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

Dual Wavelength Diffuse Correlation Spectroscopy: A Novel Tool for Identifying Determinants of Oxygen Consumption

Near-infrared diffuse correlation spectroscopy (DCS) is a novel method for measuring microvascular skeletal muscle blood flow. Our lab recently found excellent agreement between single wavelength DCS and Doppler ultrasound of the brachial artery during rhythmic handgrip exercise. PURPOSE: Here, we report new data utilizing a dual wavelength DCS system (785nm and 852nm), which extends our prior work by combining novel microvascular perfusion assessment with real-time quantification of tissue oxygenation. METHODS: We enrolled eight individuals (male/female: 3/5, mean: age 48±22 (range: 22-76 years), height 170±8cm, and weight 75±12kg). Subjects were instrumented with the DCS probe placed over the belly of the flexor digitorum profundus. Duplex ultrasound of the brachial artery was performed concurrently to provide and additional measure of skeletal muscle blood flow. Each subject performed two bouts of rhythmic hand grip exercise at 20% of their maximum voluntary contraction (MVC). Resting baseline data were acquired prior to each bout of exercise, and each period of data collection were separated by a minimum of 10 minutes of rest. The data derived from both rest and exercise periods were averaged. RESULTS: As reported previously using our single wavelength DCS device, blood flow index (BFI, the primary output from DCS) increased significantly (119+37%) with exercise. We also observed a 1.9+1.1% change in oxyhemoglobin and 21.8+10.0% change in deoxyhemoglobin resulting in a -5.9±2.6% change in tissue saturation with exercise. Using these data, relative muscle oxygen consumption (rmVO2) was calculated and found to increase by 160.2+55.4%. The novelty of this new approach is best illustrated by a case-comparison between two subjects, who performed nearly equivalent absolute (11 vs 10 kg) and relative work (20%), and yet achieved strikingly different levels of oxygen utilization during exercise (ΔrmO2 = 307% vs. 214%, Case A vs. Case B respectively). This disparity appears to be attributable to muscle oxygen extraction as both brachial artery blood flow and microvascular perfusion (by DCS) were similar in both subjects. By contrast, Case A exhibited a much greater change in StO2 (-17.8%) compared to Case B, whose StO2 more closely mirrored the group average (-6.8%). To aid in the interpretation of these results, we evaluated skeletal muscle oxidative capacity in both subjects using an established NIRS-based cuff occlusion protocol (Rosenberry et al. 2018. JoVE). Remarkably, these additional data corroborated our hypothesis; Case A exhibited a much faster muscle oxygen consumption recovery time (34 seconds) whereas Case B’s recovery time was 93 seconds. CONCLUSION: Taken together, these data establish strong proof-of-concept that dual wavelength DCS can provide valuable mechanistic insight into the determinants of oxygen consumption

A Fully Automated Deep Learning Network for Brain Tumor Segmentation

We developed a fully automated method for brain tumor segmentation using deep learning; 285 brain tumor cases with multiparametric magnetic resonance images from the BraTS2018 data set were used. We designed 3 separate 3D-Dense-UNets to simplify the complex multiclass segmentation problem into individual binary-segmentation problems for each subcomponent. We implemented a 3-fold cross-validation to generalize the network\u27s performance. The mean cross-validation Dice-scores for whole tumor (WT), tumor core (TC), and enhancing tumor (ET) segmentations were 0.92, 0.84, and 0.80, respectively. We then retrained the individual binary-segmentation networks using 265 of the 285 cases, with 20 cases held-out for testing. We also tested the network on 46 cases from the BraTS2017 validation data set, 66 cases from the BraTS2018 validation data set, and 52 cases from an independent clinical data set. The average Dice-scores for WT, TC, and ET were 0.90, 0.84, and 0.80, respectively, on the 20 held-out testing cases. The average Dice-scores for WT, TC, and ET on the BraTS2017 validation data set, the BraTS2018 validation data set, and the clinical data set were as follows: 0.90, 0.80, and 0.78; 0.90, 0.82, and 0.80; and 0.85, 0.80, and 0.77, respectively. A fully automated deep learning method was developed to segment brain tumors into their subcomponents, which achieved high prediction accuracy on the BraTS data set and on the independent clinical data set. This method is promising for implementation into a clinical workflow

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