Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

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

Effects of L-a-GP, β-GP AND 3-GPA, on platelet aggregation and thromboxane B₂ formation, induced by ADP, catecholamines and collagen

2,3 diphosphoglycerate (2,3-DPG), an important organic phosphate of the erythrocytes, has been shown to modify platelet function, in vitro. It inhibits platelet aggregation induced by adenosinodiphosphate (ADP), epinephrine (EPI), norepinephrine (NOR) and collagen (COL). It has been proposed that this occurs through an increase of the intraplatelet levels of c-AMP, or by inhibition of prostaglandin synthesis. 2-phosphoglycerate (2-PG) and 3-phosphoglycerate (3-PG) modify also platelet function, in vitro, in a way defined by their chemical structure. We studied the effects of L-a-glycerol phosphate (L-a-GP), β-glycerol phosphate (β-GP) and glyceraldehyde-3-phosphate (3-PGA), which are chemical analogs of 2,3-DPG, 2-PG and 3-PG, on platelet aggregation and thromboxane B₂ (TXB₂) formation. Aggregation studies, performed using the light transmission technique and platelet rich plasma, show that L-a-GP and 3-PGA inhibit platelet aggregation induced by ADP, EPI, NOR and COL. On the contrary, β-GP has no effect on platelet aggregation induced by the above mentioned compounds. Measurement of TXB₂ amounts - a stable metabolite of TXA₂ - by a radioimmunoassay method shows that L-a-GP and 3-PGA influence platelet secretion by inhibiting TXB₂ formation. The results show that L-a-GP and 3-PGA, phosphorylated compounds with a three carbon chain molecule and the phosphate group in either the first or the third position of the molecule, exert an effect on platelet function, they inhibit platelet aggregation and TXB₂ formation, induced by ADP, EPI, NOR and COL. β-GP, an analog of the other two compounds, but with the phosphate group in the second position of the molecule, is inactive. It does not influence ADP, catecholamines or collagen induced platelet aggregation and TXB₂ formation. These results support the hypothesis that L-a-GP, 3-PGA and their analogs exert their effect on platelet aggregation and TXB₂ synthesis, only when the phosphate is in the first or the third position of the molecule. A theoretical result of this state of events might be that release of these compounds, in vivo, might influence prostaglandin synthesis in the platelets and thus play a regulatory role in mediating haemostatic homeostasis.Το 2,3-διφωσφογλυκερινικό οξύ (2,3-DPG) σημαντικός οργανοφωσφορικός εστέρας των ερυθρών αιμοσφαιρίων, μεταβάλλει, in vitro, τη λειτουργικότητα των αιμοπεταλίων. Επιφέρει αναστολή της συσσώρευσης των αιμοπεταλίων που προκαλούν το ADP, η επινεφρίνη (EPI), η νορεπινεφρϊνη (NOR) και το κολλαγόνο (COL). Προτάθηκε ότι η δράση αυτή πιθανόν οφείλεται σε αύξηση των ενδοκυττάριων επιπέδων c-AMP ή στην αναστολή της σύνθεσης των προσταγλανδινών. Το 2-φωσφογλυκερινικό οξύ (2-PG) και το 3-φωσφογλυκερινικό οξύ (3-PG) επηρεάζουν, επίσης, τη λειτουργικότητα των αιμοπεταλίων, in vitro και μάλιστα κατά τρόπο ανάλογο της χημικής δομής τους. Μελετήσαμε τη δράση των ουσιών L-α-γλυκερίνη Ρ (L-a-GP), β-γλυκερίνη Ρ (β-GP) και γλυκεριναλδεύδη -3-Ρ (3-PGA), ουσιών χημικώς αναλόγων του 2,3-DPG, του 2-PG και του 3-PG και ενδιαμέσων προϊόντων του μεταβολισμού των ερυθρών αιμοσφαιρίων στη συσσώρευση των αιμοπεταλίων και τη παραγωγή θρομβοξάνης Β₂. Η φωτομετρική μέθοδος μελέτης της συσσώρευσης των αιμοπεταλίων δείχνει ότι η L-a-GP και η 3-PGA αναστέλλουν τη συσσώρευση των αιμοπεταλίων που προκαλούν το ADP, η EPI, η NOR και το COL. Αντίθετα, η β-GP δεν έχει καμιά επίδραση στη συσσώρευση που προκαλούν οι προαναφερθείσες ουσίες. Ο ραδιοανοσολογικός προσδιορισμός της θρομβοξάνης Β₂ ΤΧΒ₂-σταθερού μεταβολίτη της θρομβοξάνης Α₂- αποδεικνύει ότι οι ουσίες L-a-GP και 3-PGA επεμβαίνουν στην έκκριση των αιμοπεταλίων και αναστέλλουν το σχηματισμό ΤΧΒ₂. Τα αποτελέσματα δείχνουν ότι η L-a-GP και η 3-PGA, φωσφορυλιωμένα μόρια με τρία άτομα άνθρακα και τη φωσφορική ομάδα στην α' και γ' θέση αντίστοιχα, επιδρούν στη λειτουργία των αιμοπεταλίων και αναστέλλουν τη συσσώρευση των αιμοπεταλίων και την παραγωγή ΤΧΒ₂. Η β-GP, ουσία ανάλογη με τις προηγούμενες, αλλά με τη φωσφορική ομάδα στη β' θέση, παρουσιάζεται αδρανής. Δεν επηρεάζει τη συσσώρευση των αιμοπεταλίων και το σχηματισμό ΤΧΒ₂ που προκαλούν το ADP, οι κατεχολαμίνες και το κολλαγόνο. Συμπεραίνεται, λοιπόν, ότι η L-a-GP, η 3-PGA και τα ανάλογά τους, εξασκούν δράση στη συσσώρευση των αιμοπεταλίων που προκαλούν το ADP, η EPI, η NOR και το COL, μόνον όταν η φωσφορική ομάδα είναι στην α' ή στη γ' θέση του μορίου τους. Θεωρητικό επακόλουθο αυτών όλων είναι, ότι η απελευθέρωση των ουσιών αυτών στο αίμα, in vivo, μπορεί να επηρεάζει την παραγωγή προσταγλανδινών στα αιμοπετάλια και να δρα ρυθμιστικά στη διατήρηση της ομοιόστασης της κυκλοφορίας του αίματος

Η δράση των L-A-GP, B-GP και 3-PGA στη συσσώρευση των αιμοπεταλίων που προκαλούν το ADP, οι κατεχολαμίνες και το κολλαγόνο

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The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn).

Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and without contrast enhancement, T2-weighted images, and FLAIR images. However, some sequences are often missing in clinical practice due to time constraints or image artifacts, such as patient motion. Consequently, the ability to substitute missing modalities and gain segmentation performance is highly desirable and necessary for the broader adoption of these algorithms in the clinical routine. In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023. The primary objective of this challenge is to evaluate image synthesis methods that can realistically generate missing MRI modalities when multiple available images are provided. The ultimate aim is to facilitate automated brain tumor segmentation pipelines. The image dataset used in the benchmark is diverse and multi-modal, created through collaboration with various hospitals and research institutions

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset