Evaluating Diagnostic Accuracy and Determining Optimal Diagnostic Thresholds of Three Different Approaches to [68Ga]-DOTATATE PET/MRI Analysis in Patients with Meningioma

PURPOSEMultiple approaches with [Ga68]-DOTATATE, a somatostatin analog PET radiotracer, have demonstrated clinical utility in evaluation of meningioma but have not been compared directly. Our purpose was to compare diagnostic performance of three approaches to quantitative brain [68Ga]-DOTATATE PET/MRI analysis in patients with suspected meningioma recurrence and to establish the optimal diagnostic threshold for each method.METHODSPatients with suspected meningioma were imaged prospectively with [68Ga]-DOTATATE brain PET/MRI. Lesions were classified as meningiomas and post-treatment change (PTC), based on pathology findings and follow up MRI appearance. Lesions were reclassified using the following methods: absolute SUV threshold (SUV), SUV ratio (SUVR) to superior sagittal sinus (SSS) (SUVRsss), and SUVR to the pituitary gland (SUVRpit). Diagnostic performance of the three methods was compared using contingency tables and McNemar’s test. Previously published pre-determined thresholds were assessed where applicable. The optimal thresholds for each method were identified using Youden’s J statistics.RESULTS166 meningiomas and 41 PTC lesions were identified across 62 patients. SUV, SUVRsss, and SUVRpit of meningioma were significantly higher than those of PTC (P<0.0001). The optimal thresholds for SUV, SUVRsss, and SUVRpit were 4.65, 3.23, and 0.260, respectively. At the optimal thresholds, SUV had the highest specificity (97.6%) and SUVRsss had the highest sensitivity (86.1%). An ROC analysis of SUV, SUVRsss, and SUVRpit revealed AUC of 0.932, 0.910, and 0.915, respectively (P<0.0001).CONCLUSIONWe found that the SUVRsss method may have the most robust combination of sensitivity and specificity in the diagnosis of meningioma in the post-treatment setting, with the optimal threshold of 3.23. Future studies validating our findings in different patient populations are needed to continue optimizing the diagnostic performance of [68Ga]-DOTATATE PET/MRI in meningioma patients. Clinical Trial Registration: ClinicalTrials.gov Identifier: NCT04081701. Registered 9 September 2019. https://clinicaltrials.gov/ct2/show/NCT04081701

Past medical history of tumors other than meningioma is a negative prognostic factor for tumor recurrence in meningiomas WHO grade I

Background Prognostic markers for meningioma recurrence are needed to guide patient management. Apart from rare hereditary syndromes, the impact of a previous unrelated tumor disease on meningioma recurrence has not been described before. Methods We retrospectively searched our database for patients with meningioma WHO grade I and complete resection provided between 2002 and 2016. Demographical, clinical, pathological, and outcome data were recorded. The following covariates were included in the statistical model: age, sex, clinical history of unrelated tumor disease, and localization (skull base vs. convexity). Particular interest was paid to the patients’ past medical history. The study endpoint was date of tumor recurrence on imaging. Prognostic factors were obtained from multivariate proportional hazards models. Results Out of 976 meningioma patients diagnosed with a meningioma WHO grade I, 416 patients fulfilled our inclusion criteria. We encountered 305 women and 111 men with a median age of 57 years (range: 21–89 years). Forty-six patients suffered from a tumor other than meningioma, and no TERT mutation was detected in these patients. There were no differences between patients with and without a positive oncological history in terms of age, tumor localization, or mitotic cell count. Clinical history of prior tumors other than meningioma showed the strongest association with meningioma recurrence (p = 0.004, HR = 3.113, CI = 1.431–6.771) both on uni- and multivariate analysis. Conclusion Past medical history of tumors other than meningioma might be associated with an increased risk of meningioma recurrence. A detailed pre-surgical history might help to identify patients at risk for early recurrence.

PATH-37. IDENTIFYING HIGH-RISK PATIENTS FOR WHO GRADE I MENINGIOMA RECURRENCE USING HISTOPATHOLOGICAL FEATURES AND THE MIB-1 INDEX

