{"id":5818,"date":"2023-09-04T03:40:11","date_gmt":"2023-09-04T03:40:11","guid":{"rendered":"https:\/\/cm.vastapps.dev\/tcia-analysis-result\/cptac-pda-tumor-annotations\/"},"modified":"2023-09-13T12:12:31","modified_gmt":"2023-09-13T12:12:31","slug":"cptac-pda-tumor-annotations","status":"publish","type":"tcia_analysis_result","link":"https:\/\/cm.vastapps.dev\/tcia-analysis-result\/cptac-pda-tumor-annotations\/","title":{"rendered":"CPTAC-PDA-TUMOR-ANNOTATIONS"},"featured_media":0,"template":"","cancer_types":["Ductal Adenocarcinoma"],"citations":[4849,2925],"full_export":"

Summary<\/h1>

This dataset contains image annotations derived from "<\/span>The Clinical Proteomic Tumor Analysis Consortium Pancreatic Ductal Adenocarcinoma Collection (CPTAC-PDA)<\/a>\u201d.\u00a0 This dataset was generated as part of a National Cancer Institute project to augment images from The Cancer Imaging Archive with tumor annotations that will improve their value for cancer researchers and artificial intelligence experts.<\/span><\/p>

Annotation Protocol<\/strong><\/h3>

For each patient, all scans were reviewed to identify and annotate the clinically relevant time points and sequences\/series. In a typical patient all available time points were annotated. T<\/span>he following annotation rules were followed:<\/span><\/p>

  1. PERCIST criteria was followed for PET imaging. Specifically, the lesions estimated to have the most elevated SUVmax were annotated.<\/span><\/li>
  2. RECIST 1.1 was otherwise generally followed for MR and CT imaging. A maximum of 5 lesions were annotated per patient scan (timepoint); no more than 2 per organ. The same 5 lesions were annotated at each time point. Lymph nodes were annotated if >1 cm in short axis. Other lesions were annotated if >1 cm. If the primary lesion measures < 1 cm, it was still annotated.<\/span><\/li>
  3. Three-dimensional segmentations of lesions were created in the axial plane. If no axial plane was available, lesions were annotated in the coronal plane.<\/span><\/li>
  4. MRIs were annotated using axial T1-weighted post contrast sequences that best demonstrated the tumor.<\/span><\/li>
  5. CTs were annotated using all axial post contrast series. If not available, the axial non-contrast series were annotated.<\/span><\/li>
  6. PET\/CTs were annotated on the CT and attenuation corrected PET images, unless there was a diagnostic CT from the same time point, in which case the CT portion of the PET\/CT was not annotated.<\/li>
  7. Lesions were labeled separately.<\/span><\/li>
  8. Seed points were automatically generated, but reviewed by a radiologist.<\/span><\/li>
  9. A \u201cnegative\u201d annotation was created for any exam without findings.<\/span><\/li><\/ol>

    At each time point:<\/span><\/p>

    1. Volume calculations were performed for each segmented structure. \u00a0These calculations are provided in the Annotation Metadata CSV.<\/li>
    2. A seed point (kernel) was created for each segmented structure. The seed points for each segmentation are provided in a separate DICOM RTSTRUCT file.<\/li>
    3. SNOMED-CT \u201cAnatomic Region Sequence\u201d and \u201cSegmented Property Category Code Sequence\u201d and codes were inserted for all segmented structures.<\/li>
    4. Imaging time point codes were inserted to help identify each annotation in the context of the clinical trial assessment protocol.
      1. \u201cClinical Trial Time Point ID\u201d was used to encode time point type using one of the following strings as applicable: \u201cpre-dose\u201d or \u201cpost-chemotherapy\u201d.<\/li>
      2. Content Item in \u201cAcquisition Context Sequence\u201d was added containing "Time Point Type" using Concept Code Sequence (0040,A168) selected from:
        1. (255235001, SCT, \u201cPre-dose\u201d)<\/li>
        2. (719864002, SCT, "Post-cancer treatment monitoring")<\/li><\/ol><\/li><\/ol><\/li><\/ol>

          Important supplementary information and sample code<\/h3>
          1. A spreadsheet containing key details about the annotations is available in the\u00a0<\/span>Data Access<\/strong>\u00a0section below.<\/li>
          2. A\u00a0Jupyter notebook demonstrating how to use the\u00a0NBIA Data Retriever Command-Line Interface<\/a>\u00a0<\/span>application and the\u00a0<\/span>REST API<\/a>\u00a0to access these data can be found in the\u00a0Additional Resources<\/strong>\u00a0section below.<\/li><\/ol>

            <\/p>