{"id":2890,"date":"2018-09-16T19:54:27","date_gmt":"2018-09-16T23:54:27","guid":{"rendered":"https:\/\/www.med.unc.edu\/bigs2?page_id=2890&preview_id=2890"},"modified":"2019-10-08T17:41:08","modified_gmt":"2019-10-08T21:41:08","slug":"radiomics","status":"publish","type":"page","link":"https:\/\/www.med.unc.edu\/bigs2\/projects\/radiomics\/","title":{"rendered":"Radiomics"},"content":{"rendered":"
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Title:\u00a0<\/strong>Human papilloma virus (HPV) prediction from CT images<\/b><\/h4>\n

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Introduction<\/h4>\n

Human papilloma virus (HPV) associated cancers have been shown to have increased survival and better tumor control\u00a0with radiotherapy than non-HPV-associated cancers. HPV status is predictive of outcomes, and is tested routinely using\u00a0immunohistochemistry for p16, a protein, or in situ hybridization for viral DNA.\u00a0Recent data suggest that “radiomics”, or extraction of image texture analysis to generate mineable quantitative data\u00a0from medical images, can reflect phenotypes for various cancers. Several groups have shown that developed radiomics signatures,\u00a0in head and neck cancers among other tumor sites, can be correlated with survival outcomes.\u00a0The University of Texas MD Anderson Cancer Center (MDACC) provided dataset of anonymized DICOM files represent a realtively\u00a0uniform cohort of 315 oropharynx cancer patients, supplemented with relevant clinical data, known etiological\/biological\u00a0correlates (specifically, human papilloma virus “HPV” status) as ground truth.\u00a0Our major target is to assess the ability of participant-developed radiomic workflows to predict binary (phenotypic\/genotypic)\u00a0HPV status, using a defined \u201cTraining\u201d cohort as a “prior” dataset that includes all input and outcome data, to build up an algorithm.<\/p>\n

Questions<\/h4>\n

Using expert-segmented contrast-enhanced computed tomography (CT) images to predict\u00a0whether a tumor is HPV positive (as defined by p16 or HPV testing).<\/p>\n

Methods<\/h4>\n

First, we extracted the global radiomics features from each ROI segmented by radiologists and checked the quality of features\u00a0between training and testing subjects to ensure the similarity of the distributions of\u00a0the features between training and testing cohort. Then, we picked the features that were differentially distributed between recurrence\u00a0subjects of the training cohort and non-recurrence ones. The correlation between these differentially distributed features and\u00a0the responses (which is recurrence and non-recurrence) were then calculated to narrow down the candidate features.\u00a0Last the features were ranked by the prediction performance when built model with the feature alone.\u00a0the final model was then picked by a forward selection method.<\/p>\n

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Findings<\/h4>\n

Two features (Mean Breadth and Spherical Disproportion) were finally selected. Mean breadth is the mean \u201cwidth\u201d of the ROI,\u00a0while spherical disproportion is the ratio of the surface area of the image ROI to the surface area of a sphere with the same\u00a0volume as the image ROI. We found that, for HPV+ subjects, both mean breadth and spherical disproportion tend to have smaller values.\u00a0For the testing cohort, based on our method, the area under the receiver operator characteristic curve (ROC AUC) of HPV status achieved\u00a00.915, which means our proposed workflow can detect some potential radioimcs biomarkers for HPV associated cancer prediction. In addtion,\u00a0we have finished in the first place in the MICCAI Radiomics Challenge (MICCAI<\/a>).<\/p>\n

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References<\/h4>\n

In order to highlight and describe our approach and algorithm in the challenge, we recived a proffered manuscript acceptance\u00a0in the well-renowned international journal: Clinical and Translational Radiation Oncology (ctRO).<\/p>\n<\/div>\n<\/div>\n<\/div>\n

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Title:\u00a0Human papilloma virus (HPV) prediction from CT images   Introduction Human papilloma virus (HPV) associated cancers have been shown to have increased survival and better tumor control\u00a0with radiotherapy than non-HPV-associated cancers. HPV status is predictive of outcomes, and is tested routinely using\u00a0immunohistochemistry for p16, a protein, or in situ hybridization for viral DNA.\u00a0Recent data suggest … Read more<\/a><\/p>\n","protected":false},"author":55348,"featured_media":0,"parent":2857,"menu_order":2,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":"","_links_to":"","_links_to_target":""},"class_list":["post-2890","page","type-page","status-publish","hentry","odd"],"acf":[],"yoast_head":"\nRadiomics - BIG-S2<\/title>\n<meta name=\"robots\" content=\"noindex, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Radiomics - BIG-S2\" \/>\n<meta property=\"og:description\" content=\"Title:\u00a0Human papilloma virus (HPV) prediction from CT images   Introduction Human papilloma virus (HPV) associated cancers have been shown to have increased survival and better tumor control\u00a0with radiotherapy than non-HPV-associated cancers. 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