Radiomics and Machine Learning for Prediction of Relapsed and Refractory Primary Central Nervous System Lymphoma

Grading brain astrocytoma using convolutional neural network: contrast-enhanced T1 and susceptibility-weighted imaging

The application value of Radiomics combined with clinical features and genomics in predicting glioma survival

Prediction of glioma genotypes by APTw-derived radiomic features combined with deep learning networks

The Value of Multiparametric MRI-based Radiomics Features in Distinguishing Primary Central Nervous System Lymphoma from High-grade Glioma

Evaluation of relaxometry in differentiating recurrence and necrosis of high-grade glioma after radiotherapy using synthetic MR

Nosological images of brain tumor MV-MRS 3T data based on classifiers trained with SV-MRS 1.5T data, a proof-of-concept

Synthesize conventional MRI Sequences by Generative Adversarial Networks with only T2 for Use in a Multisequence gliomas classification Model

Boosting The Deep Learning Performance in Predicting IDH Mutation in Gliomas Using Multiparametric MRI Including SWI, FLAIR and CE-T1WI

Automatic Segmentation of Vasculature in DCE-MRI of Brain Tumors and its Influence in Grading

Histogram model of MRI arteriolar blood volume in detecting subclinical recurrence of high-grade glioma after chemoradiotherapy

Automated Fiber Quantification Predicts Motor Weakness in Patients Following Resection of Primary and Metastatic Brain Lesions

Comparative Evaluation of Metabolite Composition in Brain Tumor Epilepsy Patients

Autopsy-based radio-pathomic maps of tumor probability delineate tumor presence within radiological segmentations

Quantitative MRI for radiation oncology patients with head and neck squamous cell carcinoma