Ashley Wu

Histopathological features predictive of local control of atypical meningioma after surgery and adjuvant radiotherapy

In BriefIn this article, Chen et al. investigate the impact of adjuvant radiotherapy in 182 patients undergoing primary resection of atypical meningioma. The authors find that adjuvant radiotherapy improves local control of atypical meningioma irrespective of extent of resection. Moreover, they discover that MIB1 labeling index, number of mitoses, and brain or bone invasion may be useful guidelines to select patients who are most likely to benefit from adjuvant radiotherapy after resection of atypical meningioma.

Preoperative and postoperative prediction of long-term meningioma outcomes

Background Meningiomas are stratified according to tumor grade and extent of resection, often in isolation of other clinical variables. Here, we use machine learning (ML) to integrate demographic, clinical, radiographic and pathologic data to develop predictive models for meningioma outcomes. Methods and findings We developed a comprehensive database containing information from 235 patients who underwent surgery for 257 meningiomas at a single institution from 1990 to 2015. The median follow-up was 4.3 years, and resection specimens were re-evaluated according to current diagnostic criteria, revealing 128 WHO grade I, 104 grade II and 25 grade III meningiomas. A series of ML algorithms were trained and tuned by nested resampling to create models based on preoperative features, conventional postoperative features, or both. We compared different algorithms’ accuracy as well as the unique insights they offered into the data. Machine learning models restricted to preoperative information, such as patient demographics and radiographic features, had similar accuracy for predicting local failure (AUC = 0.74) or overall survival (AUC = 0.68) as models based on meningioma grade and extent of resection (AUC = 0.73 and AUC = 0.72, respectively). Integrated models incorporating all available demographic, clinical, radiographic and pathologic data provided the most accurate estimates (AUC = 0.78 and AUC = 0.74, respectively). From these models, we developed decision trees and nomograms to estimate the risks of local failure or overall survival for meningioma patients. Conclusions Clinical information has been historically underutilized in the prediction of meningioma outcomes. Predictive models trained on preoperative clinical data perform comparably to conventional models trained on meningioma grade and extent of resection. Combination of all available information can help stratify meningioma patients more accurately.

Clinical Applications of Quantitative 3-Dimensional MRI Analysis for Pediatric Embryonal Brain Tumors

PURPOSE To investigate the prognostic utility of quantitative 3-dimensional magnetic resonance imaging radiomic analysis for primary pediatric embryonal brain tumors. METHODS AND MATERIALS Thirty-four pediatric patients with embryonal brain tumor with concurrent preoperative T1-weighted postcontrast (T1PG) and T2-weighted fluid-attenuated inversion recovery (FLAIR) magnetic resonance images were identified from an institutional database. The median follow-up period was 5.2 years. Radiomic features were extracted from axial T1PG and FLAIR contours using MATLAB, and 15 features were selected for analysis based on qualitative radiographic features with prognostic significance for pediatric embryonal brain tumors. Logistic regression, linear regression, receiver operating characteristic curves, the Harrell C index, and the Somer D index were used to test the relationships between radiomic features and demographic variables, as well as clinical outcomes. RESULTS Pediatric embryonal brain tumors in older patients had an increased normalized mean tumor intensity (P = .05, T1PG), decreased tumor volume (P = .02, T1PG), and increased markers of heterogeneity (P ≤ .01, T1PG and FLAIR) relative to those in younger patients. We identified 10 quantitative radiomic features that delineated medulloblastoma, pineoblastoma, and supratentorial primitive neuroectodermal tumor, including size and heterogeneity (P ≤ .05, T1PG and FLAIR). Decreased markers of tumor heterogeneity were predictive of neuraxis metastases and trended toward significance (P = .1, FLAIR). Tumors with an increased size (area under the curve = 0.7, FLAIR) and decreased heterogeneity (area under the curve = 0.7, FLAIR) at diagnosis were more likely to recur. CONCLUSIONS Quantitative radiomic features are associated with pediatric embryonal brain tumor patient age, histology, neuraxis metastases, and recurrence. These data suggest that quantitative 3-dimensional magnetic resonance imaging radiomic analysis has the potential to identify radiomic risk features for pediatric patients with embryonal brain tumors.

Comprehensive Molecular Profiling Identifies FOXM1 as a Key Transcription Factor for Meningioma Proliferation

Meningioma is the most common primary intracranial tumor, but the molecular drivers of aggressive meningioma are incompletely understood. Using 280 human meningioma samples and RNA sequencing, immunohistochemistry, whole-exome sequencing, DNA methylation arrays, and targeted gene expression profiling, we comprehensively define the molecular profile of aggressive meningioma. Transcriptomic analyses identify FOXM1 as a key transcription factor for meningioma proliferation and a marker of poor clinical outcomes. Consistently, we discover genomic and epigenomic factors associated with FOXM1 activation in aggressive meningiomas. Finally, we define a FOXM1/Wnt signaling axis in meningioma that is associated with a mitotic gene expression program, poor clinical outcomes, and proliferation of primary meningioma cells. In summary, we find that multiple molecular mechanisms converge on a FOXM1/Wnt signaling axis in aggressive meningioma.