Multimodal Brain Tumor Classification Using Convolutional Tumnet Architecture.

The most common and aggressive tumor is brain malignancy, which has a short life span in the fourth grade of the disease. As a result, the medical plan may be a crucial step toward improving the well-being of a patient. Both diagnosis and therapy are part of the medical plan. Brain tumors are commonly imaged with magnetic resonance imaging (MRI), positron emission tomography (PET), and computed tomography (CT). In this paper, multimodal fused imaging with classification and segmentation for brain tumors was proposed using the deep learning method. The MRI and CT brain tumor images of the same slices (308 slices of meningioma and sarcoma) are combined using three different types of pixel-level fusion methods. The presence/absence of a tumor is classified using the proposed Tumnet technique, and the tumor area is found accordingly. In the other case, Tumnet is also applied for single-modal MRI/CT (561 image slices) for classification. The proposed Tumnet was modeled with 5 convolutional layers, 3 pooling layers with ReLU activation function, and 3 fully connected layers. The first-order statistical fusion metrics for an average method of MRI-CT images are obtained as SSIM tissue at 83%, SSIM bone at 84%, accuracy at 90%, sensitivity at 96%, and specificity at 95%, and the second-order statistical fusion metrics are obtained as the standard deviation of fused images at 79% and entropy at 0.99. The entropy value confirms the presence of additional features in the fused image. The proposed Tumnet yields a sensitivity of 96%, an accuracy of 98%, a specificity of 99%, normalized values of the mean of 0.75, a standard deviation of 0.4, a variance of 0.16, and an entropy of 0.90.

Association of imaging classification and histopathological grading in primary intraosseous meningioma of the skull.

BACKGROUND: Primary intraosseous meningioma of the skull (PIMS) is a rare type of primary extradural meningioma (PEM) involving cranial bone. The existing literature strongly suggest the importance of radiological feacures in pathological diagnosis of PIMS. Thereby, the aim of this study is to investigate the association between imaging classification and histopathological grading in PIMS. METHODS: In this retrospective study, we retrospectively analyzed the computed tomography scan/magnetic resonance imaging and pathological data pertaining to patients with pathologically proven PIMS. The association between radiological features, imaging classification, and histopathological grading was analyzed using logistic regression analysis. RESULTS: In this study, data of 25 patients with PIMS were assessed. The univariate logistic regression analysis results showed significant correlation between histopathological grading and imaging classification (OR: 22.5; 95% CI: 2.552-198.378; p = 0.005), intra- and extracalvarial extension (OR: 7.2; 95% CI: 1.066-48.639; p = 0.043), and tumor margin (OR: 7.19; 95% CI: 1.06-47.61; p = 0.043). According to the results of multivariate logistic regression analysis, imaging classification was the strongest independent risk factor for high-grade PIMS, and the risk of aggressiveness of osteoblastic type of PIMS was 16.664 times higher than that of osteolytic type of PIMS (OR: 16.664; 95% CI: 1.15-241.508; p = 0.039). CONCLUSIONS: Imaging classification is an independent risk factor for high-grade PIMS.

Clinical implications of DNA methylation-based integrated classification of histologically defined grade 2 meningiomas.

The combination of DNA methylation analysis with histopathological and genetic features allows for a more accurate risk stratification and classification of meningiomas. Nevertheless, the implications of this classification for patients with grade 2 meningiomas, a particularly heterogeneous tumor entity, are only partially understood. We correlate the outcomes of histopathologically confirmed grade 2 meningioma with an integrated molecular-morphologic risk stratification and determine its clinical implications. Grade 2 meningioma patients treated at our institution were re-classified using an integrated risk stratification involving DNA methylation array-based data, copy number assessment and TERT promoter mutation analyses. Grade 2 meningioma cases according to the WHO 2021 criteria treated between 2007 and 2021 (n = 100) were retrospectively analyzed. The median clinical and radiographic follow-up periods were 59.8 and 54.4 months. A total of 38 recurrences and 17 deaths were observed. The local control rates of the entire cohort after 2-, 4-, and 6-years were 84.3%, 68.5%, and 50.8%, with a median local control time of 77.2 months. The distribution of the integrated risk groups were as follows: 31 low, 54 intermediate, and 15 high risk cases. In the multivariable Cox regression analysis, integrated risk groups were significantly associated with the risk of local recurrence (hazard ratio (HR) intermediate: 9.91, HR high-risk: 7.29, p < 0.01). Gross total resections decreased the risk of local tumor progression (HR gross total resection: 0.19, p < 0.01). The comparison of 1p status and integrated risk groups (low vs. intermediate/high) revealed nearly identical local control rates within their respective subgroups. In summary, only around 50% of WHO 2021 grade 2 meningiomas have an intermediate risk profile. Integrated molecular risk stratification is crucial to guide the management of patients with grade 2 tumors and should be routinely applied to avoid over- and undertreatment, especially concerning the use of adjuvant radiotherapy.

