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

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

Aperture size selection for improved brain tumor detection and quantification in multi-pinhole \ub9\ub2\ub3I-CLINDE SPECT imaging

Abstract: A next-generation multi-pinhole system dedicated to brain SPECT imaging is being constructed by our research team, which we call AdaptiSPECT-C. The system prototype used herein consists of 25 square detector modules and a total of 100 apertures grouped by 4 per module. The system is specifically designed for multi-purpose brain imaging and capable of adapting in real-time each aperture size and whether it is open or shuttered closed. The use of such system would provide optimum high-performance patient-personalized imaging for a wide range of brain imaging tasks. In this work we investigated the effect of pinhole diameter variation on spherical tumor quantification for the promising brain tumor imaging agent 123 I-CLINDE. To assess the quality of the images reconstructed for the different aperture sizes, we used a customized multiple-sphere tumor phantom derived from the XCAT software with a tumor size of 1 cm in diameter. Our results suggest through quantification and visual inspection that an aperture diameter in the range of 2 to 5 mm in diameter for the adaptive AdaptiSPECT-C system is likely the most suited for high performance brain tumor 123I-CLINDE imaging. In addition, our study concludes that a 4 mm pinhole diameter given its excellent spatial-resolution-to-sensitivity trade-off is promising for scout acquisition in localizing target tumor regions within the brain. We have initiated a task-based performance on the tumor detection and localization accuracy for a range of simulated tumor sizes using the channelized non-pre-whitening (CNPW) matched-filter scanning-observer

AAV gene therapy for Tay-Sachs disease

Tay-Sachs disease (TSD) is an inherited neurological disorder caused by deficiency of hexosaminidase A (HexA). Here, we describe an adeno-associated virus (AAV) gene therapy expanded-access trial in two patients with infantile TSD (IND 18225) with safety as the primary endpoint and no secondary endpoints. Patient TSD-001 was treated at 30 months with an equimolar mix of AAVrh8-HEXA and AAVrh8-HEXB administered intrathecally (i.t.), with 75% of the total dose (1 x 10(14) vector genomes (vg)) in the cisterna magna and 25% at the thoracolumbar junction. Patient TSD-002 was treated at 7 months by combined bilateral thalamic (1.5 x 10(12) vg per thalamus) and i.t. infusion (3.9 x 10(13) vg). Both patients were immunosuppressed. Injection procedures were well tolerated, with no vector-related adverse events (AEs) to date. Cerebrospinal fluid (CSF) HexA activity increased from baseline and remained stable in both patients. TSD-002 showed disease stabilization by 3 months after injection with ongoing myelination, a temporary deviation from the natural history of infantile TSD, but disease progression was evident at 6 months after treatment. TSD-001 remains seizure-free at 5 years of age on the same anticonvulsant therapy as before therapy. TSD-002 developed anticonvulsant-responsive seizures at 2 years of age. This study provides early safety and proof-of-concept data in humans for treatment of patients with TSD by AAV gene therapy