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Development and validation of a longitudinal soft-tissue metastatic lesion matching algorithm.

Victor Santoro-Fernandes1, Daniel Huff1, Mathew L Scarpelli2

  • 1School of Medicine and Public Health, Department of Medical Physics, University of Wisconsin, Madison, WI, United States of America.

Physics in Medicine and Biology
|July 14, 2021
PubMed
Summary

This study introduces an automated algorithm for matching metastatic lesions in longitudinal medical images, improving accuracy and efficiency for cancer treatment assessment. The new method achieves high accuracy, even in patients with extensive disease, outperforming previous techniques.

Keywords:
PET/CTimage registrationlesion matchingtreatment response assessment

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Area of Science:

  • Medical Imaging
  • Computational Biology
  • Oncology

Background:

  • Metastatic cancer involves numerous lesions that respond differently to treatment, necessitating lesion-level assessment for accurate response evaluation.
  • Manual matching of metastatic lesions in longitudinal scans is time-consuming, subjective, and prone to errors.
  • Automated methods are needed to improve the efficiency and objectivity of metastatic lesion tracking.

Purpose of the Study:

  • To develop and validate a fully automated algorithm for matching metastatic lesions in longitudinal PET/CT scans.
  • To assess the accuracy and robustness of the automated lesion matching algorithm across different patient cohorts.
  • To provide a more efficient and reliable tool for monitoring cancer treatment response at the lesion level.

Main Methods:

  • The algorithm employs a four-step process: 3D deformable image registration, conformal lesion dilation, lesion clustering based on local metrics, and non-greedy linear assignment for matching.
  • Optimization of deformable registration approaches (whole body and articulated) and lesion dilation magnitude was performed.
  • Validation involved 140 scan-pairs from 32 metastatic cancer patients across two clinical trials, with registration accuracy assessed via landmark distance.

Main Results:

  • The automated algorithm achieved high registration accuracy, ranging from 2.3 to 2.6 mm, using optimized deformable registration methods.
  • An optimal lesion dilation magnitude of 25 mm resulted in a near-perfect matching accuracy of 0.98.
  • The algorithm maintained high matching accuracy even in patients with a high disease burden, demonstrating robustness.

Conclusions:

  • The developed automated algorithm for metastatic lesion matching is highly accurate and represents a significant improvement over existing methods.
  • This automated approach enhances the precision and efficiency of lesion-level assessment in longitudinal whole-body scans.
  • The method has the potential to improve the understanding of treatment response in metastatic cancer patients.