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This data collection consists of

The full title can be “Radiomic Biomarkers in Oropharyngeal Carcinoma”.

oropharynx squamous cell carcinoma

This will be sufficient for the 2 manuscripts that use the data to be shared on TCIA.

Radiomic Biomarkers to Refine Risk Models for Distant Metastasis in Oropharyngeal Carcinoma

One is PMID 26264429, and the other is not yet on Pubmed but has recently gone In Press:

https://www.sciencedirect.com/science/article/pii/S0360301618301299

Purpose: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Two sets of images were created to evaluate deformable image registration accuracy. The first set contains CT, T1-, and T2-weighted images from a porcine phantom. The phantom was implanted with ten 0.35 mm gold markers and then immobilized in a plastic container with movable dividers. The porcine phantom was compressed in 4 different ways and images were acquired in each position. The markers were visible on the CT scans but not the MR scans due to the selected voxel size. Therefore, the markers do not interfere with the registration between MR images and the marker locations can be obtained from the CT images to determine accuracy. The second set of images are synthetic images derived from 28 head and neck squamous cell carcinoma patients who had pre-, mid-, and post-radiotherapy treatment MR scans. From these patients, inter- and intra-patient models were created. Four synthetic pre-treatment images were created by using the inter-patient model on a selected template patient. Four synthetic post-treatment images were created for each synthetic pre-treatment image using the intra-patient model.

Rachel B. Ger, Jinzhong Yang, Yao Ding, Megan C. Jacobsen, Carlos E. Cardenas, Clifton D. Fuller, Rebecca M. Howell, Heng Li, R. Jason Stafford, Shouhao Zhou, Laurence E. Court