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

Victor Santoro-Fernandes¹, Daniel Huff¹, Mathew L Scarpelli², Timothy G Perk^1,3, Mark R Albertini⁴, Scott Perlman⁵, Stephen S F Yip^3,5, Robert Jeraj^1,6

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

²Department of Neuroimaging, Barrow Neurological Institute, Phoenix, AZ, United States of America.

³AIQ Global, Madison, WI, United States of America.

⁴School of Medicine and Public Health, Department of Medicine, University of Wisconsin, Madison, WI, United States of America.

⁵School of Medicine and Public Health, Department of Radiology, University of Wisconsin, Madison, WI, United States of America.

⁶Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia.

PubMed PMC DOI PDF

Abstract

Metastatic cancer presents with many, sometimes hundreds of metastatic lesions through the body, which often respond heterogeneously to treatment. Therefore, lesion-level assessment is necessary for a complete understanding of disease response. Lesion-level assessment typically requires manual matching of corresponding lesions, which is a tedious, subjective, and error-prone task. This study intro…

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