Texture-based convolutional neural networks (CNNs) have shown great promise in
predicting various types of cancer, including lower grade glioma (LGG) through radiomics …

Background The grading and pathologic biomarkers of glioma has important guiding
significance for the individual treatment. In clinical, it is often necessary to obtain tumor …

Radiomics-based researches have shown predictive abilities with machine-learning
approaches. However, it is still unknown whether different radiomics strategies affect the …

Objectives To develop and validate an efficient and automatically computational approach
for stratifying glioma grades and predicting survival of lower-grade glioma (LGG) patients …

Background A deep learning (DL) model based on representative biopsy tissues can predict
the recurrence and overall survival of patients with glioma, leading to optimized …

Lower-grade gliomas (LGG) can eventually progress to glioblastoma (GBM) and death. In
the context of the transfer learning approach, we aimed to train and test an MRI-based …

This paper presents a novel set of image texture features generalizing standard grey-level
cooccurrence matrices (GLCM) to multimodal image data through joint intensity matrices …

Traditional radiomics models mainly rely on explicitly-designed handcrafted features from
medical images. This paper aimed to investigate if deep features extracted via transfer …

Background To accurately predict the prognosis of glioma patients. Methods A total of 541
samples from the TCGA cohort, 181 observations from the CGGA database and 91 samples …

Background The histological sub-classes of brain tumors and the Ki-67 labeling index (LI) of
tumor cells are major factors in the diagnosis, prognosis, and treatment management of …