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Acad Radiol. 2012 Oct;19(10):1201-7. doi: 10.1016/j.acra.2012.04.015. Epub 2012 Jul 26.

Evaluation of hepatic tumor response to yttrium-90 radioembolization therapy using texture signatures generated from contrast-enhanced CT images.

Author information

1
Center for Biomedical Imaging & Informatics, The Cancer Institute of New Jersey, 195 Little Albany St, Room 3521, New Brunswick, NJ 08903, USA. gensurrh@umdnj.edu

Abstract

RATIONALE AND OBJECTIVES:

The aim of this study was to explore the use of texture features generated from liver computed tomographic (CT) datasets as potential image-based indicators of patient response to radioembolization (RE) with yttrium-90 ((90)Y) resin microspheres, an emerging locoregional therapy for advanced-stage liver cancer.

MATERIALS AND METHODS:

Overall posttherapy survival and percent change in serologic tumor marker at 3 months posttherapy represent the primary clinical outcomes in this study. Thirty advanced-stage liver cancer cases (primary and metastatic) treated with RE over a 3-year period were included. Texture signatures for tumor regions, which were delineated to reveal boundaries with normal regions, were computed from pretreatment contrast-enhanced liver CT studies and evaluated for their ability to classify patient serologic response and survival.

RESULTS:

A series of systematic leave-one-out cross-validation studies using soft-margin support vector machine (SVM) classifiers showed hepatic tumor texton and local binary pattern (LBP) signatures both achieve high accuracy (96%) in discriminating subjects in terms of their serologic response. The image-based indicators were also accurate in classifying subjects by survival status (80% and 93% accuracy for texton and LBP signatures, respectively).

CONCLUSIONS:

Hepatic texture signatures generated from tumor regions on pretreatment triphasic CT studies were highly accurate in differentiating among subjects in terms of serologic response and survival. These image-based computational markers show promise as potential predictive tools in candidate evaluation for locoregional therapy such as RE.

PMID:
22841288
PMCID:
PMC3438382
DOI:
10.1016/j.acra.2012.04.015
[Indexed for MEDLINE]
Free PMC Article
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