NS-HGlio: A generalizable and repeatable HGG segmentation and volumetric measurement AI algorithm for the longitudinal MRI assessment to inform RANO in trials and clinics.

BACKGROUND Accurate and repeatable measurement of high-grade glioma (HGG) enhancing (Enh.) and T2/FLAIR hyperintensity/edema (Ed.) is required for monitoring treatment response. 3D measurements can be used to inform the modified Response Assessment in Neuro-oncology criteria. We aim to develop an HGG volumetric measurement and visualization AI algorithm that is generalizable and repeatable. METHODS A single 3D-Convoluted Neural Network, NS-HGlio, to analyze HGG on MRIs using 5-fold cross validation was developed using retrospective (557 MRIs), multicentre (38 sites) and multivendor (32 scanners) dataset divided into training (70%), validation (20%), and testing (10%). Six neuroradiologists created the ground truth (GT). Additional Internal validation (IV, three institutions) using 70 MRIs, and External validation (EV, single institution) using 40 MRIs through measuring the Dice Similarity Coefficient (DSC) of Enh., Ed. ,and Enh. + Ed. (WholeLesion/WL) tumor tissue and repeatability testing on 14 subjects from the TCIA MGH-QIN-GBM dataset using volume correlations between timepoints were performed. RESULTS IV Preoperative median DSC Enh. 0.89 (SD 0.11), Ed. 0.88 (0.28), WL 0.88 (0.11). EV Preoperative median DSC Enh. 0.82 (0.09), Ed. 0.83 (0.11), WL 0.86 (0.06). IV Postoperative median DSC Enh. 0.77 (SD 0.20), Ed 0.78. (SD 0.09), WL 0.78 (SD 0.11). EV Postoperative median DSC Enh. 0.75 (0.21), Ed 0.74 (0.12), WL 0.79 (0.07). Repeatability testing; Intraclass Correlation Coefficient of 0.95 Enh. and 0.92 Ed. CONCLUSION NS-HGlio is accurate, repeatable, and generalizable. The output can be used for visualization, documentation, treatment response monitoring, radiation planning, intra-operative targeting, and estimation of Residual Tumor Volume among others

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Evaluation of WRF Microphysics Schemes Performance Forced by Reanalysis and Satellite-Based Precipitation Datasets for Early Warning System of Extreme Storms in Hyper Arid Environment

In this paper, we will investigate the influence of the microphysics schemes on the rainfall pattern of the extreme storm that impacted Egypt on 12 March 2020. The aim is to improve rainfall forecasting using the numerical Weather Research and Forecasting (WRF) model for an effective Early Warning System (EWS). The performance of six microphysics schemes were evaluated using the Model Object-based Evaluation analysis tool (MODE) forced by three selected satellite-based datasets (CMORPH, PERSIANN, PERSIANN-CCS, etc.) and one reanalysis dataset (ERA5). Six numerical simulations were performed using the WRF model, considering the following microphysics schemes: Lin, WSM6, Goddard, Thompson, Morrison, and NSSL2C. The models were evaluated using both conventional statistical indices and MODE, which is much more suitable in such studies. The results showed that the Lin scheme outperformed the other schemes such as WSM6, Goddard, Thompson, Morrison, and NSSL2C, in rainfall forecasting. The Thompson scheme was found to be the least reliable scheme. An extension for this study is recommended in other regions where the observational rain gauges data are available

Peroneal tendon dislocation in talus fracture and diagnostic value of fleck sign

Introduction Talus fractures are not uncommon and one of the serious fractures in the foot and ankle. Peroneal tendon dislocation is one of the commonly missed soft tissue injuries which may have significant impact on the outcomes including persistent pain and swelling. They have been reported to be associated with calcaneum as well as talus fractures. Aim To report the incidence of peroneal tendon dislocation in talus fracture and the significance of fleck sign in the diagnosis of peroneal tendon dislocation. Methods We retrospectively reviewed 93 consecutive talus fractures in the period between 1/1/2011 to 1/11/2018. Inclusion criteria were: The patient underwent open reduction and internal fixation, had pre-operative CT scan that is available for review and three view ankle plain radiographs. Two independent authors review the radiographs for peroneal tendon dislocation, fleck sign and fracture classification, if any. Any dispute was resolved by the senior author.Patient records were reviewed for laterality, age, sex,mode of injury, associated injuries and operative interventions. 50 ankles met the inclusion criteria. 49 were males, mean age was 32.5 year and the predominant mode of injury was a fall from height. Results Peroneal tendon dislocation was found in ten patients out of 50 (20%). Risk of dislocation increased with severity of the fracture and neck fractures. Most of the dislocations were missed by surgeons and radiologist, and no additional procedures were done to address such an injury. The Fleck sign had a statistically significant correlation with peroneal tendons dislocations (p=.005) Conclusion Peroneal tendons dislocation is associated with as high as 20% of talus fractures. The authors recommend carefully reviewing CT scans by surgeons and radiologists alike to avoid missing such injury and allow for appropriate surgical approach utilization. The Fleck sign is a highly specific radiographic sign that has a statistically significant correlation with PT dislocation and hence we recommend intra-operative assessment of peroneal tendons in patients with the fleck sign.Other Information Published in: International Orthopaedics License: https://creativecommons.org/licenses/by/4.0See article on publisher's website: http://dx.doi.org/10.1007/s00264-020-04534-9</p

Outcome of primary deltoid ligament repair in acute ankle fractures: a meta-analysis of comparative studies

Purpose The indications of deltoid ligament repair in ankle injuries with widened medial clear space in the absence of medial malleolus fracture remain controversial. Many authors reported no difference in long-term functional outcomes, while others stated that persistent medial clear space widening and malreduction are higher when deltoid ligaments went without repair. This meta-analysis aims to report the current published evidence about the outcomes of deltoid ligament repair in ankle fractures. Methods Several databases were searched through May 2018 for comparative studies. The primary outcome was the medial clear space correction, while secondary outcomes included maintenance of medial clear space reduction, pain scores, functional outcome, and total complications if any. Three comparative studies met the inclusion criteria for the meta-analysis. The analysis included a total of 192 patients, 81 in the deltoid ligament repair group and 111 in the non-repair group. Results The medial clear space correction and maintenance of the said correction on final follow-up radiographs were superior in the deltoid ligament repair group. Although the pain scores were better in the repair group at the final follow-up, this did not result in a better functional outcome, with similar total complication rates. Conclusion In conclusion, those who had their deltoid ligament repaired had superior early and late radiological correction of the medial clear space, an indicator of the quality of ankle reduction with better pain scores. However, no differences in the functional outcome and complications rate were reported.Other Information Published in: International Orthopaedics License: https://creativecommons.org/licenses/by/4.0See article on publisher's website: http://dx.doi.org/10.1007/s00264-019-04416-9</p