Prediction of progression in skull base meningiomas: additional benefits of apparent diffusion coefficient value

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.

Pretreatment diffusion-weighted imaging for prediction of relapsed and refractory primary central nervous system lymphoma

Objectives

A subset of primary central nervous system lymphoma (PCNSL) has been shown to undergo an early relapsed/refractory (R/R) period after first-line chemotherapy. This study investigated the pretreatment clinical and MRI features to predict R/R in PCNSL, emphasizing the apparent diffusion coefficient (ADC) values in diffusion-weighted imaging (DWI).

Methods

This retrospective study investigated the pretreatment MRI features for predicting R/R in PCNSL. Only patients who had undergone complete preoperative and postoperative MRI follow-up studies were included. From January 2006 to December 2021, 52 patients from two medical institutions with a diagnosis of PCNSL were included (median follow-up time, 26.3 months). Among these, 24 (46.2%) had developed R/R (median time to relapse, 13 months). Cox proportional hazard regression analyses were performed to determine hazard ratios for all parameters.

Results

Significant predictors of R/R in PCNSL were female sex, complete response (CR) to first-line chemotherapy, and ADC value/ratio (p < 0.05). Cut-off points of ADC values and ADC ratios for prediction of R/R were 0.68 × 10-3 mm2/s and 0.97, with AUCs of 0.78 and 0.77, respectively (p < 0.05). Multivariate Cox proportional hazards analysis showed that failure of CR to first-line chemotherapy and low ADC values (<0.68 × 10-3 mm2/s) were significant risk factors for R/R, with hazard ratios of 5.22 and 14.45, respectively (p < 0.05). Kaplan-Meier analysis showed that lower ADC values and ratios predicted significantly shorter progression-free survival (p < 0.05).

Conclusion

Pretreatment ADC values in DWI offer quantitative valuable information for the treatment planning in PCNSL.

Spheno-orbital meningiomas: Is orbit reconstruction mandatory? Long-term outcomes and exophthalmos improvement

Background:

Meningiomas correspond to one-third of all primary central nervous system tumors. Approximately 9% of them are spheno-orbital meningiomas (SOMs), presenting significant clinical symptoms as visual impairment and orbital esthetics. This article aims to evaluate exophthalmos’ improvement in a surgical series without orbital reconstruction.

Methods:

We consecutively included all patients diagnosed with SOM, admitted to a single institution for 10 years. Surgical resection was the standard of care, associated or not with adjuvant radiation therapy. The radiological investigation included preoperative and postoperative head CT or MRI. We quantified proptosis through imaging.

Results:

Forty patients composed this series, 87.5% were female. Proptosis was the most common presentation (90%), followed by decreased visual acuity (65%), motility deficit (20%), and headache (20%). Gross total resection was achieved in 65% of the procedures. In late outcomes, 78% of the patients maintained or improved visual acuity and 85% maintained or improved headache. Proptosis significantly improved after surgery and along with the follow-up (P < 0.001). Ten patients were submitted to adjuvant RT, six of them after a subtotal resection. All patients of this subgroup had proptosis. It was observed a higher frequency of worse in visual acuity in patients submitted to RT (71% vs. 28%, P = 0.038).

Conclusion:

Resection of SOM was sufficient to stop the evolution of visual deficit and allowed the improvement of proptosis. Orbital reconstruction does not seem to be an essential step in reducing enophthalmos.

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.

A Novel MRI-Based Risk Stratification Algorithm for Predicting Postoperative Recurrence of Meningioma: More Benefits to Patients

Pathological grading of meningioma is insufficient to predict recurrence after resection and to guide individualized treatment strategies. One hundred and thirty-three patients with meningiomas who underwent total resection were enrolled in this retrospective study. Univariate analyses were conducted to evaluate the association between factors and recurrence. Least absolute shrinkage and selection operator (Lasso) was used to further select variables to build a logistic model. The predictive efficiency of the model and WHO grade was compared by using receiver operating characteristic curve (ROC), decision curve analysis (DCA), and net reclassification improvement (NRI). Patients were given a new risk layer based on a nomogram. The recurrence of meningioma in different groups was observed through the Kaplan-Meier curve. Univariate analysis demonstrated that 11 risk factors were associated with prognosis (P < 0.05). The result of ROC proved that the quantified risk-scoring system (AUC = 0.853) had a higher benefit than pathological grade (AUC = 0.689, P = 0.011). The incidence of recurrence of the high risk cohort (69%) was significantly higher than that of the low risk cohort (9%) by Kaplan-Meier analysis (P < 0.001). And all patients who did not relapse in the high risk group received adjuvant radiotherapy. The novel risk stratification algorithm has a significant value for the recurrence of meningioma and can help in optimizing the individualized design of clinical therapy.

