{"id":5670,"date":"2023-09-04T03:16:50","date_gmt":"2023-09-04T03:16:50","guid":{"rendered":"https:\/\/cm.vastapps.dev\/tcia-collection\/opc-radiomics\/"},"modified":"2023-09-13T12:02:24","modified_gmt":"2023-09-13T12:02:24","slug":"opc-radiomics","status":"publish","type":"tcia_collection","link":"https:\/\/cm.vastapps.dev\/tcia-collection\/opc-radiomics\/","title":{"rendered":"OPC-RADIOMICS"},"featured_media":0,"template":"","citation-tax":[],"cancer_types":["Oropharyngeal"],"citations":[4608,4609,2925],"collection_doi":"10.7937\/tcia.2019.8dho2gls","collection_download_info":"Some data in this collection contains images that could potentially be used to reconstruct a human face. To safeguard the privacy of participants, users must sign and submit a TCIA Restricted License Agreement<\/a> to help@cancerimagingarchive.net<\/a> before accessing the data.\n\nClick the Versions tab for more info about data releases.","collection_downloads":[5220,5221],"full_export":"

Summary<\/h2>

<\/p>

Information<\/p><\/span>

This collection has been deprecated. Data from the collection formerly called OPC-Radiomics has been updated. The data are downloadable but no longer viewable in the Cancer Imaging Archive. Please view the RADCURE page to obtain access to the updated data: https:\/\/doi.org\/10.7937\/J47W-NM11<\/a><\/span><\/span>.<\/span><\/p><\/div><\/div>

Oropharynx cancer is increasing in frequency in North America. More refined risk stratification and treatment personalization strategies are needed to improve cure rates and limit toxicities. Radomic features have shown promise for prognostication in oropharynx cancer based on datasets including hundreds of patients. Clinical outcomes data for oropharynx cancer patients treated at Princess Margaret Cancer Centre is of the highest quality, collected prospectively at the point of care. This data has resulted in the recent AJCC\/UICC 8th edition staging system for oropharynx cancer (first published by O'Sullivan et al. in Lancet Oncology, 2016 [PMID 26936027<\/a>]). By making publicly available the published data from our institution, which has now been published twice, we will empower researchers to perform meta-analysis and validation exercises of prognostic radiomic models. Ultimately this will lead to more robust models and bring us closer to clinical implementation and impact for our patients.<\/p>

Purpose:<\/strong><\/p>

Distant metastasis (DM) is the main cause of death for patients with human papillomavirus (HPV)-related oropharyngeal cancers (OPC); yet there are few reliable predictors of DM in this disease. The role of quantitative imaging (i.e. radiomic) analysis was examined to determine whether there are primary tumor features discernible on imaging studies that associate with a higher risk of developing DM.\u00a0\u00a0This data collection consists of\u00a0<\/span>oropharynx squamous cell carcinoma.<\/p>

Methods:<\/strong><\/p>

Radiotherapy planning CT scans were retrieved for all non-metastatic p16-positive OPC patients treated with radiotherapy or chemoradiotherapy at a single institution between 2005 and 2010. Radiomic biomarkers were derived from each gross tumor volume (GTV). Biomarkers included four representative radiomic features from tumor first order statistics, shape, texture, and wavelet groups as well as a combined four-feature signature. Univariable Cox proportional hazards models for DM risk were identified. Discriminative performance of prognostic univariable and multivariable models was compared using the concordance index (C-index). Subgroup analyses were performed.<\/p>

Results:<\/strong><\/p>

There were 300 HPV-related OPC patients who were eligible for the analysis. A total of 36 DM events occurred within a median follow-up of five years. On univariable analysis, top results included the four representative radiomic features (p<0.001), the radiomic signature (p<0.001), tumor stage (p<0.001), tumor diameter (p<0.001), and tumor volume (p<0.001). C-indices of the radiomic features (0.670-0.686), radiomic signature (0.670), stage (0.633), and tumor size metrics (0.653-0.674) demonstrated moderate discrimination of DM risk. Combined clinical-radiomic models yielded significantly improved performance (0.701-0.714; p<0.05). In subgroup analyses, the radiomic biomarkers consistently stratified patients for DM risk, particularly for those cohorts with greater risks (0.663-0.796), such as patients with stage III disease.<\/p>

Conclusions:<\/strong><\/p>

Radiomic biomarkers appear to classify DM risk for patients with non-metastatic HPV-related OPC. Radiomic biomarkers could be used either alone or with other clinical characteristics in assignment of DM risk in future HPV-related OPC clinical trials.<\/p>

Additional References:\u00a0<\/strong><\/p>