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Cancer Radiomic and Perfusion Imaging Automated Framework: Validation on Musculoskeletal Tumors.

Elvis Duran Sierra1, Raul Valenzuela1, Mathew A Canjirathinkal1

  • 1Department of Musculoskeletal Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX.

JCO Clinical Cancer Informatics
|January 5, 2024
PubMed
Summary
This summary is machine-generated.

A new open-source software, CARPI, enables high-throughput cancer imaging analysis for radiomic features and perfusion. This tool differentiates tumor types and predicts treatment response, offering a cost-efficient solution for cancer research.

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Area of Science:

  • Radiology
  • Medical Imaging
  • Computational Biology

Background:

  • Commercial software limitations hinder cost-efficient, high-throughput cancer imaging analysis.
  • Developing open-source solutions is crucial for advancing cancer research workflows.

Purpose of the Study:

  • To develop and validate an open-source computational solution for vendor- and sequence-neutral high-throughput radiomic feature extraction and perfusion analysis in cancer research.

Main Methods:

  • Developed the Cancer Radiomic and Perfusion Imaging (CARPI) automated framework, a Python-based application.
  • Validated CARPI using two clinical datasets: pelvic chondrosarcomas/sacral chordomas (82 patients) and undifferentiated pleomorphic sarcoma (UPS) (26 patients).
  • CARPI processed 316 contour files, extracting 107 radiomic features and 7 perfusion parameters.

Main Results:

  • CARPI identified 18 significant radiomic features differentiating chordoma from chondrosarcoma (P < .00047).
  • In UPS, mean apparent diffusion coefficient increased 41% in good responders (P = .0017) post-radiation.
  • CARPI demonstrated ability to differentiate tumor types and predict treatment response.

Conclusions:

  • The CARPI framework successfully processes cancer imaging data for radiomic and perfusion analysis.
  • CARPI offers advantages in automated perfusion feature extraction and reporting compared to other open-source tools.
  • CARPI aids in differentiating tumor types and predicting patient treatment response using radiomic features.