@article {41, title = {Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach.}, journal = {Nat Commun}, volume = {5}, year = {2014}, month = {2014 Jun 03}, pages = {4006}, abstract = {

Human cancers exhibit strong phenotypic differences that can be visualized noninvasively by medical imaging. Radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features. Here we present a radiomic analysis of 440 features quantifying tumour image intensity, shape and texture, which are extracted from computed tomography data of 1,019 patients with lung or head-and-neck cancer. We find that a large number of radiomic features have prognostic power in independent data sets of lung and head-and-neck cancer patients, many of which were not identified as significant before. Radiogenomics analysis reveals that a prognostic radiomic signature, capturing intratumour heterogeneity, is associated with underlying gene-expression patterns. These data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer. This may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost.

}, keywords = {Adenocarcinoma, Carcinoma, Non-Small-Cell Lung, Carcinoma, Squamous Cell, Female, Head and Neck Neoplasms, Humans, Lung Neoplasms, Male, Multimodal Imaging, Phenotype, Positron-Emission Tomography, Prognosis, Tomography, X-Ray Computed, Tumor Burden}, issn = {2041-1723}, doi = {10.1038/ncomms5006}, author = {Aerts, Hugo J W L and Velazquez, Emmanuel Rios and Leijenaar, Ralph T H and Parmar, Chintan and Grossmann, Patrick and Carvalho, Sara and Bussink, Johan and Monshouwer, Ren{\'e} and Haibe-Kains, Benjamin and Rietveld, Derek and Hoebers, Frank and Rietbergen, Michelle M and Leemans, C Ren{\'e} and Dekker, Andre and Quackenbush, John and Gillies, Robert J and Lambin, Philippe} } @article {47, title = {Prognostic value of metabolic metrics extracted from baseline positron emission tomography images in non-small cell lung cancer.}, journal = {Acta Oncol}, volume = {52}, year = {2013}, month = {2013 Oct}, pages = {1398-404}, abstract = {

BACKGROUND: Maximum, mean and peak SUV of primary tumor at baseline FDG-PET scans, have often been found predictive for overall survival in non-small cell lung cancer (NSCLC) patients. In this study we further investigated the prognostic power of advanced metabolic metrics derived from intensity volume histograms (IVH) extracted from PET imaging.

METHODS: A cohort of 220 NSCLC patients (mean age, 66.6 years; 149 men, 71 women), stages I-IIIB, treated with radiotherapy with curative intent were included (NCT00522639). Each patient underwent standardized pre-treatment CT-PET imaging. Primary GTV was delineated by an experienced radiation oncologist on CT-PET images. Common PET descriptors such as maximum, mean and peak SUV, and metabolic tumor volume (MTV) were quantified. Advanced descriptors of metabolic activity were quantified by IVH. These comprised five groups of features: absolute and relative volume above relative intensity threshold (AVRI and RVRI), absolute and relative volume above absolute intensity threshold (AVAI and RVAI), and absolute intensity above relative volume threshold (AIRV). MTV was derived from the IVH curves for volumes with SUV above 2.5, 3 and 4, and of 40\% and 50\% maximum SUV. Univariable analysis using Cox Proportional Hazard Regression was performed for overall survival assessment.

RESULTS: Relative volume above higher SUV (80\%) was an independent predictor of OS (p = 0.05). None of the possible surrogates for MTV based on volumes above SUV of 3, 40\% and 50\% of maximum SUV showed significant associations with OS [p (AVAI3) = 0.10, p (AVAI4) = 0.22, p (AVRI40\%) = 0.15, p (AVRI50\%) = 0.17]. Maximum and peak SUV (r = 0.99) revealed no prognostic value for OS [p (maximum SUV) = 0.20, p (peak SUV) = 0.22].

CONCLUSIONS: New methods using more advanced imaging features extracted from PET were analyzed. Best prognostic value for OS of NSCLC patients was found for relative portions of the tumor above higher uptakes (80\% SUV).

}, keywords = {Aged, Carcinoma, Non-Small-Cell Lung, Female, Fluorodeoxyglucose F18, Humans, Lung Neoplasms, Male, Neoplasm Staging, Positron-Emission Tomography, Prognosis, Radiopharmaceuticals, Radiotherapy Planning, Computer-Assisted, Radiotherapy, Image-Guided, Tumor Burden}, issn = {1651-226X}, doi = {10.3109/0284186X.2013.812795}, author = {Carvalho, Sara and Leijenaar, Ralph T H and Velazquez, Emmanuel Rios and Oberije, Cary and Parmar, Chintan and van Elmpt, Wouter and Reymen, Bart and Troost, Esther G C and Oellers, Michel and Dekker, Andre and Gillies, Robert and Aerts, Hugo J W L and Lambin, Philippe} } Error | CancerData.org

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