A novel metric for quantification of homogeneous and heterogeneous tumors in PET for enhanced clinical outcome prediction.

Journal Article (Journal Article)

Oncologic PET images provide valuable information that can enable enhanced prognosis of disease. Nonetheless, such information is simplified significantly in routine clinical assessment to meet workflow constraints. Examples of typical FDG PET metrics include: (i) SUVmax, (2) total lesion glycolysis (TLG), and (3) metabolic tumor volume (MTV). We have derived and implemented a novel metric for tumor quantification, inspired in essence by a model of generalized equivalent uniform dose as used in radiation therapy. The proposed metric, denoted generalized effective total uptake (gETU), is attractive as it encompasses the abovementioned commonly invoked metrics, and generalizes them, for both homogeneous and heterogeneous tumors, using a single parameter a. We evaluated this new metric for improved overall survival (OS) prediction on two different baseline FDG PET/CT datasets: (a) 113 patients with squamous cell cancer of the oropharynx, and (b) 72 patients with locally advanced pancreatic adenocarcinoma. Kaplan-Meier survival analysis was performed, where the subjects were subdivided into two groups using the median threshold, from which the hazard ratios (HR) were computed in Cox proportional hazards regression. For the oropharyngeal cancer dataset, MTV, TLG, SUVmax, SUVmean and SUVpeak produced HR values of 1.86, 3.02, 1.34, 1.36 and 1.62, while the proposed gETU metric for a  = 0.25 (greater emphasis on volume information) enabled significantly enhanced OS prediction with HR  =  3.94. For the pancreatic cancer dataset, MTV, TLG, SUVmax, SUVmean and SUVpeak resulted in HR values of 1.05, 1.25, 1.42, 1.45 and 1.52, while gETU at a  = 3.2 (greater emphasis on SUV information) arrived at an improved HR value of 1.61. Overall, the proposed methodology allows placement of differing degrees of emphasis on tumor volume versus uptake for different types of tumors to enable enhanced clinical outcome prediction.

Full Text

Duke Authors

Cited Authors

  • Rahmim, A; Schmidtlein, CR; Jackson, A; Sheikhbahaei, S; Marcus, C; Ashrafinia, S; Soltani, M; Subramaniam, RM

Published Date

  • January 7, 2016

Published In

Volume / Issue

  • 61 / 1

Start / End Page

  • 227 - 242

PubMed ID

  • 26639024

Pubmed Central ID

  • PMC4887091

Electronic International Standard Serial Number (EISSN)

  • 1361-6560

Digital Object Identifier (DOI)

  • 10.1088/0031-9155/61/1/227

Language

  • eng

Conference Location

  • England