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A CT Dataset with RECIST Measurements and Comprehensive Segmentation Masks for Tumors and Lymph Nodes.

Roberto Rojas-Pizarro1,2, Constanza Vásquez-Venegas1,3, Gonzalo Pereira4

  • 1Laboratory for Scientific Image Analysis SCIAN-Lab, Interdisciplinary Nucleus for Biology and Genetics, Institute of Biomedical Sciences ICBM, Faculty of Medicine, University of Chile, Av. Independencia 1027, Santiago, 8380453, Chile.

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This study introduces a new dataset for cancer treatment response assessment using RECIST 1.1 criteria. The dataset aids in developing AI tools for automated lesion measurement, improving oncological clinical trials.

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Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • The Response Evaluation Criteria in Solid Tumors (RECIST 1.1) is standard for assessing cancer treatment response.
  • Manual RECIST evaluation is time-consuming and prone to variability.
  • A lack of public datasets with RECIST-compliant annotations hinders AI development.

Purpose of the Study:

  • To present a novel, manually annotated dataset for RECIST 1.1 evaluation.
  • To facilitate the development and validation of AI-driven tools for automated RECIST measurements.
  • To promote global representation in medical AI research.

Main Methods:

  • Collected 58 CT scans from 22 cancer patients at the Clinical Hospital of the University of Chile.
  • Manually segmented 1,246 lesions.
  • Performed RECIST 1.1 compliant diameter measurements for 82 target lesions.

Main Results:

  • A comprehensive dataset of 1,246 segmented lesions with RECIST 1.1 measurements is now available.
  • The dataset includes data from a Latin American institution, enhancing global diversity.
  • The resource supports AI model validation, radiomics studies, and segmentation algorithm benchmarking.

Conclusions:

  • The new dataset addresses a critical gap in resources for AI in oncology.
  • It will accelerate the development of automated RECIST assessment tools.
  • This work supports more accurate and efficient cancer treatment evaluation.