Leveraging single-cell sequencing to classify and characterize tumor subgroups in bulk RNA-sequencing data.

PURPOSE: Accurate classification of cancer subgroups is essential for precision medicine, tailoring treatments to individual patients based on their cancer subtypes. In recent years, advances in high-throughput sequencing technologies have enabled the generation of large-scale transcriptomic data from cancer samples. These data have provided opportunities for developing computational methods that can improve cancer subtyping and enable better personalized treatment strategies. METHODS: Here in this study, we evaluated different feature selection schemes in the context of meningioma classification. To integrate interpretable features from the bulk (n = 77 samples) and single-cell profiling (∼ 10 K cells), we developed an algorithm named CLIPPR which combines the top-performing single-cell models, RNA-inferred copy number variation (CNV) signals, and the initial bulk model to create a meta-model. RESULTS: While the scheme relying solely on bulk transcriptomic data showed good classification accuracy, it exhibited confusion between malignant and benign molecular classes in approximately ∼ 8% of meningioma samples. In contrast, models trained on features learned from meningioma single-cell data accurately resolved the sub-groups confused by bulk-transcriptomic data but showed limited overall accuracy. CLIPPR showed superior overall accuracy and resolved benign-malignant confusion as validated on n = 789 bulk meningioma samples gathered from multiple institutions. Finally, we showed the generalizability of our algorithm using our in-house single-cell (∼ 200 K cells) and bulk TCGA glioma data (n = 711 samples). CONCLUSION: Overall, our algorithm CLIPPR synergizes the resolution of single-cell data with the depth of bulk sequencing and enables improved cancer sub-group diagnoses and insights into their biology.

Integrated Molecular-Morphologic Meningioma Classification: A Multicenter Retrospective Analysis, Retrospectively and Prospectively Validated.

PURPOSE: Meningiomas are the most frequent primary intracranial tumors. Patient outcome varies widely from benign to highly aggressive, ultimately fatal courses. Reliable identification of risk of progression for individual patients is of pivotal importance. However, only biomarkers for highly aggressive tumors are established (CDKN2A/B and TERT), whereas no molecularly based stratification exists for the broad spectrum of patients with low- and intermediate-risk meningioma. METHODS: DNA methylation data and copy-number information were generated for 3,031 meningiomas (2,868 patients), and mutation data for 858 samples. DNA methylation subgroups, copy-number variations (CNVs), mutations, and WHO grading were analyzed. Prediction power for outcome was assessed in a retrospective cohort of 514 patients, validated on a retrospective cohort of 184, and on a prospective cohort of 287 multicenter cases. RESULTS: Both CNV- and methylation family-based subgrouping independently resulted in increased prediction accuracy of risk of recurrence compared with the WHO classification (c-indexes WHO 2016, CNV, and methylation family 0.699, 0.706, and 0.721, respectively). Merging all risk stratification approaches into an integrated molecular-morphologic score resulted in further substantial increase in accuracy (c-index 0.744). This integrated score consistently provided superior accuracy in all three cohorts, significantly outperforming WHO grading (c-index difference P = .005). Besides the overall stratification advantage, the integrated score separates more precisely for risk of progression at the diagnostically challenging interface of WHO grade 1 and grade 2 tumors (hazard ratio 4.34 [2.48-7.57] and 3.34 [1.28-8.72] retrospective and prospective validation cohorts, respectively). CONCLUSION: Merging these layers of histologic and molecular data into an integrated, three-tiered score significantly improves the precision in meningioma stratification. Implementation into diagnostic routine informs clinical decision making for patients with meningioma on the basis of robust outcome prediction.

