Background We investigated prognostic models based on clinical, radiologic, and radiomic feature to preoperatively identify meningiomas at risk for poor outcomes. Methods Retrospective review was performed for 303 patients who underwent resection of 314 meningiomas (57% World Health Organization grade I, 35% grade II, and 8% grade III) at two independent institutions, which comprised primary and external datasets. For each patient in the primary dataset, 16 radiologic and 172 radiomic features were extracted from preoperative magnetic resonance images, and prognostic features for grade, local failure (LF) or overall survival (OS) were identified using the Kaplan–Meier method, log-rank tests and recursive partitioning analysis. Regressions and random forests were used to generate and test prognostic models, which were validated using the external dataset. Results Multivariate analysis revealed that apparent diffusion coefficient hypointensity (HR 5.56, 95% CI 2.01–16.7, P = .002) was associated with high grade meningioma, and low sphericity was associated both with increased LF (HR 2.0, 95% CI 1.1–3.5, P = .02) and worse OS (HR 2.94, 95% CI 1.47–5.56, P = .002). Both radiologic and radiomic predictors of adverse meningioma outcomes were significantly associated with molecular markers of aggressive meningioma biology, such as somatic mutation burden, DNA methylation status, and FOXM1 expression. Integrated prognostic models combining clinical, radiologic, and radiomic features demonstrated improved accuracy for meningioma grade, LF, and OS (area under the curve 0.78, 0.75, and 0.78, respectively) compared to models based on clinical features alone. Conclusions Preoperative radiologic and radiomic features such as apparent diffusion coefficient and sphericity can predict tumor grade, LF, and OS in patients with meningioma.

Deep learning algorithms have recently been developed that utilize patient anatomy and raw imaging information to predict radiation dose, as a means to increase treatment planning efficiency and improve radiotherapy plan quality. Current state-of-the-art techniques rely on convolutional neural networks (CNNs) that use pixel-to-pixel loss to update network parameters. However, stereotactic body radiotherapy (SBRT) dose is often heterogeneous, making it difficult to model using pixel-level loss. Generative adversarial networks (GANs) utilize adversarial learning that incorporates image-level loss and is better suited to learn from heterogeneous labels. However, GANs are difficult to train and rely on compromised architectures to facilitate convergence. This study suggests an attention-gated generative adversarial network (DoseGAN) to improve learning, increase model complexity, and reduce network redundancy by focusing on relevant anatomy. DoseGAN was compared to alternative state-of-the-art dose prediction algorithms using heterogeneity index, conformity index, and various dosimetric parameters. All algorithms were trained, validated, and tested using 141 prostate SBRT patients. DoseGAN was able to predict more realistic volumetric dosimetry compared to all other algorithms and achieved statistically significant improvement compared to all alternative algorithms for the V 100 and V 120 of the PTV, V 60 of the rectum, and heterogeneity index. Advanced treatment techniques such as intensity modulated radiation therapy (IMRT) and volumetrically modulated arc therapy (VMAT) have become standard of care for many treatment sites 1,2. Creating clinically acceptable treatment plans using these advanced techniques requires extensive domain expertise and is exceedingly time consuming 3,4. To reduce the burden on clinical resources, the development of automated treatment planning technologies has accelerated in recent years 5-10. Historically, automated treatment planning technologies relied on selecting handcrafted features, such as spatial relationships between planning volumes, overlapping volume histograms, planning volume shapes, planning volume and field intersections, field shapes, planning volume depths , and distance-to-target histograms (DTH) 11-14. These techniques rely on machine learning algorithms such as gradient boosting, random forests, and support vector machines to find strong correlations between groups of weakly correlated predictive features 6,15-17. Such techniques achieve good performance on inherently structured data, but tend to struggle if the problem does not easily reduce to a structured format. Because of this, deep learning approaches have emerged that predict dose using fully connected layers 18. However, fully connected layers tend to not generalize well on highly dimensional data. Convolutional neural networks (CNNs) have emerged to solve many image processing tasks 4,6,19-23. Recently, encoder-decoder CNNs have been used to predict radiation dose from arbitrary patient anatomy. These method...

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To suggest an attention-aware, cycle-consistent generative adversarial network (A-CycleGAN) enhanced with variational autoencoding (VAE) as a superior alternative to current state-of-the-art MR-to-CT image translation methods.Materials and Methods: An attention-gating mechanism is incorporated into a discriminator network to encourage a more parsimonious use of network parameters, whereas VAE enhancement enables deeper discrimination architectures without inhibiting model convergence. Findings from 60 patients with head, neck, and brain cancer were used to train and validate A-CycleGAN, and findings from 30 patients were used for the holdout test set and were used to report final evaluation metric results using mean absolute error (MAE) and peak signal-to-noise ratio (PSNR).Results: A-CycleGAN achieved superior results compared with U-Net, a generative adversarial network (GAN), and a cycle-consistent GAN. The A-CycleGAN averages, 95% confidence intervals (CIs), and Wilcoxon signed-rank two-sided test statistics are shown for

Object The authors report the evolution of linear accelerator (LINAC)–based radiosurgery in the treatment of patients with intracranial meningiomas. They describe the technical aspects as well as clinical and radiological outcomes. Methods The authors performed a retrospective review of 161 patients harboring 194 intracranial meningiomas treated with various types of stereotactic irradiation at their institution between May 1991 and July 2002. Clinical and radiological follow-up data (mean follow-up period 32.5 months, range 6–125 months) were obtained in 128 patients (79.5%) with 156 meningiomas (80.4%). There were 88 women and 40 men whose mean age was 57.2 years (range 18–87 years). Stereotactic irradiation was the primary treatment in 44 patients, and 84 patients underwent resection prior to radiosurgery. Stereotactic radiosurgery (SRS) was used to treat 79 lesions and fractionated stereotactic radiotherapy (SRT) was used to treat 77. The mean dose for SRS was 1567 cGy (range 1200–2285 cGy) prescribed to a mean isodose line of 66.6% (range 50–90%). Stereotactic radiotherapy was delivered using a mean dose of 4839 cGy (range 2380–5400 cGy), prescribed to a mean isodose line of 89% (range 50–90%). The mean follow-up periods were 40 and 24 months in SRS- and SRT-treated patients, respectively. Tumor control was achieved in 58 SRT-treated benign meningiomas (90%) and in 70 SRT-treated lesions (97.2%). In patients with atypical meningiomas a considerably poorer prognosis was seen. Clinical improvement or stabilization of symptoms was observed in the majority of patients. Symptomatic complications were limited to four patients (5%) treated with SRS and four (5.2%) treated with SRT. Conclusions Stereotactic irradiation techniques have changed the neurosurgical approach to intracranial meningiomas. Either SRS or SRT delivered as a primary treatment in selected cases of skull base lesions or as an adjuvant after conservative resection has improved the management of these complex intracranial tumors.