@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 {39, title = {International data-sharing for radiotherapy research: an open-source based infrastructure for multicentric clinical data mining.}, journal = {Radiother Oncol}, volume = {110}, year = {2014}, month = {2014 Feb}, pages = {370-374}, abstract = {

Extensive, multifactorial data sharing is a crucial prerequisite for current and future (radiotherapy) research. However, the cost, time and effort to achieve this are often a roadblock. We present an open-source based data-sharing infrastructure between two radiotherapy departments, allowing seamless exchange of de-identified, automatically translated clinical and biomedical treatment data.

}, keywords = {Clinical Trials as Topic, Data Mining, Humans, Information Dissemination, Neoplasms, Radiotherapy}, issn = {1879-0887}, doi = {10.1016/j.radonc.2013.11.001}, author = {Roelofs, Erik and Dekker, Andre and Meldolesi, Elisa and van Stiphout, Ruud G P M and Valentini, Vincenzo 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} } @article {51, title = {A semiautomatic CT-based ensemble segmentation of lung tumors: comparison with oncologists{\textquoteright} delineations and with the surgical specimen.}, journal = {Radiother Oncol}, volume = {105}, year = {2012}, month = {2012 Nov}, pages = {167-73}, abstract = {

PURPOSE: To assess the clinical relevance of a semiautomatic CT-based ensemble segmentation method, by comparing it to pathology and to CT/PET manual delineations by five independent radiation oncologists in non-small cell lung cancer (NSCLC).

MATERIALS AND METHODS: For 20 NSCLC patients (stages Ib-IIIb) the primary tumor was delineated manually on CT/PET scans by five independent radiation oncologists and segmented using a CT based semi-automatic tool. Tumor volume and overlap fractions between manual and semiautomatic-segmented volumes were compared. All measurements were correlated with the maximal diameter on macroscopic examination of the surgical specimen. Imaging data are available on www.cancerdata.org.

RESULTS: High overlap fractions were observed between the semi-automatically segmented volumes and the intersection (92.5{\textpm}9.0, mean{\textpm}SD) and union (94.2{\textpm}6.8) of the manual delineations. No statistically significant differences in tumor volume were observed between the semiautomatic segmentation (71.4{\textpm}83.2 cm(3), mean{\textpm}SD) and manual delineations (81.9{\textpm}94.1 cm(3); p=0.57). The maximal tumor diameter of the semiautomatic-segmented tumor correlated strongly with the macroscopic diameter of the primary tumor (r=0.96).

CONCLUSIONS: Semiautomatic segmentation of the primary tumor on CT demonstrated high agreement with CT/PET manual delineations and strongly correlated with the macroscopic diameter considered as the "gold standard". This method may be used routinely in clinical practice and could be employed as a starting point for treatment planning, target definition in multi-center clinical trials or for high throughput data mining research. This method is particularly suitable for peripherally located tumors.

}, keywords = {Algorithms, Humans, Lung Neoplasms, Multimodal Imaging, Positron-Emission Tomography, Tomography, X-Ray Computed}, issn = {1879-0887}, doi = {10.1016/j.radonc.2012.09.023}, author = {Rios Velazquez, Emmanuel and Aerts, Hugo J W L and Gu, Yuhua and Goldgof, Dmitry B and De Ruysscher, Dirk and Dekker, Andre and Korn, Ren{\'e} and Gillies, Robert J and Lambin, Philippe} } Error | CancerData.org

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