = 0.0002). Further, estimated blood loss (OR 1.4 per 100 ml increase, 95% CI 1.1-1.7, p < 0.0001), older age (OR 1.1 per year older, 95% CI 1.0-1.1, p = 0.0009), middle third location in the sagittal plane (OR 16.9, 95% CI 1.3-216.9, p = 0.0026), and preoperative peritumoral edema (OR 4.6, 95% CI 1.2-17.7, p = 0.0249) were significantly associated with a postoperative DWI signal > 1 cm.

CONCLUSIONS

A DWI signal > 1 cm is significantly associated with postoperative neurological deficits, both immediate and long-lasting. Greater estimated blood loss, older age, tumor location over the motor strip, and preoperative peritumoral edema increase the risk of having a postoperative DWI signal > 1 cm, reflective of perilesional ischemia. Most immediate postoperative deficits will improve over time. These data are valuable when preoperatively communicating with patients about the risks of surgery and when postoperatively discussing prognosis after a deficit occurs.

Long-term apparent diffusion coefficient value changes in patients undergoing radiosurgical treatment of meningiomas

PURPOSE

A noninvasive method to predict the progress or treatment response of meningiomas is desirable to improve the tumor management. Studies showed that apparent diffusion coefficient (ADC) pretreatment values can predict treatment response in brain tumors. The aim of this study was to analyze changes of intratumoral ADC values in patients with meningiomas undergoing conservative or radiosurgery.

METHOD

MR images of 51 patients with diagnose of meningiomas were retrospectively reviewed. Twenty-five patients undergoing conservative or radiosurgery treatment, respectively, were included in the study. The follow-up data ranged between 1 and 10 years. Based on ROI analysis, the mean ADC values, ADC_10%min, and ADC_90%max were evaluated at different time points during follow-up.

RESULTS

Baseline ADC values in between both groups were similar. The ADC_mean values, ADC_10%min, and ADC_90%max within the different groups did not show any significant changes during the follow-up times in the untreated (ADC_mean over 10 years period: 0.87 ± 0.05 × 10^-3 mm²/s) and radiosurgically treated (ADC_mean over 4 years period: 1.02 ± 0.12 × 10^-3 mm²/s) group. However, statistically significant difference was observed when comparing the ADC_mean and ADC_90%max values of untreated with radiosurgically treated (p < 0.0001) meningiomas. Also, ADC_10%min revealed statistically significant difference between the untreated and the radiosurgery group (p < 0.05).

CONCLUSIONS

ADC values in conservatively managed meningiomas remain stable during the follow-up. However, meningiomas undergoing radiosurgery reveal significant change of the mean ADC values over time, suggesting that ADC may reflect a change in the biological behavior of the tumor. These observations might suggest the value of ADC changes as an indicator of treatment response.

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

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.

The predictive value of preoperative apparent diffusion coefficient (ADC) for facial nerve outcomes after vestibular schwannoma resection: clinical study

OBJECT

Diffusion MRI has been used to predict intraoperative consistency of tumors. Apparent diffusion coefficient (ADC) has shown predictive value as an imaging biomarker in many CNS tumors but has not been studied in a large cohort of patients with vestibular schwannoma. In this study, we examine the utility of ADC as a predictive biomarker for intraoperative tumor characteristics and postoperative facial nerve outcome.

METHODS

A retrospective review of patients who underwent vestibular schwannoma resection at our institution from 2008 to 2018 yielded 87 patients, of which 72 met inclusion criteria. Operative reports and clinical records were reviewed for clinical data; MRI data were interpreted in a blinded fashion for qualitative and quantitative biomarkers, including tumor ADC.

RESULTS

Mean tumor ADC values did not predict intraoperative consistency or adherence (p = 0.63). Adherent tumors were associated with worse facial nerve outcomes (p = 0.003). Regression tree analysis identified 3 ADC categories with statistically different facial nerve outcomes. The categories identified were ADC < 1006.04 × 10^-6 mm^2/s; ADC 1006.04-1563.93 × 10^-6 mm^2/s and ADC ≥ 1563.94 × 10^-6 mm^2/s. Postoperative and final House-Brackmann (HB) scores were significantly higher in the intermediate ADC group (2.3, p = 0.0038). HB outcomes were similar between the group with ADC < 1006.04 × 10^-6 mm^2/s and ≥ 1563.94 × 10^-6 mm^2/s (1.3 vs 1.3).

