The Cancer Imaging Archive

AREN0534-Tumor-Annotations | Annotations for Combination Chemotherapy and Surgery in Treating Young Patients With Wilms Tumor

DOI: 10.7937/N930-BM78 | Data Citation Required | 6 Views | Analysis Result

Summary

This dataset contains image annotations derived from the NCI Clinical Trial “Combination Chemotherapy and Surgery in Treating Young Patients With Wilms Tumor (AREN0534)”.  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. Scans were initially annotated by an international team of radiologists holding MBBS degrees or higher, which were then reviewed by US-based board-certified radiologists to ensure accuracy. In a typical patient the following time points were annotated:

1. Pre-surgical CT chest and CT/MRI abdomen
2. CT chest and/or CT/MRI abdomen at 6 weeks
3. Possible CT/MRI abdomen at 12 weeks.
4. Any negative imaging included past 12 weeks was annotated as negative. If any included imaging past 12 weeks is positive for tumor, the last positive exam was annotated.

In a typical patient the following annotation rules were followed:

1. The primary renal tumor(s) were annotated on post-contrast axial series. Normal renal parenchyma were excluded.
2. 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.  RECIST 1.1 principles were followed for lesion annotation, however, if <5 lesions measuring >1 cm were present, then smaller lesions were annotated, again up to 2 lesions per organ or 5 lesions per patient scan. Bone lesions were included if other lesions were not present.
3. Lesions were labeled separately.
4. Seed points were automatically generated but reviewed by a radiologist.
5. To ensure a high standard of accuracy and data quality, each annotation was reviewed by a secondary reader.

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 RTSS file.
2. SNOMED-CT “Anatomic Region Sequence” and “Segmented Property Category Code Sequence” and codes were inserted for all segmented structures.
3. 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”, “post-chemotherapy”, or “post-operative”.
   2. Content Item in “Acquisition Context Sequence” will be added containing “Time Point Type” using Concept Code Sequence (0040,A168) selected from:
      1. (255235001, SCT, “Pre-dose”)
      2. (262502001, SCT, “Post-chemotherapy”)
      3. (262061000, SCT, “Post-operative”)

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 (with authentication) to access these data can be found in the Additional Resources section below.

Data Access

Version 1: Updated 2023/02/08

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: