Biomarker discovery and development are popular for detecting the subtle diseases.
However, biomarkers are needed to be validated and approved, and even fewer are ever …

Purpose Noticing the fast growing translation of artificial intelligence (AI) technologies to
medical image analysis this paper emphasizes the future role of the medical physicist in this …

Background The purpose of this study was to analyze the potential of radiomics for disease
stratification beyond key molecular, clinical, and standard imaging features in patients with …

Background Grading of gliomas is critical information related to prognosis and survival. We
aimed to apply a radiomics approach using various machine learning classifiers to …

High-grade gliomas are the most aggressive malignant brain tumors. Accurate pre-operative
prognosis for this cohort can lead to better treatment planning. Conventional survival …

Brain metastasis (BM) is one of the main complications of many cancers, and the most
frequent malignancy of the central nervous system. Imaging studies of BMs are routinely …

Objectives To build a reliable radiomics model from multiregional and multiparametric
magnetic resonance imaging (MRI) for pretreatment prediction of O 6-methylguanine-DNA …

BACKGROUND Although survival statistics in patients with glioblastoma multiforme (GBM)
are well-defined at the group level, predicting individual patient survival remains challenging …

Purpose To characterize intratumoral spatial heterogeneity at perfusion magnetic resonance
(MR) imaging and investigate intratumoral heterogeneity as a predictor of recurrence-free …

Purpose To predict pathological response of breast cancer to neoadjuvant chemotherapy
(NAC) based on quantitative, multiregion analysis of dynamic contrast enhancement …