Continuous learning AI in radiology: implementation principles and early applications
Artificial intelligence (AI) is becoming increasingly present in radiology and health care. This
expansion is driven by the principal AI strengths: automation, accuracy, and objectivity …
expansion is driven by the principal AI strengths: automation, accuracy, and objectivity …
Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features
Gliomas belong to a group of central nervous system tumors, and consist of various sub-
regions. Gold standard labeling of these sub-regions in radiographic imaging is essential for …
regions. Gold standard labeling of these sub-regions in radiographic imaging is essential for …
Integrated biophysical modeling and image analysis: application to neuro-oncology
Central nervous system (CNS) tumors come with vastly heterogeneous histologic,
molecular, and radiographic landscapes, rendering their precise characterization …
molecular, and radiographic landscapes, rendering their precise characterization …
Brain imaging genomics: integrated analysis and machine learning
Brain imaging genomics is an emerging data science field, where integrated analysis of
brain imaging and genomics data, often combined with other biomarker, clinical, and …
brain imaging and genomics data, often combined with other biomarker, clinical, and …
In Vivo Detection of EGFRvIII in Glioblastoma via Perfusion Magnetic Resonance Imaging Signature Consistent with Deep Peritumoral Infiltration: The ϕ-Index
Purpose: The epidermal growth factor receptor variant III (EGFRvIII) mutation has been
considered a driver mutation and therapeutic target in glioblastoma, the most common and …
considered a driver mutation and therapeutic target in glioblastoma, the most common and …
Machine learning for brain imaging genomics methods: a review
In the past decade, multimodal neuroimaging and genomic techniques have been
increasingly developed. As an interdisciplinary topic, brain imaging genomics is devoted to …
increasingly developed. As an interdisciplinary topic, brain imaging genomics is devoted to …
Radiogenomics for precision medicine with a big data analytics perspective
Precision medicine promises better healthcare delivery by improving clinical practice. Using
evidence-based substratification of patients, the objective is to achieve better prognosis …
evidence-based substratification of patients, the objective is to achieve better prognosis …
Overall survival prediction in glioblastoma patients using structural magnetic resonance imaging (MRI): advanced radiomic features may compensate for lack of …
Purpose: Glioblastoma, the most common and aggressive adult brain tumor, is considered
noncurative at diagnosis, with 14 to 16 months median survival following treatment. There is …
noncurative at diagnosis, with 14 to 16 months median survival following treatment. There is …
Precision diagnostics based on machine learning-derived imaging signatures
The complexity of modern multi-parametric MRI has increasingly challenged conventional
interpretations of such images. Machine learning has emerged as a powerful approach to …
interpretations of such images. Machine learning has emerged as a powerful approach to …
Attentive deep canonical correlation analysis for diagnosing alzheimer's disease using multimodal imaging genetics
Integration of imaging genetics data provides unprecedented opportunities for revealing
biological mechanisms underpinning diseases and certain phenotypes. In this paper, a new …
biological mechanisms underpinning diseases and certain phenotypes. In this paper, a new …