SAVE is fast and appears to be very effective in terms of first-pass complete reperfusion in patients with LVO.

Objectives Magnetic resonance imaging (MRI) is the method of choice for imaging meningiomas. Volumetric assessment of meningiomas is highly relevant for therapy planning and monitoring. We used a multiparametric deep-learning model (DLM) on routine MRI data including images from diverse referring institutions to investigate DLM performance in automated detection and segmentation of meningiomas in comparison to manual segmentations. Methods We included 56 of 136 consecutive preoperative MRI datasets [T1/T2-weighted, T1-weighted contrast-enhanced (T1CE), FLAIR] of meningiomas that were treated surgically at the University Hospital Cologne and graded histologically as tumour grade I (n = 38) or grade II (n = 18). The DLM was trained on an independent dataset of 249 glioma cases and segmented different tumour classes as defined in the brain tumour image segmentation benchmark (BRATS benchmark). The DLM was based on the DeepMedic architecture. Results were compared to manual segmentations by two radiologists in a consensus reading in FLAIR and T1CE. T1-weighted contrast-enhanced MRI

Introduction: Deep learning-based algorithms have demonstrated enormous performance in segmentation of medical images. We collected a dataset of multiparametric MRI and contour data acquired for use in radiosurgery, to evaluate the performance of deep convolutional neural networks (DCNN) in automatic segmentation of brain metastases (BM). Methods: A conventional U-Net (cU-Net), a modified U-Net (moU-Net) and a U-Net trained only on BM smaller than 0.4 ml (sU-Net) were implemented. Performance was assessed on a separate test set employing sensitivity, specificity, average false positive rate (AFPR), the dice similarity coefficient (DSC), Bland-Altman analysis and the concordance correlation coefficient (CCC). Results: A dataset of 509 patients (1223 BM) was split into a training set (469 pts) and a test set (40 pts). A combination of all trained networks was the most sensitive (0.82) while maintaining a specificity 0.83. The same model achieved a sensitivity of 0.97 and a specificity of 0.94 when considering only lesions larger than 0.06 ml (75% of all lesions). Type of primary cancer had no significant influence on the mean DSC per lesion (p = 0.60). Agreement between manually and automatically assessed tumor volumes as quantified by a CCC of 0.87 (95% CI, 0.77-0.93), was excellent. Conclusion: Using a dataset which properly captured the variation in imaging appearance observed in clinical practice, we were able to conclude that DCNNs reach clinically relevant performance for most lesions. Clinical applicability is currently limited by the size of the target lesion. Further studies should address if small targets are accurately represented in the test data.

Immune checkpoint inhibition with ipilimumab has revolutionized cancer immunotherapy and significantly improved outcomes of patients with advanced malignant melanoma. Local peripheral treatments (LPT), such as radiotherapy or electrochemotherapy, have been shown to modulate systemic immune responses, and preliminary data have raised the hypothesis that the combination of LPT with systemic immune checkpoint blockade might be beneficial. Clinical data from 127 consecutively treated melanoma patients at four cancer centers in Germany and Switzerland were analyzed. Patients received either ipilimumab (n ¼ 82) or ipilimumab and additional LPT (n ¼ 45) if indicated for local tumor control. The addition of LPT to ipilimumab significantly prolonged overall survival (OS; median OS 93 vs. 42 weeks, unadjusted HR, 0.46; P ¼ 0.0028). Adverse immunerelated events were not increased by the combination treatment, and LPT-induced local toxicities were in most cases mild. In a multivariable Cox regression analysis, we show that the effect of added LPT on OS remained statistically significant after adjusting for BRAF status, tumor stage, tumor burden, and central nervous system metastases (adjusted HR, 0.56; 95% confidence interval, 0.31-1.01, P ¼ 0.05). Our data suggest that the addition of LPT to ipilimumab is safe and effective in patients with metastatic melanoma irrespective of clinical disease characteristics and known risk factors. Induction of antitumor immune responses is most likely the underlying mechanism and warrants prospective validation.