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

Age and education adjusted normative data and discriminative validity for Reys Auditory Verbal Learning Test in the elderly Greek population

Reys Auditory Verbal Learning Test (RAVLT) is a widely used neuropsychological test to assess episodic memory. In the present study we sought to establish normative and discriminative validity data for the RAVLT in the elderly population using previously adapted learning lists for the Greek adult population. We administered the test to 258 cognitively healthy elderly participants, aged 60-89 years, and two patient groups (192 with amnestic mild cognitive impairment, aMCI, and 65 with Alzheimers disease, AD). From the statistical analyses, we found that age and education contributed significantly to most trials of the RAVLT, whereas the influence of gender was not significant. Younger elderly participants with higher education outperformed the older elderly with lower education levels. Moreover, both clinical groups performed significantly worse on most RAVLT trials and composite measures than matched cognitively healthy controls. Furthermore, the AD group performed more poorly than the aMCI group on most RAVLT variables. Receiver operating characteristic (ROC) analysis was used to examine the utility of the RAVLT trials to discriminate cognitively healthy controls from aMCI and AD patients. Area under the curve (AUC), an index of effect size, showed that most of the RAVLT measures (individual and composite) included in this study adequately differentiated between the performance of healthy elders and aMCI/AD patients. We also provide cutoff scores in discriminating cognitively healthy controls from aMCI and AD patients, based on the sensitivity and specificity of the prescribed scores. Moreover, we present age-and education-specific normative data for individual and composite scores for the Greek adapted RAVLT in elderly subjects aged between 60 and 89 years for use in clinical and research settings. © 2015 Taylor and Francis