A clinically applicable integrative molecular classification of meningiomas.

Meningiomas are the most common primary intracranial tumour in adults1. Patients with symptoms are generally treated with surgery as there are no effective medical therapies. The World Health Organization histopathological grade of the tumour and the extent of resection at surgery (Simpson grade) are associated with the recurrence of disease; however, they do not accurately reflect the clinical behaviour of all meningiomas2. Molecular classifications of meningioma that reliably reflect tumour behaviour and inform on therapies are required. Here we introduce four consensus molecular groups of meningioma by combining DNA somatic copy-number aberrations, DNA somatic point mutations, DNA methylation and messenger RNA abundance in a unified analysis. These molecular groups more accurately predicted clinical outcomes compared with existing classification schemes. Each molecular group showed distinctive and prototypical biology (immunogenic, benign NF2 wild-type, hypermetabolic and proliferative) that informed therapeutic options. Proteogenomic characterization reinforced the robustness of the newly defined molecular groups and uncovered highly abundant and group-specific protein targets that we validated using immunohistochemistry. Single-cell RNA sequencing revealed inter-individual variations in meningioma as well as variations in intrinsic expression programs in neoplastic cells that mirrored the biology of the molecular groups identified.

WHO classification of meningiomas-A single institutional experience.

INTRODUCTION: Meningiomas are among the most common intracranial neoplasms worldwide. The World Health Organization (WHO) has classified the neoplasm into three grades with each grade having several histological variants. Several studies done in blacks have shown differences with Caucasian populations regarding the occurrence of histological variants. Our study sought to examine the histological variants of meningioma seen in a predominantly black population using the WHO grading system. METHODS: We conducted a retrospective study of all meningiomas seen in our hospital facility for over twenty years. An analysis of data from all the patients diagnosed with meningioma, who also had surgical biopsies taken, was done. The meningiomas were graded using the WHO grading system and also classified into different histological variants within each grade as described by the WHO study group. RESULTS: The study included a total number of 163 biopsies. There were more females diagnosed with meningiomas with a female to male ratio of 1.4. Most of the tumors were grade one, however, there were more males with malignant meningiomas. Transitional meningiomas were the most commonly seen variants among the grade one tumors while atypical and anaplastic were most common in grades two and three, respectively. CONCLUSION: A larger population-based study is needed to provide epidemiological data on the occurrence of meningiomas in blacks.

Role of adjuvant radiotherapy in atypical (WHO grade II) and anaplastic (WHO grade III) meningiomas: a systematic review.

The systematic adoption of the histopathologic criteria provided by the 2016 update of the WHO classification of brain tumors has markedly increased the relative proportion of atypical and anaplastic meningiomas. These tumors exhibit a much greater recurrence rate compared to benign meningiomas, which negatively impacts survival. In recent years, the publication of numerous retrospective case series, yet no randomized controlled trials, on the impact of radiation therapy in non-benign meningioma, has yielded conflicting evidence. At present, maximum safe resection, including the dural attachment, is the preferred primary treatment modality for all types of meningiomas. Adjuvant radiotherapy is currently recommended for subtotally resected grade II and for all grade III meningiomas. However, in grade II meningiomas achieving complete resection, close radiologic and clinical observation is a feasible option. Despite the great amount of non-benign meningiomas available and eligible for trials, there is a striking lack of prospective studies testing adjuvant therapies against observation for this subset of patients. An updated and systematic literature review is provided on the effectiveness and indications of radiotherapy on grade II and III meningiomas.

Novel histopathological classification of meningiomas based on dural invasion.

