Privacy preservation for federated learning in health care
Artificial intelligence (AI) shows potential to improve health care by leveraging data to build
models that can inform clinical workflows. However, access to large quantities of diverse …
models that can inform clinical workflows. However, access to large quantities of diverse …
Artificial intelligence in CT and MR imaging for oncological applications
Simple Summary The two most common cross-sectional imaging modalities, computed
tomography (CT) and magnetic resonance imaging (MRI), have shown enormous utility in …
tomography (CT) and magnetic resonance imaging (MRI), have shown enormous utility in …
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 …
generalizability is concerning. This is currently addressed by sharing multi-site data, but …
Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data
Several studies underscore the potential of deep learning in identifying complex patterns,
leading to diagnostic and prognostic biomarkers. Identifying sufficiently large and diverse …
leading to diagnostic and prognostic biomarkers. Identifying sufficiently large and diverse …
The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics
Glioblastoma is the most common aggressive adult brain tumor. Numerous studies have
reported results from either private institutional data or publicly available datasets. However …
reported results from either private institutional data or publicly available datasets. However …
Clinical measures, radiomics, and genomics offer synergistic value in AI-based prediction of overall survival in patients with glioblastoma
Multi-omic data, ie, clinical measures, radiomic, and genetic data, capture multi-faceted
tumor characteristics, contributing to a comprehensive patient risk assessment. Here, we …
tumor characteristics, contributing to a comprehensive patient risk assessment. Here, we …
Applications of radiomics and radiogenomics in high-grade gliomas in the era of precision medicine
Simple Summary Radiomics and radiogenomics offer new insight into high-grade glioma
biology, as well as into glioma behavior in response to standard therapies. In this article, we …
biology, as well as into glioma behavior in response to standard therapies. In this article, we …
The federated tumor segmentation (FeTS) tool: an open-source solution to further solid tumor research
Objective. De-centralized data analysis becomes an increasingly preferred option in the
healthcare domain, as it alleviates the need for sharing primary patient data across …
healthcare domain, as it alleviates the need for sharing primary patient data across …
Association of partial T2-FLAIR mismatch sign and isocitrate dehydrogenase mutation in WHO grade 4 gliomas: results from the ReSPOND consortium
Abstract Purpose While the T2-FLAIR mismatch sign is highly specific for isocitrate
dehydrogenase (IDH)-mutant, 1p/19q-noncodeleted astrocytomas among lower-grade …
dehydrogenase (IDH)-mutant, 1p/19q-noncodeleted astrocytomas among lower-grade …
Analyzing magnetic resonance imaging data from glioma patients using deep learning
The quantitative analysis of images acquired in the diagnosis and treatment of patients with
brain tumors has seen a significant rise in the clinical use of computational tools. The …
brain tumors has seen a significant rise in the clinical use of computational tools. The …