CONCLUSIONS

Middle-range preoperative ADC in vestibular schwannoma suggests a less favorable postoperative HB score. Preoperative measurement of ADC in vestibular schwannoma may provide additional information regarding prognostication of facial nerve outcomes.

Predictors of recurrence and high growth rate of residual meningiomas after subtotal resection

OBJECTIVE

The extent of resection has been shown to improve outcomes in patients with meningiomas. However, resection can be complicated by constraining local anatomy, leading to subtotal resections. An understanding of the natural history of residual tumors is necessary to better guide postsurgical management and minimize recurrence. This study seeks to identify predictors of recurrence and high growth rate following subtotal resection of intracranial meningiomas.

METHODS

Adult patients who underwent primary surgical resection of a WHO grade I meningioma at a tertiary care institution from 2007-2017 were retrospectively reviewed. Volumetric tumor measurements were made on patients with subtotal resections. Stepwise multivariate proportional hazards regression analyses were performed to identify factors associated with time to recurrence, as well as stepwise multivariate regression analyses to assess for factors associated with high postoperative growth rate.

RESULTS

Of the 141 patients (18%) who underwent radiographic subtotal resection of an intracranial meningioma during the reviewed period, 74 (52%) suffered a recurrence, in which the median (interquartile range, IQR) time to recurrence was 14 (IQR 6-34) months. Among those tumors subtotally resected, the median pre- and postoperative tumor volumes were 17.19 cm3 (IQR 7.47-38.43 cm3) and 2.31 cm3 (IQR 0.98-5.16 cm3), which corresponded to a percentage resection of 82% (IQR 68%-93%). Postoperatively, the median growth rate was 0.09 cm3/year (IQR 0-1.39 cm3/year). Factors associated with recurrence in multivariate analysis included preoperative tumor volume (hazard ratio [HR] 1.008,95% confidence interval [CI] 1.002-1.013, p = 0.008), falcine location (HR 2.215, 95% CI 1.179-4.161, p = 0.021), tentorial location (HR 2.410, 95% CI 1.203-4.829, p = 0.024), and African American race (HR 1.811, 95% CI 1.042-3.146, p = 0.044). Residual volume (RV) was associated with high absolute annual growth rate (odds ratio [OR] 1.175, 95% CI 1.078-1.280, p < 0.0001), with the maximum RV benefit at < 5 cm3 (OR 4.056, 95% CI 1.675-9.822, p = 0.002).

CONCLUSIONS

By identifying predictors of recurrence and growth rate, this study helps identify potential patients with a high chance of recurrence following subtotal resection, which are those with large preoperative tumor volume, falcine location, tentorial location, and African American race. Higher RVs were associated with tumors with higher postoperative growth rates. Recurrences typically occurred 14 months after surgery.

Radiomics approach for prediction of recurrence in skull base meningiomas

PURPOSE

A subset of skull base meningiomas (SBM) may show early progression/recurrence (P/R) as a result of incomplete resection. The purpose of this study is the implementation of MR radiomics to predict P/R in SBM.

METHODS

From October 2006 to December 2017, 60 patients diagnosed with pathologically confirmed SBM (WHO grade I, 56; grade II, 3; grade III, 1) were included in this study. Preoperative MRI including T2WI, diffusion-weighted imaging (DWI), and contrast-enhanced T1WI were analyzed. On each imaging modality, 13 histogram parameters and 20 textural gray level co-occurrence matrix (GLCM) features were extracted. Random forest algorithms were utilized to evaluate the importance of these parameters, and the most significant three parameters were selected to build a decision tree for prediction of P/R in SBM. Furthermore, ADC values obtained from manually placed ROI in tumor were also used to predict P/R in SBM for comparison.

RESULTS

Gross-total resection (Simpson Grades I-III) was performed in 33 (33/60, 55%) patients, and 27 patients received subtotal resection. Twenty-one patients had P/R (21/60, 35%) after a postoperative follow-up period of at least 12 months. The three most significant parameters included in the final radiomics model were T1 max probability, T1 cluster shade, and ADC correlation. In the radiomics model, the accuracy for prediction of P/R was 90%; by comparison, the accuracy was 83% using ADC values measured from manually placed tumor ROI.

CONCLUSIONS

The results show that the radiomics approach in preoperative MRI offer objective and valuable clinical information for treatment planning in SBM.