AIMS: Histological invasion into the adjacent brain parenchyma is frequently investigated in meningioma because it is an important morphological criterion for grade II meningioma according to the 2016 WHO classification. However, few studies have focused on dural invasion of meningiomas. Herein, we propose a novel histopathological classification based on dural invasion of meningiomas. METHODS: Forty-nine cases with WHO grade I meningiomas who underwent Simpson grade I removal were collected. After the meningeal layer (ML) and periosteal layer (PL) of dura mater were visualised by Masson's trichrome stain, we evaluated the depth (to the ML and PL) and the patterns (1, expanding; 2, infiltrating) of dural invasion of meningiomas using serial paraffin sections. Invasion-associated markers, including Ki-67, matrix metalloproteinase (MMP)-1, MMP-9 and MMP-13, aquaporin 1 and Na-K-2Cl cotransporter, were quantitatively analysed by immunohistochemistry. RESULTS: Thirty-five cases (71.4%) showed the dural invasion. In 27 of these 35 cases (77.1%), dural invasion was localised in ML. Type 1 (expanding type) and type 2 (infiltrating type) invasions were observed in 23 and 12 cases, respectively. The recurrence rate in cases with type 2 invasion was significantly higher than that in cases with type 1 invasion. The percentage of MMP-1-positive tumour cells was also significantly higher in cases with dural invasion than those without, suggesting involvement of MMP-1 in dural invasion. CONCLUSIONS: We quantitatively evaluated the depth and patterns of dural invasion in meningiomas. The patterns of dural invasion were associated with meningioma recurrence.

Distinct genomic subclasses of high-grade/progressive meningiomas: NF2-associated, NF2-exclusive, and NF2-agnostic.

BACKGROUND: Genomic studies of high-grade/progressive meningiomas have reported a heterogeneous mutation spectrum, identifying few recurrently mutated genes. Most studies have been underpowered to detect genomic subclasses of aggressive meningiomas due to relatively small number of available samples. Here, we present a genomic survey of one of the largest multi-institutional cohorts of high-grade/progressive meningiomas to date. METHODS: 850 high-grade/progressive meningiomas, including 441 WHO grade 2 and 176 WHO grade 3 meningiomas and 220 progressive WHO grade 1 meningiomas, were tested as part of a clinical testing program by hybridization capture of 406 cancer-related genes to detect base substitutions, indels, amplifications, deletions, and rearrangements. Information from pathology reports, histopathology review, and patient clinical data was assessed. RESULTS: Genomic analyses converged to identify at least three distinct patterns of biologically-aggressive meningiomas. The first and most common contained NF2-mutant tumors (n = 426, 50%), was associated with male sex (64.4% %, p = 0.0001) and often harbored additional mutations in CDKN2A/B (24%), and the chromatin regulators ARID1A (9%), and KDM6A (6%). A second group (NF2-agnostic) featured TERT promoter (TERTp; n = 56) or TP53 mutations (n = 25) and were either NF2-mutant or wild-type, and displayed no association with either sex (p = 0.39). The remaining group generally lacked NF2 mutations, and accounted for 40% of the cases-with three subgroups. One consistent primarily of grade 3 lesions harboring alterations in chromatin regulators BAP1 (n = 22) or PBRM1 (n = 16). A second subgroup contained AKT1 (n = 26), PIK3CA (n = 14) and SMO (n = 7) mutant skull-based meningiomas, and a third mixed subgroup included 237 meningiomas with a heterogeneous spectrum of low frequency and non-recurrent alterations. CONCLUSIONS: Our findings indicate that the patterns of genomic alterations in high-grade/progressive meningiomas commonly group into three different categories. The most common NF2-associated canonical group frequently harbored CDKN2A/B alterations, which is potentially amenable to targeted therapies. An NF2-agnostic group harbored frequent TERTp and TP53 mutations. The final subclass, distinct from the canonical NF2 mutant associated pathway, was partly characterized by BAP1/PBRM1 alterations (rhabdoid/papillary histology) or skull-base disease. Overall, these data increase our understanding of the pathobiology of high-grade/progressive meningiomas and can guide the design of clinical trials. IRB APPROVAL STATUS: Reviewed and approved by Western IRB; Protocol No. 20152817.

