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TCIA includes a wealth of non-image data that could be utilized for image classification purposes.  

1. Image Analyses - Filter
   1. You can also filter the table for "Image Analyses"
    on
   1. on the Browse Collections page to find datasets with this type of information.  Image Analyses could 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?). 
   In the case of "radiomics" and other quantitative imaging features it is critical to use standardized image feature definitions such as those outlined in this publication.  
   2. The Browse Analysis Results page contains similar types of analysis data that were published by researchers who analyzed TCIA collections.
2. Clinical data (e.g. outcomes, stage) - Filter the table for "clinical" on the Browse Collections page to find datasets with this type of information
3. 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.
4. 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.

Guidance on sharing datasets related to Machine Learning or Artificial Intelligence studies on TCIA

In the case of "radiomics" and other quantitative imaging features it is critical to use standardized image feature definitions such as those outlined in this publication.  

Deep Learning parameters are critical for researchers to reproduce Deep Learning experiments. Where applicable, we recommend that data submitters include the following key pieces of information in their dataset summaries such that TCIA users can easily reproduce their study and compare their analysis results.

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