...
- Image Analyses
- You can filter the table for "Image Analyses" which may include expert-derived image annotations (e.g. Where is the tumor located? What is the shape of the tumor?) or quantitative imaging features (e.g. What is the tumor volume? What is the texture of the tumor?).
- The Browse Analysis Results page also contains similar types of data that were published by researchers who analyzed TCIA collections.
- Clinical data (e.g. demographics, outcomes, stage) - Filter the table for "clinical" to find datasets with this type of information.
- Finding datasets with healthy controls or other diseases to study cancer detection can be achieved by filtering for "non-cancer", which will show datasets that were screening studies, have other diagnoses or healthy controls. Also, if you filter for "screen" you'll find datasets that were screening for cancer which should have a mix of patients with and without cancer. Note: Filtering for "screen" doesn't catch the NLST dataset (even though that stands for National Lung Screening Trial) because "screening" wasn't spelled out in the dataset short title. However, this is a massive low-dose CT lung cancer screening dataset with 26,000+ subjects.(e.g. COVID-19) or healthy controls.
- Distinguishing between cancer types (e.g. low grade vs high grade gliomas) - Cancer Type is one of the columns on the Browse Collections, making it easy to filter or search for datasets based on this criteria.
- Genomic/Proteomic subtypes - Filter the table for "genomics" or "proteomics" on the Browse Collections page to find datasets with this type of information. In most cases you will need to retrieve specific details about the patients' genomic/proteomic from external databases such as NCI's Genomic Data Commons or Proteomic Data Commons. Please note these websites are not supported by TCIA staff, but we do coordinate with the teams that operate these archives to ensure common patient identifiers are used which enable you to link these data to TCIA images.
...