Intracranial bony ball - An ossified variant of meningioma.

An ossified variant of meningioma in the intracranial region is very rare. We report a case of a 28-year-old female who presented with headache for 4 years. Imaging revealed an extra-axial calcified mass in the left parieto-occipital lobe. The differential diagnosis included osteoblastoma both intracranial and periosteal, calcifying pseudotumor of the neural axis, hyperostosis of the cranium, and ossifying fibroma. She underwent excision of the calcified space occupying lesion. Histopathology was suggestive of an ossified meningioma. At the end of 2-year follow-up, she was asymptomatic with no signs of recurrence.

Advances in Molecular Classification and Therapeutic Opportunities in Meningiomas.

PURPOSE OF REVIEW: Our understanding of the genetic and epigenetic alterations in meningioma and the underlying tumor biology of meningioma has significantly changed over the past decade and resulted in revision of prognostically relevant meningioma subclasses within and beyond the WHO classification of CNS tumors. RECENT FINDINGS: The 2016 WHO classification of CNS tumors recognizes WHO grade I, II, and III based on histopathological features. Recent work has identified genetic alterations with prognostic implications, including mutations of the TERT promoter, loss of function of the DMD gene, and inactivation of the tumor suppressor BAP-1. Studies of DNA methylation patterns in meningiomas have resulted in a novel and prognostically relevant meningioma subclassification schema. There have been major advances in our understanding of prognostically relevant genetic and epigenetic changes in meningioma which will hopefully allow for improvement in clinical trial design and the development of more effective therapies for meningioma.

Myxoid meningioma: A case report of an extremely rare subtype of metaplastic meningioma.

Myxoid meningioma is a rare histological variant of meningiomas grouped into the subtype of metaplastic meningiomas (World Health Organization grade I) characterized by the presence of myxoid areas and meningothelial cells along with unique ultrastructural features that help distinguish it from other meningiomas. Hereby, we report a case of a myxoid meningioma in a 40-year-old female who presented with altered sensorium and loss of consciousness. Imaging studies showed a dura-based solitary mass located in the left frontal lobe. The tumor was excised completely. Histopathology revealed a benign appearing myxoid neoplasm with uniform elongated cells, without any atypia and a very low mitotic activity. Immunohistochemical stains showed positivity for vimentin and epithelial membrane antigen confirming the diagnosis of myxoid meningioma. Only seven cases of myxoid meningioma have been reported till date in the literature and ours is the eighth case.

Grade I meningioma with disseminated bone disease: a rare clinical phenomenon.

Meningioma, the second most common primary tumour of the central nervous system, is classified into three different grades based on their characteristics. Each tumour grade includes different molecular subtype, growth potential, and thus, different prognosis. Grade I meningioma is the most common subtype with a benign course, in which systemic dissemination rarely occurs. We present the case of a 48-year-old male patient with a history of grade I meningioma who was referred 3 years after the initial diagnosis to our centre due to pelvic pain. Computed tomography (CT) images showed new pelvic bone lesions whose histopathological report was compatible with a grade I meningioma. Neither hormonal therapy concomitant with octreotide nor hydroxiurea treatments were effective. Very little is known about this entity's prevalence and treatment when disseminated disease occurs. Thus, we think it is important to increase the positive and negative clinical experiences in this setting.

A Rapid Robust Method for Subgrouping Non-NF2 Meningiomas According to Genotype and Detection of Lower Levels of M2 Macrophages in AKT1 E17K Mutated Tumours.

