ARAR0331-Tumor-Annotations | Annotations for Radiation Therapy, Amifostine, and Chemotherapy in Treating Young Patients With Newly Diagnosed Nasopharyngeal Cancer Collection

DOI: 10.25737/H65S-8F58 | Data Citation Required | Analysis Result

Summary

This dataset contains image annotations derived from the NCI Clinical Trial “Radiation Therapy, Amifostine, and Chemotherapy in Treating Young Patients With Newly Diagnosed Nasopharyngeal Cancer (ARAR0331)”.  This dataset was generated as part of an NCI project to augment TCIA datasets with annotations that will improve their value for cancer researchers and AI developers.

Annotation Protocol

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. The following annotation rules were followed:

1. PERCIST criteria was followed for PET imaging. Specifically, the lesions estimated to have the most elevated SUVmax were annotated.
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 >1.5 cm in short axis. Other lesions were annotated if >1 cm.
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.
4. MRIs were annotated using the T1-weighted axial post contrast sequence.
5. Some lesions may cross multiple exams (ie. an MRI of the head and an MRI of the neck). The images portions on each exam were then annotated. If, however, the complete lesion was visualized on either a neck or head exam, then the other exam was not annotated to avoid redundancy.
6. Lesions were labeled separately.
7. The volume of each annotated lesion was calculated and reported in cubic centimeters [cc] in the Annotation Metadata CSV.
8. A “negative” annotation was created for any exam without findings.

At each time point:

1. A seed point (kernel) was created for each segmented structure. The seed points for each segmentation are provided in a separate DICOM RTSTRUCT file.
2. SNOMED-CT “Anatomic Region Sequence” and “Segmented Property Category Code Sequence” and codes were inserted for all segmented structures.
3. “Tracking ID” and “Tracking UID” tags were inserted for each segmented structure to enable longitudinal lesion tracking.
4. Imaging time point codes were inserted to help identify each annotation in the context of the clinical trial assessment protocol.
   1. “Clinical Trial Time Point ID” was used to encode time point type using one of the following strings as applicable: “pre-dose” or “post-chemotherapy”.
   2. Content Item in “Acquisition Context Sequence” was added containing “Time Point Type” using Concept Code Sequence (0040,A168) selected from:
      1. (255235001, SCT, “Pre-dose”)
      2. (262502001, SCT, “Post-chemotherapy”)

Important supplementary information and sample code

1. A spreadsheet containing key details about the annotations is available in the Data Access section below.
2. A Jupyter notebook demonstrating how to use the NBIA Data Retriever Command-Line Interface application and the REST API to access these data can be found in the Additional Resources section below.

Data Access

Version 1: Updated 2023/11/14

Collections Used In This Analysis Result

Additional Resources For This Dataset

• NCTN/NCORP Data Archive provides the Clinical Data files related to these subjects, and is also where you go to request access to the entire dataset
• Jupyter notebook demonstrating how to use the NBIA Data Retriever Command-Line Interface application and REST API (with authentication) to access these data
• Instructions for Visualizing these data in 3D Slicer

Citations & Data Usage Policy

Data Citation Required: Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution must include the following citation, including the Digital Object Identifier: