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A Lesion-Based Response Prediction Model Using Pretherapy PET/CT Image Features for Y90 Radioembolization to Hepatic Malignancies.

Authors
  • Mehta, Rahul1, 2
  • Cai, Kejia1, 2, 3
  • Kumar, Nishant1
  • Knuttinen, M Grace1
  • Anderson, Thomas M1
  • Lu, Hui2
  • Lu, Yang1
  • 1 1 Department of Radiology, College of Medicine, University of Illinois Hospital & Health Sciences System, Chicago, IL, USA.
  • 2 2 Department of Bioengineering, College of Medicine, University of Illinois Hospital & Health Sciences System, Chicago, IL, USA.
  • 3 3 The Center for MR Research, College of Medicine, University of Illinois Hospital & Health Sciences System, Chicago, IL, USA.
Type
Published Article
Journal
Technology in Cancer Research & Treatment
Publisher
SAGE Publications
Publication Date
Oct 01, 2017
Volume
16
Issue
5
Pages
620–629
Identifiers
DOI: 10.1177/1533034616666721
PMID: 27601017
Source
Medline
Keywords
License
Unknown

Abstract

We present a probabilistic approach to identify patients with primary and secondary hepatic malignancies as responders or nonresponders to yttrium-90 radioembolization therapy. Recent advances in computer-aided detection have decreased false-negative and false-positive rates of perceived abnormalities; however, there is limited research in using similar concepts to predict treatment response. Our approach is driven by the goal of precision medicine to determine pretherapy fluorine-18-2-fluoro-2-deoxy-d-glucose positron emission tomography and computed tomography imaging parameters to facilitate the identification of patients who would benefit most from yttrium-90 radioembolization therapy, while avoiding complex and costly procedures for those who would not. Our algorithm seeks to predict a patient's response by discovering common co-occurring image patterns in the lesions of baseline fluorine-18-2-fluoro-2-deoxy-d-glucose positron emission tomography and computed tomography scans by extracting invariant shape and texture features. The extracted imaging features were represented as a distribution of each subject based on the bag-of-feature paradigm. The distribution was applied in a multinomial naive Bayes classifier to predict whether a patient would be a responder or nonresponder to yttrium-90 radioembolization therapy based on the imaging features of a pretherapy fluorine-18-2-fluoro-2-deoxy-d-glucose positron emission tomography and computed tomography scan. Comprehensive published criteria were used to determine lesion-based clinical treatment response based on fluorine-18-2-fluoro-2-deoxy-d-glucose positron emission tomography and computed tomography imaging findings. Our results show that the model is able to predict a patient with liver cancer as a responder or nonresponder to yttrium-90 radioembolization therapy with a sensitivity of 0.791 using extracted invariant imaging features from the pretherapy fluorine-18-2-fluoro-2-deoxy-d-glucose positron emission tomography and computed tomography test. The sensitivity increased to 0.821 when combining extracted invariant image features with variable features of tumor volume.

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