The majority of meningiomas are grade I, but some grade I tumours are clinically more aggressive. Recent advances in the genetic study of meningiomas has allowed investigation into the influence of genetics on the tumour microenvironment, which is important for tumorigenesis. We have established that the endpoint genotyping method Kompetitive Allele Specific PCR (KASP™) is a fast, reliable method for the screening of meningioma samples into different non-NF2 mutational groups using a standard real-time PCR instrument. This genotyping method and four-colour flow cytometry has enabled us to assess the variability in the largest immune cell infiltrate population, M2 macrophages (CD45+HLA-DR+CD14+CD163+) in 42 meningioma samples, and to suggest that underlying genetics is relevant. Further immunohistochemistry analysis comparing AKT1 E17K mutants to WHO grade I NF2-negative samples showed significantly lower levels of CD163-positive activated M2 macrophages in meningiomas with mutated AKT1 E17K, signifying a more immunosuppressive tumour microenvironment in NF2 meningiomas. Our data suggested that underlying tumour genetics play a part in the development of the immune composition of the tumour microenvironment. Stratifying meningiomas by mutational status and correlating this with their cellular composition will aid in the development of new immunotherapies for patients.

An enhanced deep learning approach for brain cancer MRI images classification using residual networks.

Cancer is the second leading cause of death after cardiovascular diseases. Out of all types of cancer, brain cancer has the lowest survival rate. Brain tumors can have different types depending on their shape, texture, and location. Proper diagnosis of the tumor type enables the doctor to make the correct treatment choice and help save the patient's life. There is a high need in the Artificial Intelligence field for a Computer Assisted Diagnosis (CAD) system to assist doctors and radiologists with the diagnosis and classification of tumors. Over recent years, deep learning has shown an optimistic performance in computer vision systems. In this paper, we propose an enhanced approach for classifying brain tumor types using Residual Networks. We evaluate the proposed model on a benchmark dataset containing 3064 MRI images of 3 brain tumor types (Meningiomas, Gliomas, and Pituitary tumors). We have achieved the highest accuracy of 99% outperforming the other previous work on the same dataset.

Cyclin E1 expression and malignancy in meningiomas.

OBJECTIVE: The aim of the present study was to analyze if the pathway Skp2-p27-cyclin E1 could also be a tumor progression marker for meningiomas. PATIENTS AND METHODS: We used quantitative real-time PCR to assess the relative expression levels of the genes coding for cyclin E1 (CCNE1), Skp2 (SKP2), and p27 (P27). The expression levels were compared in grades I to III meningiomas and among different histological subtypes of grade I meningiomas. RESULTS: Anaplastic meningiomas accounted for 4.9%, atypical meningiomas for 23.5% and grade I meningiomas for 71.6%.CCNE1 expression level was significantly higher in grade II compared to grade I meningiomas (p = 0.0027), and its expression level reliably predicts grade II meningiomas (ROC AUC = 0.731, p = 0.003). CCNE1 expression also correlated with SKP2 and P27 expression levels in grade I meningiomas (r = 0.539, p < 0.0001 and r = 0.687, p = <0.0001, respectively for CCNE1/SKP2 and CCNE1/P27, Spearman's test). Fibrous subtype among grade I meningiomas presented the highest expression levels of CCNE1, SKP2 and P27. Higher expression of cyclin E1 protein was detected in the nuclei of atypical meningiomas compared to grade I meningiomas. CONCLUSIONS: CCNE1 expression level predicts meningioma malignancy, and the fibrous subtype presents the highest gene expression levels among grade I meningiomas.

Discrimination of fresh frozen non-tumour and tumour brain tissue using spectrochemical analyses and a classification model.

Introduction: In order for brain tumours to be successfully treated, maximal resection is beneficial. A method to detect infiltrative tumour edges intraoperatively, improving on current methods would be clinically useful. Vibrational spectroscopy offers the potential to provide a handheld, reagent-free method for tumour detection.Purpose: This study was designed to determine the ability of both Raman and Fourier-transform infrared (FTIR) spectroscopy towards differentiating between normal brain tissue, glioma or meningioma.Method: Unfixed brain tissue, which had previously only been frozen, comprising normal, glioma or meningioma tissue was placed onto calcium fluoride slides for analysis using Raman and attenuated total reflection (ATR)-FTIR spectroscopy. Matched haematoxylin and eosin slides were used to confirm tumour areas. Analyses were then conducted to generate a classification model.Results: This study demonstrates the ability of both Raman and ATR-FTIR spectroscopy to discriminate tumour from non-tumour fresh frozen brain tissue with 94% and 97.2% of cases correctly classified, with sensitivities of 98.8% and 100%, respectively. This decreases when spectroscopy is used to determine tumour type.Conclusion: The study demonstrates the ability of both Raman and ATR-FTIR spectroscopy to detect tumour tissue from non-tumour brain tissue with a high degree of accuracy. This demonstrates the ability of spectroscopy when targeted for a cancer diagnosis. However, further improvement would be required for a classification model to determine tumour type using this technology, in order to make this tool clinically viable.

