Extensive surgical cytoreduction and intraoperative hyperthermic intraperitoneal chemotherapy in patients with pseudomyxoma peritonei

Background: Pseudomyxoma peritonei remains a fatal disease. However, extensive surgical cytoreduction combined with intraoperative heated intraperitoneal chemotherapy (HIPEC) has recently emerged as a new treatment modality, which might improve survival. Methods: Patients underwent treatment if the tumour appeared to be technically resectable on preoperative abdominal computed tomography and there were no distant metastases. After aggressive surgical cytoreduction, HIPEC with the administration of mitomycin C was performed for 90 min. Depending on histological grading, patients received adjuvant 5-¯uorouracil and leucovorin therapy. Results: Forty-six patients were treated. Optimal surgical cytoreduction was obtained in 40 patients. Postoperative surgical complications occurred in 18 patients. Four patients died as a direct result of the treatment. Bone marrow suppression due to mitomycin C toxicity occurred in 22 patients. There was no other major toxicity related to the HIPEC procedure. After a median follow-up of 12 months, 40 patients are alive, eight of whom have proven recurrence. The actuarial survival rate (Kaplan±Meier) at 3 years was 81 per cent. Conclusion: These results con®rm that extensive surgery combined with HIPEC is feasible in patients with pseudomyxoma peritonei and that improved long-term survival might be achieved

Automated final lesion segmentation in posterior circulation acute ischemic stroke using deep learning

Final lesion volume (FLV) is a surrogate outcome measure in anterior circulation stroke (ACS). In posterior circulation stroke (PCS), this relation is plausibly understudied due to a lack of methods that automatically quantify FLV. The applicability of deep learning approaches to PCS is limited due to its lower incidence compared to ACS. We evaluated strategies to develop a convolutional neural network (CNN) for PCS lesion segmentation by using image data from both ACS and PCS patients. We included follow-up non-contrast computed tomography scans of 1018 patients with ACS and 107 patients with PCS. To assess whether an ACS lesion segmentation generalizes to PCS, a CNN was trained on ACS data (ACS-CNN). Second, to evaluate the performance of only including PCS patients, a CNN was trained on PCS data. Third, to evaluate the performance when combining the datasets, a CNN was trained on both datasets. Finally, to evaluate the performance of transfer learning, the ACS-CNN was fine-tuned using PCS patients. The transfer learning strategy outperformed the other strategies in volume agreement with an intra-class correlation of 0.88 (95% CI: 0.83–0.92) vs. 0.55 to 0.83 and a lesion detection rate of 87% vs. 41–77 for the other strategies. Hence, transfer learning improved the FLV quantification and detection rate of PCS lesions compared to the other strategies

Neurophysiological basis of rapid eye movement sleep behavior disorder: informing future drug development

Poul Jennum, Julie AE Christensen, Marielle Zoetmulder Department of Clinical Neurophysiology, Faculty of Health Sciences, Danish Center for Sleep Medicine, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark Abstract: Rapid eye movement (REM) sleep behavior disorder (RBD) is a parasomnia characterized by a history of recurrent nocturnal dream enactment behavior and loss of skeletal muscle atonia and increased phasic muscle activity during REM sleep: REM sleep without atonia. RBD and associated comorbidities have recently been identified as one of the most specific and potentially sensitive risk factors for later development of any of the alpha-synucleinopathies: Parkinson’s disease, dementia with Lewy bodies, and other atypical parkinsonian syndromes. Several other sleep-related abnormalities have recently been identified in patients with RBD/Parkinson’s disease who experience abnormalities in sleep electroencephalographic frequencies, sleep–wake transitions, wake and sleep stability, occurrence and morphology of sleep spindles, and electrooculography measures. These findings suggest a gradual involvement of the brainstem and other structures, which is in line with the gradual involvement known in these disorders. We propose that these findings may help identify biomarkers of individuals at high risk of subsequent conversion to parkinsonism. Keywords: motor control, brain stem, hypothalamus, hypocreti

Domain- and Task-Specific Transfer Learning For Medical Segmentation Tasks

Background and objectives: Transfer learning is a valuable approach to perform medical image segmentation in settings with limited cases available for training convolutional neural networks (CNN). Both the source task and the source domain influence transfer learning performance on a given target medical image segmentation task. This study aims to assess transfer learning-based medical segmentation task performance for various source task and domain combinations. Methods: CNNs were pre-trained on classification, segmentation, and self-supervised tasks on two domains: natural images and T1 brain MRI. Next, these CNNs were fine-tuned on three target T1 brain MRI segmentation tasks: stroke lesion, MS lesions, and brain anatomy segmentation. In all experiments, the CNN architecture and transfer learning strategy were the same. The segmentation accuracy on all target tasks was evaluated using the mIOU or Dice coefficients. The detection accuracy was evaluated for the stroke and MS lesion target tasks only. Results: CNNs pre-trained on a segmentation task on the same domain as the target tasks resulted in higher or similar segmentation accuracy compared to other source task and domain combinations. Pre-training a CNN on ImageNet resulted in a comparable, but not consistently higher lesion detection rate, despite the amount of training data used being 10 times larger. Conclusions: This study suggests that optimal transfer learning for medical segmentation is achieved with a similar task and domain for pre-training. As a result, CNNs can be effectively pre-trained on smaller datasets by selecting a source domain and task similar to the target domain and task