Explainability of deep neural networks for MRI analysis of brain tumors

Purpose Artificial intelligence (AI), in particular deep neural networks, has achieved remarkable results for medical image analysis in several applications. Yet the lack of explainability of deep neural models is considered the principal restriction before applying these methods in clinical practice. Methods In this study, we propose a NeuroXAI framework for explainable AI of deep learning networks to increase the trust of medical experts. NeuroXAI implements seven state-of-the-art explanation methods providing visualization maps to help make deep learning models transparent. Results NeuroXAI has been applied to two applications of the most widely investigated problems in brain imaging analysis, i.e., image classification and segmentation using magnetic resonance (MR) modality. Visual attention maps of multiple XAI methods have been generated and compared for both applications. Another experiment demonstrated that NeuroXAI can provide information flow visualization on internal layers of a segmentation CNN. Conclusion Due to its open architecture, ease of implementation, and scalability to new XAI methods, NeuroXAI could be utilized to assist radiologists and medical professionals in the detection and diagnosis of brain tumors in the clinical routine of cancer patients. The code of NeuroXAI is publicly accessible at https://github.com/razeineldin/NeuroXAI

iRegNet: Non-rigid Registration of MRI to Interventional US for Brain-Shift Compensation using Convolutional Neural Networks

Accurate and safe neurosurgical intervention can be affected by intra-operative tissue deformation, known as brain-shift. In this study, we propose an automatic, fast, and accurate deformable method, called iRegNet, for registering pre-operative magnetic resonance images to intra-operative ultrasound volumes to compensate for brain-shift. iRegNet is a robust end-to-end deep learning approach for the non-linear registration of MRI-iUS images in the context of image-guided neurosurgery. Pre-operative MRI (as moving image) and iUS (as fixed image) are first appended to our convolutional neural network, after which a non-rigid transformation field is estimated. The MRI image is then transformed using the output displacement field to the iUS coordinate system. Extensive experiments have been conducted on two multi-location databases, which are the BITE and the RESECT. Quantitatively, iRegNet reduced the mean landmark errors from pre-registration value of (4.18 ± 1.84 and 5.35 ± 4.19 mm) to the lowest value of (1.47 ± 0.61 and 0.84 ± 0.16 mm) for the BITE and RESECT datasets, respectively. Additional qualitative validation of this study was conducted by two expert neurosurgeons through overlaying MRI-iUS pairs before and after the deformable registration. Experimental findings show that our proposed iRegNet is fast and achieves state-of-the-art accuracies outperforming state-of-the-art approaches. Furthermore, the proposed iRegNet can deliver competitive results, even in the case of non-trained images as proof of its generality and can therefore be valuable in intra-operative neurosurgical guidance

Facile C=O Bond Splitting of Carbon Dioxide Induced by Metal-Ligand Cooperativity in a Phosphinine Iron(0) Complex

New iron complexes [Cp*FeL](-) (1-sigma and 1-pi, Cp*=C5Me5) containing the chelating phosphinine ligand 2-(2 '-pyridyl)-4,6-diphenylphosphinine (L) have been prepared, and found to undergo facile reaction with CO2 under ambient conditions. The outcome of this reaction depends on the coordination mode of the versatile ligand L. Interaction of CO2 with the isomer 1-pi, in which L binds to Fe through the phosphinine moiety in an eta(5) fashion, leads to the formation of 3-pi, in which CO2 has undergone electrophilic addition to the phosphinine group. In contrast, interaction with 1-sigma-in which L acts as a sigma-chelating [P,N] ligand-leads to product 3-sigma in which one C=O bond has been completely broken. Such CO2 cleavage reactions are extremely rare for late 3d metals, and this represents the first such example mediated by a single Fe centre

Bevacizumab in temozolomide refractory high-grade gliomas: single-centre experience and review of the literature

BACKGROUND: Despite multidisciplinary treatment approaches, the prognosis for patients with high-grade glioma (HGG) is poor, with a median overall survival (OS) of 14.6 months for glioblastoma multiforme (GB). As high levels of vascular endothelial growth factor A (VEGF) are found in HGG, targeted anti-antiangiogenic therapy using the humanized monoclonal antibody bevacizumab (BEV) was studied in a series of clinical trials. Still, the discrepancy of BEV's efficacy with regard to initial clinical and radiological response and its reported failure to prolong survival remains to be explained. Here, we illustrate the effectiveness of BEV in recurrent HGG by summarizing our single-centre experience. METHODS: We have retrospectively investigated the effect of BEV in temozolomide refractory HGG in 39 patients treated at the University Hospital of Ulm, Germany. RESULTS: Median duration of BEV treatment was 12.5 weeks; 23% of patients received BEV for more than 6 months and 15% for more than 1 year, until clinical or radiological tumour progression led to discontinuation. Furthermore, Karnofsky performance status increased in 30.6% and steroid dose decreased in 39% of all patients. CONCLUSIONS: The review of literature reveals that phase II and III studies support BEV as an effective therapy in recurrent HGG, at least with regard to progression-free survival (PFS), but landmark phase III trials failed to prove benefit concerning OS. Here, we discuss reasons that may account for this observation. We conclude that prolonging PFS with maintenance of neurological function and personal and economic independency justifies the off-label use of BEV

ICAR: endoscopic skull‐base surgery

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