{"id":5743,"date":"2023-09-04T03:35:11","date_gmt":"2023-09-04T03:35:11","guid":{"rendered":"https:\/\/cm.vastapps.dev\/tcia-analysis-result\/radiomics-tumor-phenotypes\/"},"modified":"2023-09-13T12:08:08","modified_gmt":"2023-09-13T12:08:08","slug":"radiomics-tumor-phenotypes","status":"publish","type":"tcia_analysis_result","link":"https:\/\/cm.vastapps.dev\/tcia-analysis-result\/radiomics-tumor-phenotypes\/","title":{"rendered":"RADIOMICS-TUMOR-PHENOTYPES"},"featured_media":8548,"template":"","cancer_types":false,"citations":[4740,4741,2925],"full_export":"

Description<\/h2>

This data applies a radiomic approach to computed tomography data of 1,019 patients with lung or head-and-neck cancer which are described in Nature Communications (<\/span>http:\/\/doi.org\/10.1038\/ncomms5006<\/a>)<\/span>.\u00a0 The various arms of the study are represented in TCIA as distinct Collections including\u00a0N<\/a>SCLC-Radiomics<\/a>\u00a0(Lung1),\u00a0NSCLC-Radiomics-Genomics<\/a>\u00a0(Lung3),\u00a0<\/span><\/span>H<\/a>ead-Neck-Radiomics-HN1<\/a>\u00a0(H&N1),\u00a0NSCLC-Radiomics-Interobserver1<\/a> (Multiple delineation), and\u00a0<\/span>RIDER Lung CT Segmentation Labels from: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach (RIDER-LungCT-Seg)<\/a>\u00a0(RIDER test\/retest).<\/span><\/p>

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Radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features. In present analysis 440 features quantifying tumour image intensity, shape and texture, were extracted. We found that a large number of radiomic features have prognostic power in independent data sets, many of which were not identified as significant before. Radiogenomics analysis revealed that a prognostic radiomic signature, capturing intra-tumour heterogeneity, was 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.<\/p>

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