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

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

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

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

Complexities And Potential Pitfalls Of Clinical Study Design And Data Analysis In Assisted Reproduction

Purpose of review: The purpose of the current review is to describe the common pitfalls in design and statistical analysis of reproductive medicine studies. It serves to guide both authors and reviewers toward reducing the incidence of spurious statistical results and erroneous conclusions. Recent findings: The large amount of data gathered in IVF cycles leads to problems with multiplicity, multicollinearity, and over fitting of regression models. Furthermore, the use of the word \u27trend\u27 to describe nonsignificant results has increased in recent years. Finally, methods to accurately account for female age in infertility research models are becoming more common and necessary. Summary: The pitfalls of study design and analysis reviewed provide a framework for authors and reviewers to approach clinical research in the field of reproductive medicine. By providing a more rigorous approach to study design and analysis, the literature in reproductive medicine will have more reliable conclusions that can stand the test of time

Low-dose human chorionic gonadotropin may improve in vitro fertilization cycle outcomes in patients with low luteinizing hormone levels after gonadotropin-releasing hormone antagonist administration

Objective: To evaluate the effect of low levels of endogenous luteinizing hormone (LH) and low-dose human chorionic gonadotropin (hCG) supplementation on in vitro fertilization (IVF) cycle outcomes in a gonadotropinreleasing hormone (GnRH) antagonist protocol. Design: Retrospective study. Setting: Military medical center. Patient(s): General in vitro fertilization/embryo transfer (IVF-ET) population. Intervention(s): Addition of low-dose urinary hCG to IVF stimulations using a recombinant follicle-stimulating hormone (FSH) and GnRH antagonist protocol. Main Outcome Measure(s): Implantation and live-birth rates. Result(s): As part of a larger cohort of 239 patients, 42 patients with LH levels ≤0.5 mIU/mL were evaluated. In the larger cohort, there were no differences in implantation and pregnancy rates between the recombinant FSH only (n = 113) and the recombinant FSH with low-dose hCG supplementation (n = 126) groups. In the FSH-only group, patients with LH levels ≤0.5 mIU/mL had decreased implantation rates (19% vs. 42%) and live-birth rates (25% vs. 54%) as compared with patients with LH levels \u3e0.5 mIU/mL. Low LH patients in the recombinant FSH with low-dose urinary hCG group had statistically significantly higher implantation rates (54% vs. 19%) and live-birth rates (64% vs. 25%) as compared with patients with similar low LH levels in the recombinant FSH-only group. Conclusion(s): Endogenous LH levels ≤0.5 mIU/mL after GnRH antagonist treatment are associated with statistically significantly lower implantation and pregnancy rates in recombinant FSH-only cycles. The addition of lowdose urinary hCG results in improved implantation and live-birth rates in patients with low LH levels