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

Central vein sign for multiple sclerosis: a systematic review and meta-analysis

AIMS: To systematically review the diagnostic value of the central vein sign (CVS) in multiple sclerosis (MS) and to meta-analyse the proportion of positive lesions for CVS needed to distinguish MS from non-MS mimics. MATERIALS AND METHODS: A literature review was performed and a proportion meta-analysis was performed to examine the proportion of the CVS in MS lesions. Studies reporting a threshold of the CVS containing lesions with 100% diagnostic accuracy were included in the meta-analysis. This was compared to MS mimics in order to establish the discriminative value of the CVS. RESULTS: The CVS was found to be viable at lower field strengths (3 T and 1.5 T) and automated analysis is currently less accurate than manual counting. Five studies were included for the proportional meta-analysis. From the analysis, a proportion of 45% of lesions having the CVS was suggested given that the findings that the weighted proportion was 46.4% (95% confidence interval [CI]: of 40.3%–52.6%) with low heterogeneity (I2 = 0.0%; p=0.5). CONCLUSION: Although the CVS is a clinically relevant and viable sign, further work is needed to integrate this into the existing diagnostic criteria. As manual determination is a time-consuming process, the development of automated methods will be beneficial. With improvements in computational imaging techniques, the CVS will have an important role in the diagnosis and differentiation of MS

Treatment of metastatic sialoblastoma with chemotherapy and surgery

© 2006 Wiley-Liss, Inc.Tumors of the salivary gland are very uncommon in children. Sialoblastoma is a rare, aggressive, blastomatous, and potentially malignant congenital tumor. Distant metastases are rare. We present a case of sialoblastoma with lung metastases that developed in a 4-year-old girl adjacent to a congenital nevus in the left cheek. The tumor was inoperable at diagnosis but the largest of the pulmonary metastases was removed surgically. The patient responded well to chemotherapy and underwent surgical excision of the primary tumor, followed by three more courses of chemotherapy.Julius X. Scott, Suren Krishnan, Anthony J. Bourne, Michael P. Williams, Marc Agzarian and Tamas Reves

A novel mitochondrial DNA deletion producing progressive external ophthalmoplegia associated with multiple sclerosis

We report a previously undescribed 7676 base pair mitochondrial (mt)DNA deletion involving genes of complex I, complex IV subunits 2 and 3 (cytochrome oxidase [Cox] II, III), adenosine triphosphatase 8 and 6, cytochrome b and 8 transfer (t)RNA genes producing myopathy and progressive external ophthalmoplegia (PEO) in a 44-year-old right-handed Caucasian man with features of multiple sclerosis (MS). We performed complete mtDNA sequencing and deletion analysis, spectrophotometric analysis of muscle and platelet respiratory chain activity, measurement of platelet mitochondrial membrane potential with the potentiometric dye JC-1 and magnetic resonance spectroscopy (MRS) and MRI studies of normal-appearing and lesional cerebral tissue. The deletion resulted in significant respiratory chain deficiency in muscle and blood and abnormalities of the platelet mitochondrial membrane potential. However, cerebrospinal fluid analysis, magnetic resonance spectroscopy and MRI features suggested inflammatory central nervous system demyelination rather than a primary respiratory chain disorder. We conclude that this novel mtDNA deletion causing myopathy and PEO is associated with severe muscle and platelet cellular energetic abnormalities. Furthermore, clinical and paraclinical features of multiple sclerosis were found. The potential pathomechanistic interaction between mtDNA variation and multiple sclerosis is reviewed.M. Slee, J. Finkemeyer, M. Krupa, R. Raghupathi, J. Gardner, P. Blumbergs, M. Agzarian, D. Thyagaraja