OBJECTIVES: To develop and validate a deep learning imaging signature (DLIS) for risk stratification in patients with multiforme (GBM), and to investigate the biological pathways and genetic alterations underlying the DLIS.
BACKGROUND: Recent developments to segment and characterize the regions of interest (ROI) within medical images have led to promising shape analysis studies. However, the procedures to analyze the ROI are arbitrary and vary by study. A tool to transl...
INTRODUCTION: Glioblastomas (GBMs) are highly aggressive tumors. A common clinical challenge after standard of care treatment is differentiating tumor progression from treatment-related changes, also known as pseudoprogression (PsP). Usually, PsP res...
Bioluminescence imaging (BLI) is a valuable tool for non-invasive monitoring of glioblastoma multiforme (GBM) tumor-bearing small animals without incurring x-ray radiation burden. However, the use of this imaging modality is limited due to photon sca...
OBJECTIVE: Hypoxia presents a salient feature investigated in most solid tumors that holds key roles in cancer progression, including glioblastoma multiforme (GBM). Here, we aimed to construct a hypoxia-derived gene signature for identifying the high...
BACKGROUND: Glioblastoma (GBM) can be divided into subtypes according to their genomic features, including Proneural (PN), Neural (NE), Classical (CL) and Mesenchymal (ME). However, it is a difficult task to unify various genomic expression profiles ...
Cancer subtype classification helps us to understand the pathogenesis of cancer and develop new cancer drugs, treatment from which patients would benefit most. Most previous studies detect cancer subtypes by extracting features from individual sample...
Translating in vitro results from experiments with cancer cell lines to clinical applications requires the selection of appropriate cell line models. Here we present MFmap (model fidelity map), a machine learning model to simultaneously predict the c...
BACKGROUND: Glioblastoma is the commonest malignant brain tumour. Sarcopenia is associated with worse cancer survival, but manually quantifying muscle on imaging is time-consuming. We present a deep learning-based system for quantification of tempora...
International journal of molecular sciences
Oct 26, 2021
The theoretical prediction of drug-decorated nanoparticles (DDNPs) has become a very important task in medical applications. For the current paper, Perturbation Theory Machine Learning (PTML) models were built to predict the probability of different ...
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