Radiomic Texture Analysis Mapping Predicts Areas of True Functional MRI Activity.

Individual analysis of functional Magnetic Resonance Imaging (fMRI) scans requires user-adjustment of the statistical threshold in order to maximize true functional activity and eliminate false positives. In this study, we propose a novel technique that uses radiomic texture analysis (TA) features associated with heterogeneity to predict areas of true functional activity. Scans of 15 right-handed healthy volunteers were analyzed using SPM8. The resulting functional maps were thresholded to optimize visualization of language areas, resulting in 116 regions of interests (ROIs). A board-certified neuroradiologist classified different ROIs into Expected (E) and Non-Expected (NE) based on their anatomical locations. TA was performed using the mean Echo-Planner Imaging (EPI) volume, and 20 rotation-invariant texture features were obtained for each ROI. Using forward stepwise logistic regression, we built a predictive model that discriminated between E and NE areas of functional activity, with a cross-validation AUC and success rate of 79.84% and 80.19% respectively (specificity/sensitivity of 78.34%/82.61%). This study found that radiomic TA of fMRI scans may allow for determination of areas of true functional activity, and thus eliminate clinician bias

Brain extraction on MRI scans in presence of diffuse glioma: Multi-institutional performance evaluation of deep learning methods and robust modality-agnostic training

Brain extraction, or skull-stripping, is an essential pre-processing step in neuro-imaging that has a direct impact on the quality of all subsequent processing and analyses steps. It is also a key requirement in multi-institutional collaborations to comply with privacy-preserving regulations. Existing automated methods, including Deep Learning (DL) based methods that have obtained state-of-the-art results in recent years, have primarily targeted brain extraction without considering pathologically-affected brains. Accordingly, they perform sub-optimally when applied on magnetic resonance imaging (MRI) brain scans with apparent pathologies such as brain tumors. Furthermore, existing methods focus on using only T1-weighted MRI scans, even though multi-parametric MRI (mpMRI) scans are routinely acquired for patients with suspected brain tumors. In this study, we present a comprehensive performance evaluation of recent deep learning architectures for brain extraction, training models on mpMRI scans of pathologically-affected brains, with a particular focus on seeking a practically-applicable, low computational footprint approach, generalizable across multiple institutions, further facilitating collaborations. We identified a large retrospective multi-institutional dataset of n=3340 mpMRI brain tumor scans, with manually-inspected and approved gold-standard segmentations, acquired during standard clinical practice under varying acquisition protocols, both from private institutional data and public (TCIA) collections. To facilitate optimal utilization of rich mpMRI data, we further introduce and evaluate a novel ‘‘modality-agnostic training’’ technique that can be applied using any available modality, without need for model retraining. Our results indicate that the modality-agnostic approach1 obtains accurate results, providing a generic and practical tool for brain extraction on scans with brain tumors

The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)

Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and without contrast enhancement, T2-weighted images, and FLAIR images. However, some sequences are often missing in clinical practice due to time constraints or image artifacts, such as patient motion. Consequently, the ability to substitute missing modalities and gain segmentation performance is highly desirable and necessary for the broader adoption of these algorithms in the clinical routine. In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023. The primary objective of this challenge is to evaluate image synthesis methods that can realistically generate missing MRI modalities when multiple available images are provided. The ultimate aim is to facilitate automated brain tumor segmentation pipelines. The image dataset used in the benchmark is diverse and multi-modal, created through collaboration with various hospitals and research institutions.Comment: Technical report of BraSy

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

NEUROPATHOLOGIC CORRELATES OF BRAIN MACROSTRUCTURE

Alzheimer’s disease, the most common form of dementia, is a degenerative disorder of the brain that leads to memory loss. Clinical diagnostic techniques in use today rely on mental and behavioral tests and physical examinations and only provide diagnoses of possible or probable Alzheimer’s disease. However, lately it has become clear that clinical-pathological correspondence is not always consistent. A definitive diagnosis of Alzheimer’s disease is only possible via histology, when the density of neurofibrillary tangles and amyloid plaques is measured. Therefore, the development of a reliable neuroimaging technique that allows detection of Alzheimer’s pathology during life is needed. This method would be noninvasive, and could allow the detection of Alzheimer’s disease in the early stages, and could be also used to monitor the progression of the disease through time. The purpose of this work was to investigate the use of magnetic resonance imaging (MRI) as diagnostic tool for Alzheimer’s pathology and other age-related neurodegenerative pathologies that are common in older persons. To uncover the anatomical origins and determine the macrostructural signatures of age-related neuropathologies, it is necessary to link MRI findings with pathologic information on the same individuals. In this work, we focused on imaging cerebral hemispheres ex-vivo, when a complete pathology report was available from a board-certified neuropathologist. The main difference between this work and any other study is the abundance of postmortem imaging data paired with neuropathology data in a relatively large pool of subjects. First, we developed and validated a protocol to perform ex-vivo MR volumetry. By using this protocol we observed the longitudinal behavior of the volume of different brain regions. Furthermore, we tested the hypothesis that volumetric measurements performed ex-vivo are associated with in-vivo measurements. It was shown that: (a) regional brain volumes measured with this approach for ex-vivo MR volumetry remain relatively unchanged for a period of 6 months postmortem, and (b) a linear correspondence was detected between in-vivo and ex-vivo measurements, suggesting that this approach captures information linked to antemortem macrostructural brain characteristics. Using the approach for ex-vivo MR volumetry, we combined ex-vivo MR volumetry with pathology on the same adults. AD pathology was significantly negatively correlated with volumes of cortical gray matter regions, mainly in the temporal, frontal, parietal and cingulate cortices, subcortical gray matter, and whole-hemisphere white matter. A significant negative correlation was shown between hippocampal sclerosis and volumes of the hippocampus, as well as other temporal and frontal gray matter regions. Finally, we performed a morphometric MRI study to investigate associations of brain volumes with pathology using voxel-based analysis. This technique allows the assessment of gray and white matter volumes in subjects with different pathologies compared with controls in an automated fashion, across the whole brain. AD pathology was negatively associated with regions of gray matter and white matter located in temporal and frontal lobes, and orbitofrontal cortex. This work examined the associations of brain volumes with Alzheimer’s pathology and other typed of age-related neurodegenerative pathologies. Combining histological result with MR images requires that the time elapsed between imaging and histology is minimal. Ex-vivo MRI provides images at essentially the same time-point as histological examination of the tissue, and this study is unique in that it involves a large number of cadaveric brain hemispheres. The findings of this ex-vivo study could allow for future standardization of MRI as a biomarker of neurodegenerative diseases, and also allow in identification and classification of subjects in groups for tests of new drugs.Ph.D. in Biomedical Engineering, July 201