Decoding global gene expression programs in liver cancer by noninvasive imaging

By Eran Segal, Claude B. Sirlin, C. Ooi, A. Adler, J. Gollub, Xin Chen, Bryan K Chan, G. Matcuk, Christopher Barry, Howard Y. Chang and 1 more. Nature Biotechnology, 2007

608 citations recorded by the source corpus.

Subfields: Molecular Biology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/f0f5cfda67ed8ccca776ecd8799c0e7375b0bfc6

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In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

A Deep Look Into the Future of Quantitative Imaging in Oncology: A Statement of Working Principles and Proposal for Change. (2018, methodology): “Ongoing efforts at standardization are paramount in the following areas: (1) medical imaging acquisition and reconstruction; (2) workflow of computation of radiomics features proposed by the Image Biomarker Standardization Initiative (19); and (3) statistical analysis.”
Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer (2015, background): “Furthermore, some radio-genomic studies have reported associations between radiomic features and underlying gene expression patterns (2, 9, 11, 24, 25).”
Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures (2017, background): “Radiomics quantifies the phenotype of the underlying biology; however, this link is not straightforward.(94,95) More research is needed into the correlation between the underlying biological processes and the perceived image data sets.”
Radiomics: the bridge between medical imaging and personalized medicine (2017, unknown): “Another investigation compared clinician-defined features extracted from contrast-enhanced CT images in patients with hepatocellular carcinoma with gene-expression patterns using machine learning with a neural network 55 .”
QIN “Radiomics: The Process and the Challenges” (2012, methodology): “Following a complete manual extraction of numerous (N100) image features, a subset of 14 features was able to predict 80% of the gene expression pattern in hepatocellular carcinoma using computed tomographic (CT) images [12].”
Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology (2017, background): “A landmark study in liver cancer demonstrated that the combination of 28 traits could reconstruct as much as 78% of transcriptome variation, and were speciﬁcally associated with genes involving cell proliferation [77].”
Intratumor Heterogeneity Characterized by Textural Features on Baseline 18F-FDG PET Images Predicts Response to Concomitant Radiochemotherapy in Esophageal Cancer (2011, background): “(22) showed that contrast-enhanced CT image characteristics (such as texture heterogeneity score or estimated percentage of necrosis) correlate with most of the liver global gene expression profiles, revealing cell proliferation, liver synthetic function, and patient prognosis.”
Machine Learning methods for Quantitative Radiomic Biomarkers (2015, background): “Moreover, some studies have reported the association between radiomic features and the underlying gene expression patterns 8,14,15 .”
Radiomics: Extracting more information from medical images using advanced feature analysis (2012, result): “Similar findings have been found in hepatocellular carcinomas by Segal et al.21, showing that the combination of only 28 imaging traits was sufficient to reconstruct the variation of 116 gene expression modules.”
Robust Radiomics Feature Quantification Using Semiautomatic Volumetric Segmentation (2014, background): “For example, quantitative imaging features extracted from CT images have been shown to predict 78% of the gene expression variability in hepatocellular carcinoma [11].”