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Multimodal data fusion for cancer biomarker discovery with deep learning
Technological advances have made it possible to study a patient from multiple angles with
high-dimensional, high-throughput multiscale biomedical data. In oncology, massive …
high-dimensional, high-throughput multiscale biomedical data. In oncology, massive …
The biological meaning of radiomic features
Radiomic analysis offers a powerful tool for the extraction of clinically relevant information
from radiologic imaging. Radiomics can be used to predict patient outcome through …
from radiologic imaging. Radiomics can be used to predict patient outcome through …
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 …
The applications of radiomics in precision diagnosis and treatment of oncology: opportunities and challenges
Medical imaging can assess the tumor and its environment in their entirety, which makes it
suitable for monitoring the temporal and spatial characteristics of the tumor. Progress in …
suitable for monitoring the temporal and spatial characteristics of the tumor. Progress in …
Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge
Enhancing NSCLC recurrence prediction with PET/CT habitat imaging, ctDNA, and integrative radiogenomics-blood insights
While we recognize the prognostic importance of clinicopathological measures and
circulating tumor DNA (ctDNA), the independent contribution of quantitative image markers …
circulating tumor DNA (ctDNA), the independent contribution of quantitative image markers …