BACKGROUND In this study, we identify clinical, radiographic, and histopathologic prognosticators of overall, early, and post-median recurrence in World Health Organization (WHO) grade I meningiomas. We also determine a clinically relevant cutoff for MIB-1 to identify patients at high risk for recurrence. METHODS A retrospective review of WHO grade I meningioma patients with available MIB-1 index data who underwent treatment at our institution from 2007-2017 was performed. Univariate and multivariate analyses, and recursive partitioning analysis (RPA), were used to identify risk factors for overall, early (within 24 months), and post-median (greater than 24 months post-treatment) recurrence. RESULTS A total of 239 patients were included. The mean age was 60.0 years, and 69.5% of patients were female. The average follow-up was 41.1 months. All patients received surgery and 2 patients each received either adjuvant radiotherapy or gamma knife treatment. The incidence of recurrence was 10.9%, with an average time to recurrence of 33.2 months (6-105 months). Posterior fossa tumor location (p=0.004), MIB-1 staining (p=0.008), nuclear atypia (p=0.003), and STR (p&lt; 0.001) were independently associated with an increased risk of recurrence on cox-regression analysis. RPA for overall recurrence highlighted extent of resection, and after gross total resection (GTR), a MIB-1 index cutoff of 4.5% as key prognostic factors for recurrence. Patients with a GTR and MIB-1 &gt;4.5% had a similar incidence of recurrence as those with STR (18.8vs.18.6%). Variables independently associated with early recurrence on binary logistic regression modeling included STR (p=0.002) and nuclear atypia (p=0.019). RPA confirmed STR as associated with early recurrence. MIB-1 index (p=0.010) was identified as an independent predictor of post-median recurrence using similar methods. CONCLUSIONS STR, posterior fossa location, nuclear atypia, and elevated MIB-1 index are prognostic factors for WHO grade I meningioma recurrence. Moreover, MIB-1 index &gt;4.5% is prognostic for recurrence in patients with GTR.

Meta-gene markers predict meningioma recurrence with high accuracy

Meningiomas, the most common adult brain tumors, recur in up to half of cases. This requires timely intervention and therefore accurate risk assessment of recurrence is essential. Our current practice relies heavily on histological grade and extent of surgical excision to predict meningioma recurrence. However, prediction accuracy can be as poor as 50% for low or intermediate grade tumors which constitute the majority of cases. Moreover, attempts to find molecular markers to predict their recurrence have been impeded by low or heterogenous genetic signal. We therefore sought to apply systems-biology approaches to transcriptomic data to better predict meningioma recurrence. We apply gene co-expression networks to a cohort of 252 adult patients from the publicly available genetic repository Gene Expression Omnibus. Resultant gene clusters (“modules”) were represented by the first principle component of their expression, and their ability to predict recurrence assessed with a logistic regression model. External validation was done using two independent samples: one merged microarray-based cohort with a total of 108 patients and one RNA-seq-based cohort with 145 patients, using the same modules. We used the bioinformatics database Enrichr to examine the gene ontology associations and driver transcription factors of each module. Using gene co-expression analysis, we were able predict tumor recurrence with high accuracy using a single module which mapped to cell cycle-related processes (AUC of 0.81 ± 0.09 and 0.77 ± 0.10 in external validation using microarray and RNA-seq data, respectively). This module remained predictive when controlling for WHO grade in all cohorts, and was associated with several cancer-associated transcription factors which may serve as novel therapeutic targets for patients with this disease. With the easy accessibility of gene panels in healthcare diagnostics, our results offer a basis for routine molecular testing in meningioma management and propose potential therapeutic targets for future research.

A Prognostic Gene-Expression Signature and Risk Score for Meningioma Recurrence After Resection

BACKGROUND Prognostic markers for meningioma are needed to risk-stratify patients and guide postoperative surveillance and adjuvant therapy. OBJECTIVE To identify a prognostic gene signature for meningioma recurrence and mortality after resection using targeted gene-expression analysis. METHODS Targeted gene-expression analysis was used to interrogate a discovery cohort of 96 meningiomas and an independent validation cohort of 56 meningiomas with comprehensive clinical follow-up data from separate institutions. Bioinformatic analysis was used to identify prognostic genes and generate a gene-signature risk score between 0 and 1 for local recurrence. RESULTS We identified a 36-gene signature of meningioma recurrence after resection that achieved an area under the curve of 0.86 in identifying tumors at risk for adverse clinical outcomes. The gene-signature risk score compared favorably to World Health Organization (WHO) grade in stratifying cases by local freedom from recurrence (LFFR, P &lt; .001 vs .09, log-rank test), shorter time to failure (TTF, F-test, P &lt; .0001), and overall survival (OS, P &lt; .0001 vs .07) and was independently associated with worse LFFR (relative risk [RR] 1.56, 95% CI 1.30-1.90) and OS (RR 1.32, 95% CI 1.07-1.64), after adjusting for clinical covariates. When tested on an independent validation cohort, the gene-signature risk score remained associated with shorter TTF (F-test, P = .002), compared favorably to WHO grade in stratifying cases by OS (P = .003 vs P = .10), and was significantly associated with worse OS (RR 1.86, 95% CI 1.19-2.88) on multivariate analysis. CONCLUSION The prognostic meningioma gene-expression signature and risk score presented may be useful for identifying patients at risk for recurrence.