New and Advanced Magnetic Resonance Imaging Diagnostic Imaging Techniques in the Evaluation of Cranial Nerves and the Skull Base

The skull base and cranial nerves are technically challenging to evaluate using magnetic resonance (MR) imaging, owing to a combination of anatomic complexity and artifacts. However, improvements in hardware, software and sequence development seek to address these challenges. This section will discuss cranial nerve imaging, with particular attention to the techniques, applications and limitations of MR neurography, diffusion tensor imaging and tractography. Advanced MR imaging techniques for skull base pathology will also be discussed, including diffusion-weighted imaging, perfusion and permeability imaging, with a particular focus on practical applications.

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.

Advanced Magnetic Resonance Imaging of the Skull Base

Magnetic resonance (MR) imaging is a crucial tool for evaluation of the skull base, enabling characterization of complex anatomy by utilizing multiple image contrasts. Recent technical MR advances have greatly enhanced radiologists' capability to diagnose skull base pathology and help direct management. In this paper, we will summarize cutting-edge clinical and emerging research MR techniques for the skull base, including high-resolution, phase-contrast, diffusion, perfusion, vascular, zero echo-time, elastography, spectroscopy, chemical exchange saturation transfer, PET/MR, ultra-high-field, and 3D visualization. For each imaging technique, we provide a high-level summary of underlying technical principles accompanied by relevant literature review and clinical imaging examples.

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.

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.

Imaging and diagnostic advances for intracranial meningiomas

The archetypal imaging characteristics of meningiomas are among the most stereotypic of all central nervous system (CNS) tumors. In the era of plain film and ventriculography, imaging was only performed if a mass was suspected, and their results were more suggestive than definitive. Following more than a century of technological development, we can now rely on imaging to non-invasively diagnose meningioma with great confidence and precisely delineate the locations of these tumors relative to their surrounding structures to inform treatment planning. Asymptomatic meningiomas may be identified and their growth monitored over time; moreover, imaging routinely serves as an essential tool to survey tumor burden at various stages during the course of treatment, thereby providing guidance on their effectiveness or the need for further intervention. Modern radiological techniques are expanding the power of imaging from tumor detection and monitoring to include extraction of biologic information from advanced analysis of radiological parameters. These contemporary approaches have led to promising attempts to predict tumor grade and, in turn, contribute prognostic data. In this supplement article, we review important current and future aspects of imaging in the diagnosis and management of meningioma, including conventional and advanced imaging techniques using CT, MRI, and nuclear medicine.

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.

Predicting the risk of postoperative recurrence and high-grade histology in patients with intracranial meningiomas using routine preoperative MRI

Risk factors for prediction of prognosis in meningiomas derivable from routine preoperative magnetic resonance imaging (pMRI) remain elusive. Correlations of tumor and edema volume, disruption of the arachnoid layer, heterogeneity of contrast enhancement, enhancement of the capsule, T2-intensity, tumor shape, and calcifications on pMRI with tumor recurrence and high-grade (WHO grade II/III) histology were analyzed in 565 patients who underwent surgery for WHO grade I (N = 516, 91%) or II/III (high-grade histology, N = 49, 9%) meningioma between 1991 and 2018. Edema volume (OR, 1.00; p = 0.003), heterogeneous contrast enhancement (OR, 3.10; p < 0.001), and an irregular shape (OR, 2.16; p = 0.015) were associated with high-grade histology. Multivariate analyses confirmed edema volume (OR, 1.00; p = 0.037) and heterogeneous contrast enhancement (OR, 2.51; p = 0.014) as risk factors for high-grade histology. Tumor volume (HR, 1.01; p = 0.045), disruption of the arachnoid layer (HR, 2.50; p = 0.003), heterogeneous contrast enhancement (HR, 2.05; p = 0.007), and an irregular tumor shape (HR, 2.57; p = 0.001) were correlated with recurrence. Multivariate analyses confirmed tumor volume (HR, 1.01; p = 0.032) and disruption of the arachnoid layer (HR, 2.44; p = 0.013) as risk factors for recurrence, independent of histology. Subgroup analyses revealed disruption of the arachnoid layer (HR, 9.41; p < 0.001) as a stronger risk factor for recurrence than high-grade histology (HR, 5.15; p = 0.001). Routine pMRI contains relevant information about the risk of recurrence or high-grade histology of meningioma patients. Loss of integrity of the arachnoid layer on MRI had a higher prognostic value than the WHO grading, and underlying histological or molecular alterations remain to be determined.

Prediction of recurrence in solid nonfunctioning pituitary macroadenomas: additional benefits of diffusion-weighted MR imaging

OBJECTIVE

A subset of benign, nonfunctioning pituitary macroadenomas (NFMAs) has been shown to undergo early progression/recurrence (P/R) during the first years after surgical resection. The aim of this study was to determine preoperative MR imaging features for the prediction of P/R in benign solid NFMAs, with emphasis on apparent diffusion coefficient (ADC) values.

