Predicting Hypoxia in Brain Tumors from Multiparametric MRI

This research paper presents a novel approach to the prediction of hypoxia in brain tumors, using multi-parametric Magnetic Resonance Imaging (MRI). Hypoxia, a condition characterized by low oxygen levels, is a common feature of malignant brain tumors associated with poor prognosis. Fluoromisonidazole Positron Emission Tomography (FMISO PET) is a well-established method for detecting hypoxia in vivo, but it is expensive and not widely available. Our study proposes the use of MRI, a more accessible and cost-effective imaging modality, to predict FMISO PET signals. We investigate deep learning models (DL) trained on the ACRIN 6684 dataset, a resource that contains paired MRI and FMISO PET images from patients with brain tumors. Our trained models effectively learn the complex relationships between the MRI features and the corresponding FMISO PET signals, thereby enabling the prediction of hypoxia from MRI scans alone. The results show a strong correlation between the predicted and actual FMISO PET signals, with an overall PSNR score above 29.6 and a SSIM score greater than 0.94, confirming MRI as a promising option for hypoxia prediction in brain tumors. This approach could significantly improve the accessibility of hypoxia detection in clinical settings, with the potential for more timely and targeted treatments.

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally sharing ample, and importantly diverse, data from multiple sites. However, such centralization is challenging to scale (or even not feasible) due to various limitations. Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here we present findings from the largest FL study to-date, involving data from 71 healthcare institutions across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, utilizing the largest dataset of such patients ever used in the literature (25,256 MRI scans from 6,314 patients). We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent. We anticipate our study to: 1) enable more studies in healthcare informed by large and diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further quantitative analyses for glioblastoma via performance optimization of our consensus model for eventual public release, and 3) demonstrate the effectiveness of FL at such scale and task complexity as a paradigm shift for multi-site collaborations, alleviating the need for data sharing.