WHO grade I meningiomas: classification-tree for prognostic factors of survival.

World Health Organization (WHO) grade I meningiomas are intracranial extracerebral tumors, in which microsurgery as a stand-alone therapy provides high rates of disease control and low recurrence rates. Our aim was to identify prognostic factors of overall survival and time-to-retreat (OS; TTR) in a cohort of patients with surgically managed WHO grade I meningioma. Patients with WHO grade I meningiomas from a retrospectively (1990 to 2002) and prospectively managed (2003 to 2010) databank of Oslo University Hospital, Norway, were included. The mean follow-up was 9.2 ± 5.7 years, with a total of 11,414 patient-years. One thousand three hundred fifty-five patients were included. The mean age was 58 ± 13.2, mean Karnofsky Performance Status (KPS) 92.6 ± 26.1 and female-to-male ratio 2.5:1. The 1-year, 5-year, 10-year, 15-year, and 20-year probabilities were 0.98, 0.91, 0.87, 0.84, and 0.8 for TTR. Patient age (OR 0.92 [0.91, 0.94]), male sex (OR 0.59 [0.45, 0.76]), preoperative KPS ≥ 70 (OR 2.22 [1.59, 3.13]), skull base location (OR 0.77 [0.60, 1]), and the occurrence of a postoperative hematoma (OR 0.44 [0.26, 0.76]) were identified as independent prognostic factors of OS. Patient age (OR 1.02 [1.01, 1.03]) and skull base location (OR 0.30 [0.21, 0.45]) were independent predictors of decreased PFS. Using a recursive partitioning analysis, we suggest a classification tree for the prediction of 5-year PFS based on patient and tumor characteristics. The findings from this cohort of meningioma WHO I patients helps to identify patients at risk of recurrence and tailor the therapeutic management.

A prognostic model to personalize monitoring regimes for patients with incidental asymptomatic meningiomas.

BACKGROUND: Asymptomatic meningioma is a common incidental finding with no consensus on the optimal management strategy. We aimed to develop a prognostic model to guide personalized monitoring of incidental meningioma patients. METHODS: A prognostic model of disease progression was developed in a retrospective cohort (2007-2015), defined as: symptom development, meningioma-specific mortality, meningioma growth or loss of window of curability. Secondary endpoints included non-meningioma-specific mortality and intervention. RESULTS: Included were 441 patients (459 meningiomas). Over a median of 55 months (interquartile range, 37-80), 44 patients had meningioma progression and 57 died (non-meningioma-specific). Forty-four had intervention (at presentation, n = 6; progression, n = 20; nonprogression, n = 18). Model parameters were based on statistical and clinical considerations and included: increasing meningioma volume (hazard ratio [HR] 2.17; 95% CI: 1.53-3.09), meningioma hyperintensity (HR 10.6; 95% CI: 5.39-21.0), peritumoral signal change (HR 1.58; 95% CI: 0.65-3.85), and proximity to critical neurovascular structures (HR 1.38; 95% CI: 0.74-2.56). Patients were stratified based on these imaging parameters into low-, medium- and high-risk groups and 5-year disease progression rates were 3%, 28%, and 75%, respectively. After 5 years of follow-up, the risk of disease progression plateaued in all groups. Patients with an age-adjusted Charlson comorbidity index ≥6 (eg, an 80-year-old with chronic kidney disease) were 15 times more likely to die of other causes than to receive intervention at 5 years following diagnosis, regardless of risk group. CONCLUSIONS: The model shows that there is little benefit to rigorous monitoring in low-risk and older patients with comorbidities. Risk-stratified follow-up has the potential to reduce patient anxiety and associated health care costs.