{"id":5617,"date":"2023-09-04T03:12:55","date_gmt":"2023-09-04T03:12:55","guid":{"rendered":"https:\/\/cm.vastapps.dev\/tcia-collection\/pdmr-833975-119-r\/"},"modified":"2023-09-13T12:00:01","modified_gmt":"2023-09-13T12:00:01","slug":"pdmr-833975-119-r","status":"publish","type":"tcia_collection","link":"https:\/\/cm.vastapps.dev\/tcia-collection\/pdmr-833975-119-r\/","title":{"rendered":"PDMR-833975-119-R"},"featured_media":7913,"template":"","citation-tax":[],"cancer_types":["Adenocarcinoma Pancreas"],"citations":[4509,2925],"collection_doi":"10.7937\/TCIA.0ECK-C338","collection_download_info":"","collection_downloads":[5094,5095,5096],"full_export":"

Summary<\/h1>

Characterization of tissue using in vivo<\/em> non-invasive imaging is the foundation of Radiology and is clinically used for detection and measurement of disease burden in oncology.\u00a0 With the migration of imaging to digital media the possibility for advanced mathematically based imaging biomarkers was realized. As part of this endeavor, researchers have developed various algorithms using neural networks, and classification techniques to improve tissue characterization (morphological changes).\u00a0 However, large datasets are a requirement in this research endeavor in part due to the genomic heterogeneity of tumors in the same histologic classification \u2013 many tumors from different patients are required to have enough with the same genomic characteristics to adequately evaluate the range of imaging variability for a specific genomic pattern.\u00a0 Pre-clinical animal models of patient derived xenografts may be an important resource by providing collections with a more homogenous tumor genome across the collection with companion extensive tumor genomic characterization available, allowing determination of the variability of imaging characteristics for that pattern in different individuals. This dataset of a patient derived xenograft\u00a0model adenocarcinoma pancreas PDMR: 833975-119-R<\/a> can be used for training algorithms for evaluating variations in tissue texture with respect to tumor growth and regrowth after surgical resection.\u00a0\u00a0<\/p>

In this study we performed a detailed imaging characterization (workflow below) of this model, details are provided in the attached standard operating procedures. Tumors in half of the mice were resected in the range 200-300 mm3<\/sup> size; tumors in the other half were allowed to grow until it was necessary to euthanize them because of tumor size.<\/p><\/div>

<\/span><\/p>

T2w MRI at 56 days post implant demonstrated a heterogenous tumor with an apparent central hemorrhage and the implant appears to have a defined capsule.\u00a0 On day 98 (post-implant) the tumor increased in size (618%) with very similar imaging characteristics as displayed on day 56.<\/p>

In contrast, the regrowth following surgical resection of the primary xenograft (105 days post resection) is homogenous, but also appears to have a well-defined capsule.<\/p>

PET\/CT Characterization of the primary tumor:\u00a0 Baseline PET (SOP attached) were performed when tumor reached an approximate 200 mm3<\/sup>.\u00a0\u00a0 Average\u00a0SUVbw_max values (n=5) were calculated; <\/span>[18<\/sup>F]FDG: 1.8 \u00b1 0.2 and [18<\/sup>F]FLT: 2.8 \u00b1 0.7.<\/span><\/p>

The imaging characteristics of\u00a0this model, which is available from the National Cancer Institute Patient-Derived Models\u00a0Repository (https:\/\/pdmr.cancer.gov\/<\/a>), is highly favorable for preclinical research studies when used in conjunction with non-contrast T2 weighted MRI.<\/p><\/div>

Acknowledgements<\/h3>
We would like to acknowledge the individuals and institutions that have provided data for this collection:<\/div>
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