Serial Non-contrast Non-gated T2w MRI Datasets of Patient Derived Xenograft Cancer Models for Development of Tissue Characterization Algorithms (PDMR-Texture Analysis)

This collection contains serial non-contrast non-gated T2w MRI of 18 patient derived xenograft cancer models. 175 mice were imaged at multiple time points (514 total studies) for researchers to develop algorithms using neural networks, and classification techniques to improve tissue characterization (morphological changes) for the improvement in patient care through advances in precision medicine.
Characterization of tissue using non-invasive in vivo imaging techniques is used for detection and measurement of disease burden in oncology. Researchers have developed numerous algorithms, such as neural networks, and classification techniques to improve the characterization (morphological changes) of tissue. Unfortunately, to obtain statistical significance, large datasets are a requirement in this research endeavor due to tumor heterogeneity within the same histologic classification. Pre-clinical patient derived xenograft animal models can be a significant resource by providing collections with a more homogenous tumor genome across the collection with companion genomic and pathologic characterization available (https://pdmr.cancer.gov/), allowing determination of the variability of imaging characteristics.

This dataset of a patient derived xenograft model (below table) can be used for training algorithms for evaluating variations in tissue texture with respect to tumor growth and cancer model.