METHODS

We retrospectively investigated the preoperative MR imaging features for the prediction of P/R in benign solid NFMAs. Only the patients who had undergone preoperative MRI and postoperative MRI follow-ups for more than 1 year (at least every 6-12 months) were included. From November 2010 to December 2016, a total of 30 patients diagnosed with benign solid NFMAs were included (median follow-up time 45 months), and 19 (63.3%) patients had P/R (median time to P/R 24 months).

RESULTS

Benign solid NFMAs with cavernous sinus invasion, failed chiasmatic decompression, large tumor height and tumor volume, high diffusion-weighted imaging (DWI) signal, and lower ADC values/ratios were significantly associated with P/R (p < 0.05). The cutoff points of ADC value and ADC ratio for prediction of P/R are 0.77 × 10-3 mm2/sec and 1.01, respectively, with area under the curve (AUC) values (0.9 and 0.91) (p < 0.01). In multivariate Cox proportional hazards analysis, low ADC value (< 0.77 × 10-3 mm2/sec) is a high-risk factor of P/R (p < 0.05) with a hazard ratio of 14.07.

CONCLUSIONS

Benign solid NFMAs with low ADC values/ratios are at a significantly increased risk of P/R, and aggressive treatments accompanied by close follow-up with imaging studies should be considered.

Long-Term Clinical Outcome of First Recurrence Skull Base Meningiomas

Skull base meningiomas (SBMs) are considered to be less aggressive and have a slower growth rate than non-SBMs. However, SBMs often develop local recurrences after surgical resection. Gross total removal is difficult because SBMs are deep-seated tumors and involve critical neurovascular structures. The treatment strategy for recurrent SBMs remains controversial. The present study aimed to evaluate the long-term clinical course and prognostic factors associated with shorter progression-free survival (PFS) of recurrent SBMs. This retrospective study included 85 recurrent SBMs from 65 patients who underwent surgery from January 2005 to September 2018. Overall survival (OS) and PFS were evaluated, and the associations among shorter PFS and age, sex, tumor size, lesions, World Health Organization (WHO) grading, removal rate, and time since prior surgery were analyzed. The median follow-up period for PFS was 68 months. The 2-, 5-, and 10-year PFS rates were 68.0%, 52.8%, and 22.7%, respectively. WHO grade II or III, multiple lesions, and tumor size were significantly associated with shorter PFS (p < 0.0001, p = 0.030, and p = 0.173, respectively). Although, radiotherapy did not improve PFS and OS for overall patients, PFS of the patients with subtotal and partial removal for WHO grade II SBMs was significantly improved by the radiotherapy. Multivariate analysis identified WHO grade II or III and multiple lesions as independent prognostic factors for shorter PFS (p < 0.0001 and p = 0.040, respectively). It is essential to estimate the risks associated with shorter PFS for patients with recurrent SBMs to aid in the development of appropriate postoperative strategies.

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.

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

BACKGROUND

Parasagittal and parafalcine (PSPF) meningiomas recur more frequently than other intracranial meningiomas owing to the difficulty in achieving gross total resection. The present study investigated the preoperative magnetic resonance imaging (MRI) features for the prediction of progression/recurrence (P/R) in benign PSPF meningiomas with an emphasis on the apparent diffusion coefficient (ADC) values.

METHODS

We retrospectively investigated the preoperative MRI features for the prediction of P/R in benign (World Health Organization grade I) PSPF meningiomas. Only patients who had undergone preoperative and postoperative MRI follow-up studies for ≥1 year were included. From October 2006 to December 2015, 48 patients with a diagnosis of benign PSPF meningioma were included (median follow-up period, 42.5 months). Of these 48 patients, 12 (25%) developed P/R (median time to P/R, 23 months).

RESULTS

PSPF meningiomas in male patients, subtotal resection, large tumor diameter, high diffusion-weighted imaging signal, and lower ADC values or ratios were significantly associated with P/R (P < 0.05). The cutoff points of the ADC value and ADC ratio for the prediction of P/R were 0.83 × 10^-3 mm²/second and 0.99, with an area under the curve of 0.82 and 0.83, respectively (P = 0.001). On multivariate Cox proportional hazards analysis, male sex and low ADC values (<0.83 × 10^-3 mm²/second) were high-risk factors for P/R, with a hazard ratio of 12.37 and 30.2, respectively (P < 0.05). Kaplan-Meier analysis showed that lower ADC values and ratios predicted for significantly shorter progression-free survival (P < 0.05).

CONCLUSIONS

The preoperative ADC values and ratios for the prediction of P/R offer additional valuable information for the treatment planning for PSPF meningiomas.