Radiomics and radiogenomics in gliomas: a contemporary update
The natural history and treatment landscape of primary brain tumours are complicated by the
varied tumour behaviour of primary or secondary gliomas (high-grade transformation of low …
varied tumour behaviour of primary or secondary gliomas (high-grade transformation of low …
Characterization of PET/CT images using texture analysis: the past, the present… any future?
After seminal papers over the period 2009–2011, the use of texture analysis of PET/CT
images for quantification of intratumour uptake heterogeneity has received increasing …
images for quantification of intratumour uptake heterogeneity has received increasing …
Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge
Gliomas are the most common primary brain malignancies, with different degrees of
aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, ie …
aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, ie …
Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features
Gliomas belong to a group of central nervous system tumors, and consist of various sub-
regions. Gold standard labeling of these sub-regions in radiographic imaging is essential for …
regions. Gold standard labeling of these sub-regions in radiographic imaging is essential for …
[HTML][HTML] Machine learning methods for quantitative radiomic biomarkers
Radiomics extracts and mines large number of medical imaging features quantifying tumor
phenotypic characteristics. Highly accurate and reliable machine-learning approaches can …
phenotypic characteristics. Highly accurate and reliable machine-learning approaches can …
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 …
Defining the biological basis of radiomic phenotypes in lung cancer
P Grossmann, O Stringfield, N El-Hachem, MM Bui… - elife, 2017 - elifesciences.org
Medical imaging can visualize characteristics of human cancer noninvasively. Radiomics is
an emerging field that translates these medical images into quantitative data to enable …
an emerging field that translates these medical images into quantitative data to enable …
MRI features predict survival and molecular markers in diffuse lower-grade gliomas
Background. Previous studies have shown that MR imaging features can be used to predict
survival and molecular profile of glioblastoma. However, no study of a similar type has been …
survival and molecular profile of glioblastoma. However, no study of a similar type has been …
Radiomic feature clusters and prognostic signatures specific for lung and head & neck cancer
Radiomics provides a comprehensive quantification of tumor phenotypes by extracting and
mining large number of quantitative image features. To reduce the redundancy and compare …
mining large number of quantitative image features. To reduce the redundancy and compare …
Radiomic machine-learning classifiers for prognostic biomarkers of head and neck cancer
Introduction “Radiomics” extracts and mines a large number of medical imaging features in a
non-invasive and cost-effective way. The underlying assumption of radiomics is that these …
non-invasive and cost-effective way. The underlying assumption of radiomics is that these …