The Essential Role of Open Data and Software for the Future of Ultrasound-Based Neuronavigation

With the recent developments in machine learning and modern graphics processing units (GPUs), there is a marked shift in the way intra-operative ultrasound (iUS) images can be processed and presented during surgery. Real-time processing of images to highlight important anatomical structures combined with in-situ display, has the potential to greatly facilitate the acquisition and interpretation of iUS images when guiding an operation. In order to take full advantage of the recent advances in machine learning, large amounts of high-quality annotated training data are necessary to develop and validate the algorithms. To ensure efficient collection of a sufficient number of patient images and external validity of the models, training data should be collected at several centers by different neurosurgeons, and stored in a standard format directly compatible with the most commonly used machine learning toolkits and libraries. In this paper, we argue that such effort to collect and organize large-scale multi-center datasets should be based on common open source software and databases. We first describe the development of existing open-source ultrasound based neuronavigation systems and how these systems have contributed to enhanced neurosurgical guidance over the last 15 years. We review the impact of the large number of projects worldwide that have benefited from the publicly available datasets “Brain Images of Tumors for Evaluation” (BITE) and “Retrospective evaluation of Cerebral Tumors” (RESECT) that include MR and US data from brain tumor cases. We also describe the need for continuous data collection and how this effort can be organized through the use of a well-adapted and user-friendly open-source software platform that integrates both continually improved guidance and automated data collection functionalities.publishedVersio

Benchmarking changepoint detection algorithms on cardiac time series

The pattern of state changes in a biomedical time series can be related to health or disease. This work presents a principled approach for selecting a changepoint detection algorithm for a specific task, such as disease classification. Eight key algorithms were compared, and the performance of each algorithm was evaluated as a function of temporal tolerance, noise, and abnormal conduction (ectopy) on realistic artificial cardiovascular time series data. All algorithms were applied to real data (cardiac time series of 22 patients with REM-behavior disorder (RBD) and 15 healthy controls) using the parameters selected on artificial data. Finally, features were derived from the detected changepoints to classify RBD patients from healthy controls using a K-Nearest Neighbors approach. On artificial data, Modified Bayesian Changepoint Detection algorithm provided superior positive predictive value for state change identification while Recursive Mean Difference Maximization (RMDM) achieved the highest true positive rate. For the classification task, features derived from the RMDM algorithm provided the highest leave one out cross validated accuracy of 0.89 and true positive rate of 0.87. Automatically detected changepoints provide useful information about subject's physiological state which cannot be directly observed. However, the choice of change point detection algorithm depends on the nature of the underlying data and the downstream application, such as a classification task. This work represents the first time change point detection algorithms have been compared in a meaningful way and utilized in a classification task, which demonstrates the effect of changepoint algorithm choice on application performance.Comment: 24 pages, 2 figure

Dose distribution in the thyroid gland following radiation therapy of breast cancer-a retrospective study

Purpose: To relate the development of post-treatment hypothyroidism with the dose distribution within the thyroid gland in breast cancer (BC) patients treated with loco-regional radiotherapy (RT). Methods and materials: In two groups of BC patients postoperatively irradiated by computer tomography (CT)- based RT, the individual dose distributions in the thyroid gland were compared with each other; Cases developed post-treatment hypothyroidism after multimodal treatment including 4-field RT technique. Matched patients in Controls remained free for hypothyroidism. Based on each patient’s dose volume histogram (DVH) the volume percentages of the thyroid absorbing respectively 20, 30, 40 and 50 Gy were then estimated (V20, V30, V40 and V50) together with the individual mean thyroid dose over the whole gland (MeanTotGy). The mean and median thyroid dose for the included patients was about 30 Gy, subsequently the total volume of the thyroid gland (VolTotGy) and the absolute volumes (cm3) receiving respectively < 30 Gy and ≥ 30 Gy were calculated (Vol < 30 and Vol ≥ 30) and analyzed. Results: No statistically significant inter-group differences were found between V20, V30, V40 and V50Gy or the median of MeanTotGy. The median VolTotGy in Controls was 2.3 times above VolTotGy in Cases (r = 0.003), with large inter-individual variations in both groups. The volume of the thyroid gland receiving < 30 Gy in Controls was almost 2.5 times greater than the comparable figure in Cases. Conclusions: We concluded that in patients with small thyroid glands after loco-radiotherapy of BC, the risk of post-treatment hypothyroidism depends on the volume of the thyroid gland.publishedVersio

Weight Loss and Mortality in Overweight and Obese Cancer Survivors: A Systematic Review

Background Excess adiposity is a risk factor for poorer cancer survival, but there is uncertainty over whether losing weight reduces the risk. We conducted a critical review of the literature examining weight loss and mortality in overweight or obese cancer survivors. Methods We systematically searched PubMed and EMBASE for articles reporting associations between weight loss and mortality (cancer-specific or all-cause) in overweight/obese patients with obesity-related cancers. Where available, data from the same studies on non-overweight patients were compared. Results Five articles describing observational studies in breast cancer survivors were included. Four studies reported a positive association between weight loss and mortality in overweight/obese survivors, and the remaining study observed no significant association. Results were similar for non-overweight survivors. Quality assessment indicated high risk of bias across studies. Conclusions There is currently a lack of observational evidence that weight loss improves survival for overweight and obese cancer survivors. However, the potential for bias in these studies is considerable and the results likely reflect the consequences of disease-related rather than intentional weight loss. There is a need for stronger study designs, incorporating measures of intentionality of weight loss, and extended to other cancers