Purpose: Gliomas are the most common and aggressive type of brain tumors due to their infiltrative nature and rapid progression. The process of distinguishing tumor boundaries from healthy cells is still a challenging task in the clinical routine. Fluid-attenuated inversion recovery (FLAIR) MRI modality can provide the physician with information about tumor infiltration. Therefore, this paper proposes a new generic deep learning architecture, namely DeepSeg, for fully automated detection and segmentation of the brain lesion using FLAIR MRI data. Methods: The developed DeepSeg is a modular decoupling framework. It consists of two connected core parts based on an encoding and decoding relationship. The encoder part is a convolutional neural network (CNN) responsible for spatial information extraction. The resulting semantic map is inserted into the decoder part to get the full-resolution probability map. Based on modified U-Net architecture, different CNN models such as residual neural network (ResNet), dense convolutional network (DenseNet), and NASNet have been utilized in this study. Results: The proposed deep learning architectures have been successfully tested and evaluated on-line based on MRI datasets of brain tumor segmentation (BraTS 2019) challenge, including s336 cases as training data and 125 cases for validation data. The dice and Hausdorff distance scores of obtained segmentation results are about 0.81 to 0.84 and 9.8 to 19.7 correspondingly. Conclusion: This study showed successful feasibility and comparative performance of applying different deep learning models in a new DeepSeg framework for automated brain tumor segmentation in FLAIR MR images. The proposed DeepSeg is open source and freely available at https://github.com/razeineldin/DeepSeg/.

The benefits of intraoperative magnetic resonance (MR) imaging for diagnostic and therapeutic measures are as follows: 1) intraoperative update of data sets for navigational systems, 2) intraoperative resection control of brain tumors, and 3) frameless and frame-based on-line MR-guided interventions. The concept of an intraoperative MR scanner in the sterile environment of operating theater is presented, and its advantages, disadvantages, and limitations are discussed. A 0.2-tesla magnet (Magnetom Open; Siemens AG, Erlangen, Germany) inside a radiofrequency cabin with a radiofrequency-shielded sliding door was installed adjacent to one of the operating theaters. A specially designed patient transport system carried the patient in a fixed position on an air cushion to the scanner and back to the surgeon. In a series of 27 patients, intraoperative resection control was performed in 13 cases, with intraoperative reregistration in 4 cases. Biopsies, cyst aspirations, and catheter placements (mainly frameless) were performed under direct MR visualization with fast image sequences. The MR-compatible equipment and the patient transport system are safe and reliable. Intraoperative MR imaging is a safe and successful tool for surgical resection control and is clearly superior to computed tomography. Intraoperative acquisition of data sets eliminates the problem of brain shift in conventional navigational systems. Finally, on-line MR-guided interventional procedures can be performed easily with this setting. As with all MR systems, individual testing with phantoms, application of correction programs, and determination of the optimal amount of contrast media are absolute prerequisites to guarantee patient safety and surgical success.

OBJECTIVE. The use of intraoperative magnetic resonance imaging (MRI) in neurosurgery has increased rapidly, and a variety of concepts have recently be

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.

To improve prognosis in recurrent glioblastoma we developed a treatment protocol based on a combination of drugs not traditionally thought of as cytotoxic chemotherapy agents but that have a robust history of being well-tolerated and are already marketed and used for other non-cancer indications. Focus was on adding drugs which met these criteria: a) were pharmacologically well characterized, b) had low likelihood of adding to patient side effect burden, c) had evidence for interfering with a recognized, well-characterized growth promoting element of glioblastoma, and d) were coordinated, as an ensemble had reasonable likelihood of concerted activity against key biological features of glioblastoma growth. We found nine drugs meeting these criteria and propose adding them to continuous low dose temozolomide, a currently accepted treatment for relapsed glioblastoma, in patients with recurrent disease after primary treatment with the Stupp Protocol. The nine adjuvant drug regimen, Coordinated Undermining of Survival Paths, CUSP9, then are aprepitant, artesunate, auranofin, captopril, copper gluconate, disulfiram, ketoconazole, nelfinavir, sertraline, to be added to continuous low dose temozolomide. We discuss each drug in turn and the specific rationale for use- how each drug is expected to retard glioblastoma growth and undermine glioblastoma’s compensatory mechanisms engaged during temozolomide treatment. The risks of pharmacological interactions and why we believe this drug mix will increase both quality of life and overall survival are reviewed.

Object: High-grade gliomas (HGGs) and metastasis (MET) are the most common intracranial lesions in neurosurgical routine. Both of them show an invasive growth pattern extending into neural tissue beyond the margins of contrast enhancement on MRI. These “undetected” areas might be the origin of early tumor recurrence. The aim of the present study was to evaluate whether 5-aminolevulinic acid (5-ALA) fluorescence provides an additional benefit in detection of invasive tumor compared with intraoperative MRI (iMRI). Methods: The authors prospectively enrolled 45 patients harboring contrast-enhancing lesions, in whom gross-total resection was intended. All patients had surgery in which iMRI and 5-ALA–guided resection were used following a specific protocol. First, a typical white light tumor resection was performed. Then, spatial location of residual fluorescence was marked. After that, an iMRI was performed and residual uptake of contrast was marked. Navigated biopsy samples were taken from all marked areas and from additional sites according to the surgeon's judgment. Cross tables and receiver operating characteristic curves were calculated, assessing performance of the imaging methods for tumor detection alone and for combined detection of infiltration zone and solid tumor (pathological tissue). Also, correlations of histopathological findings with imaging results were tested using Spearman rho. Results: Thirty-four patients with HGGs and 11 with METs were enrolled. Three patients harboring a MET showed no 5-ALA enhancement and were excluded; 127 histopathological samples were harvested in the remaining patients. In HGG, sensitivity for tumor detection was significantly higher (p < 0.001) in 5-ALA (0.85) than in iMRI (0.41). Specificity was significantly lower (p < 0.001) in 5-ALA (0.43) than in iMRI (0.70). For detection of pathological tissue, 5-ALA significantly exceeded iMRI in specificity (0.80 vs 0.60) and sensitivity (0.91 vs 0.66) (p < 0.001). Imaging results of iMRI and 5-ALA did not correlate significantly; only 5-ALA showed a significant correlation with final histopathological diagnosis of the specimen and with typical histopathological features of HGGs. In METs, sensitivity and specificity for tumor detection were equal in 5-ALA and iMRI. Both techniques showed high values for sensitivity (0.75) and specificity (0.80). The odds ratio for detection of tumor tissue was 12 for both techniques. Concerning pathological tissue, no statistically significant difference was found either. Imaging results of iMRI and 5-ALA correlated significantly (p < 0.022), as with final histopathological diagnosis in METs. Conclusions: In METs, due to the rate of nonenhancing lesions, the authors found no additional benefit of 5-ALA compared with iMRI. In HGG, imaging results of 5-ALA and iMRI are significantly different at the border zone; 5-ALA has a higher sensitivity and a lower specificity for tumor detection than Gd-DTPA–enhanced iMRI. For detection of infiltrating tumor at the border of the resection cavity, 5-ALA is superior to Gd-DTPA–enhanced iMRI concerning both sensitivity and specificity. Thus, use of 5-ALA in addition to iMRI might be beneficial to maximize extent of resection. Clinical synergistic effects will be evaluated in a prospective randomized trial.