Abstract-Horizontal roll vortex pairs are dynamical structures that transfer energy and emissions from wildfires into the atmosphere. The vortices form at the edges of an intense line wildfire and emulate two cylinders, which form two curvatures of a biconcave thermal lens. Wildfire plume provides a dielectric material for the dielectric lens, whose permittivity is influenced by the nature, quantity of constituents (e.g., potassium and graphitic carbon) and variation of temperature with height in the plume. The environment created by the plume is radio sub-refractive with an effect of spreading radio wave beams. A numerical experiment was carried out to quantify loss of Ultra High Frequency (UHF) radio signal intensity when high intensity wildfireinduced horizontal roll vortices intercept UHF propagation path. In the numerical experiment, a collimated radio wave beam was caused to propagate along fuel-fire interface of a very high intensity wildfire in which up to two roll vortex pairs are formed. Maximum temperature of the simulated wildfire was 1200 K. Flame potassium content was varied from 0.5-3.0%. At 3.0% potassium content, a vortex pair imposed a maximum radio ray divergence of 2.1 arcmins while two vortex respectively.
Background Treatment of brain metastases can be tailored to individual lesions with treatments such as stereotactic radiosurgery (SRS). Accurate surveillance of lesions is a prerequisite but challenging in patients with multiple lesions and prior imaging studies, in a process that is laborious and time consuming. We aimed to longitudinally track several lesions using a PACS-integrated lesion tracking tool (LTT) to evaluate the efficiency of a PACS-integrated lesion tracking workflow, and characterize the prevalence of heterogenous response to treatment after GK. Methods We selected a group of brain metastases patients treated with GK at our institution. We used a PACS-Integrated LTT to track the treatment response of each lesion after first GK intervention to maximally seven diagnostic follow-up scans. We evaluated the efficiency of this tool by comparing the number of clicks necessary to complete this task with and without the tool, and examined the prevalence of heterogenous response to treatment. Results A cohort of eighty patients was selected and 494 lesions measured and tracked longitudinally for a mean follow-up time of 374 days after first GK. Use of LTT significantly decreased number of necessary clicks. 81.7% of patients had heterogenous response to treatment at the end of follow-up. The prevalence increased with increasing number of lesions. Conclusions Lesions in a single patient often differ in their response to treatment, highlighting the importance of individual lesion size assessments for further treatment planning. PACS-integrated lesion tracking enables efficient lesion surveillance workflow and specific and objective result reports to treating clinicians.
Background While there are innumerable Machine learning (ML) research algorithms used for segmentation of gliomas, there is yet to be a US FDA cleared product. The aim of this study is to explore the systemic limitations of research algorithms that have prevented translation from concept to product by a review of the current research literature. Methods We performed a systematic literature review on four databases. Of 11,727 articles, 58 articles met the inclusion criteria and were used for data extraction and screening using TRIPOD. Results We found that while many articles were published on ML based glioma segmentation and report high accuracy results, there were substantial limitations in the methods and results portions of the papers that result in difficulty reproducing the methods and translation into clinical practice. Conclusion In addition, we identified that more than a third of the articles used the same publicly available BRaTS and TCIA datasets and are responsible for the majority of patient data on which ML algorithms were trained, which leads to limited generalizability and potential for overfitting and bias.
PURPOSE Identifying molecular subtypes in gliomas has prognostic and therapeutic value, traditionally after invasive neurosurgical tumor resection or biopsy. Recent advances using artificial intelligence (AI) show promise in using pre-therapy imaging for predicting molecular subtype. We performed a systematic review of recent literature on AI methods used to predict molecular subtypes of gliomas. METHODS Literature review conforming to PRSIMA guidelines was performed for publications prior to February 2021 using 4 databases: Ovid Embase, Ovid MEDLINE, Cochrane trials (CENTRAL), and Web of Science core-collection. Keywords included: artificial intelligence, machine learning, deep learning, radiomics, magnetic resonance imaging, glioma, and glioblastoma. Non-machine learning and non-human studies were excluded. Screening was performed using Covidence software. Bias analysis was done using TRIPOD guidelines. RESULTS 11,727 abstracts were retrieved. After applying initial screening exclusion criteria, 1,135 full text reviews were performed, with 82 papers remaining for data extraction. 57% used retrospective single center hospital data, 31.6% used TCIA and BRATS, and 11.4% analyzed multicenter hospital data. An average of 146 patients (range 34-462 patients) were included. Algorithms predicting IDH status comprised 51.8% of studies, MGMT 18.1%, and 1p19q 6.0%. Machine learning methods were used in 71.4%, deep learning in 27.4%, and 1.2% directly compared both methods. The most common algorithm for machine learning were support vector machine (43.3%), and for deep learning convolutional neural network (68.4%). Mean prediction accuracy was 76.6%. CONCLUSION Machine learning is the predominant method for image-based prediction of glioma molecular subtypes. Major limitations include limited datasets (60.2% with under 150 patients) and thus limited generalizability of findings. We recommend using larger annotated datasets for AI network training and testing in order to create more robust AI algorithms, which will provide better prediction accuracy to real world clinical datasets and provide tools that can be translated to clinical practice.
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