Imaging research laboratories are rapidly creating machine learning systems that achieve expert human performance using open-source methods and tools. These artificial intelligence systems are being ...developed to improve medical image reconstruction, noise reduction, quality assurance, triage, segmentation, computer-aided detection, computer-aided classification, and radiogenomics. In August 2018, a meeting was held in Bethesda, Maryland, at the National Institutes of Health to discuss the current state of the art and knowledge gaps and to develop a roadmap for future research initiatives. Key research priorities include: 1, new image reconstruction methods that efficiently produce images suitable for human interpretation from source data; 2, automated image labeling and annotation methods, including information extraction from the imaging report, electronic phenotyping, and prospective structured image reporting; 3, new machine learning methods for clinical imaging data, such as tailored, pretrained model architectures, and federated machine learning methods; 4, machine learning methods that can explain the advice they provide to human users (so-called explainable artificial intelligence); and 5, validated methods for image de-identification and data sharing to facilitate wide availability of clinical imaging data sets. This research roadmap is intended to identify and prioritize these needs for academic research laboratories, funding agencies, professional societies, and industry.
Lower-grade gliomas (WHO grade II/III) have been classified into clinically relevant molecular subtypes based on
and 1p/19q mutation status. The purpose was to investigate whether T2/FLAIR MRI ...features could distinguish between lower-grade glioma molecular subtypes.
MRI scans from the TCGA/TCIA lower grade glioma database (
= 125) were evaluated by two independent neuroradiologists to assess (i) presence/absence of homogenous signal on T2WI; (ii) presence/absence of "T2-FLAIR mismatch" sign; (iii) sharp or indistinct lesion margins; and (iv) presence/absence of peritumoral edema. Metrics with moderate-substantial agreement underwent consensus review and were correlated with glioma molecular subtypes. Somatic mutation, DNA copy number, DNA methylation, gene expression, and protein array data from the TCGA lower-grade glioma database were analyzed for molecular-radiographic associations. A separate institutional cohort (
= 82) was analyzed to validate the T2-FLAIR mismatch sign.
Among TCGA/TCIA cases, interreader agreement was calculated for lesion homogeneity
= 0.234 (0.111-0.358), T2-FLAIR mismatch sign
= 0.728 (0.538-0.918), lesion margins
= 0.292 (0.135-0.449), and peritumoral edema
= 0.173 (0.096-0.250). All 15 cases that were positive for the T2-FLAIR mismatch sign were
-mutant, 1p/19q non-codeleted tumors (
< 0.0001; PPV = 100%, NPV = 54%). Analysis of the validation cohort demonstrated substantial interreader agreement for the T2-FLAIR mismatch sign
= 0.747 (0.536-0.958); all 10 cases positive for the T2-FLAIR mismatch sign were
-mutant, 1p/19q non-codeleted tumors (
< 0.00001; PPV = 100%, NPV = 76%).
Among lower-grade gliomas, T2-FLAIR mismatch sign represents a highly specific imaging biomarker for the
-mutant, 1p/19q non-codeleted molecular subtype.
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Traumatic spinal cord injury (SCI) leads to immediate neuronal and axonal damage at the focal injury site and triggers secondary pathologic series of events resulting in sensorimotor and autonomic ...dysfunction below the level of injury. Although there is no cure for SCI, neuroprotective and regenerative therapies show promising results at the preclinical stage. There is a pressing need to develop non-invasive outcome measures that can indicate whether a candidate therapeutic agent or a cocktail of therapeutic agents are positively altering the underlying disease processes. Recent conventional MRI studies have quantified spinal cord lesion characteristics and elucidated their relationship between severity of injury to clinical impairment and recovery. Next to the quantification of the primary cord damage, quantitative MRI measures of spinal cord (rostrocaudally to the lesion site) and brain integrity have demonstrated progressive and specific neurodegeneration of afferent and efferent neuronal pathways. MRI could therefore play a key role to ultimately uncover the relationship between clinical impairment/recovery and injury-induced neurodegenerative changes in the spinal cord and brain. Moreover, neuroimaging biomarkers hold promises to improve clinical trial design and efficiency through better patient stratification. The purpose of this narrative review is therefore to propose a guideline of clinically available MRI sequences and their derived neuroimaging biomarkers that have the potential to assess tissue damage at the macro- and microstructural level after SCI. In this piece, we make a recommendation for the use of key MRI sequences-both conventional and advanced-for clinical work-up and clinical trials.
The major language pathways such as superior longitudinal fasciculus (SLF) pathways have been outlined by experimental and diffusion tensor imaging (DTI) studies. The SLF I and some of the superior ...parietal lobule connections of the SLF pathways have not been depicted by prior DTI studies due to the lack of imaging sensitivity and adequate spatial resolution. In the current study, the trajectory of the SLF fibers has been delineated on five healthy human subjects using diffusion tensor tractography on a 3.0-T scanner at high spatial resolution. We also demonstrate for the first time the trajectory and connectivity of the SLF fibers in relation to other language pathways as well as the superior parietal lobule connections of the language circuit using high spatial resolution DTI in the healthy adult human brain.
•Radiomics detects imaging features not identifiable by the simple visual analysis.•Radiomics features can be combined with data from other “…-omics” data.•Radiomics creates enhanced data models for ...supporting medical decision.•These algorithms help to assess various aspects of cancer immunotherapy treatment.
In recent years the concept of precision medicine has become a popular topic particularly in medical oncology. Besides the identification of new molecular prognostic and predictive biomarkers and the development of new targeted and immunotherapeutic drugs, imaging has started to play a central role in this new era. Terms such as “radiomics”, “radiogenomics” or “radi…-omics” are becoming increasingly common in the literature and soon they will represent an integral part of clinical practice. The use of artificial intelligence, imaging and “-omics” data can be used to develop models able to predict, for example, the features of the tumor immune microenvironment through imaging, and to monitor the therapeutic response beyond the standard radiological criteria.
The aims of this narrative review are to provide a simplified guide for clinicians to these concepts, and to summarize the existing evidence on radiomics and “radi…-omics” in cancer immunotherapy.
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