Exploring Feature-based Approaches in PET Images for Predicting Cancer Treatment Outcomes

Overview

Journal Pattern Recognit

Publisher Elsevier

Date 2010 Feb 18

PMID 20161266

Citations 220

Authors

I El Naqa

P Grigsby

A Apte

E Kidd

E Donnelly

D Khullar

S Chaudhari

D Yang

M Schmitt

Richard Laforest

W Thorstad

J O Deasy

Affiliations

Soon will be listed here.

Abstract

Accumulating evidence suggests that characteristics of pre-treatment FDG-PET could be used as prognostic factors to predict outcomes in different cancer sites. Current risk analyses are limited to visual assessment or direct uptake value measurements. We are investigating intensity-volume histogram metrics and shape and texture features extracted from PET images to predict patient's response to treatment. These approaches were demonstrated using datasets from cervix and head and neck cancers, where AUC of 0.76 and 1.0 were achieved, respectively. The preliminary results suggest that the proposed approaches could potentially provide better tools and discriminant power for utilizing functional imaging in clinical prognosis.

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