Characterizing tumor heterogeneity with functional imaging and quantifying high-risk tumor volume for early prediction of treatment outcome: Cervical cancer as a model

Nina A. Mayr*, Zhibin Huang, Jian Z. Wang, Simon S. Lo, Joline M. Fan, John C. Grecula, Steffen Sammet, Christina L. Sammet, Guang Jia, Jun Zhang, Michael V. Knopp, William T C Yuh

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

49 Scopus citations

Abstract

Purpose: Treatment response in cancer has been monitored by measuring anatomic tumor volume (ATV) at various times without considering the inherent functional tumor heterogeneity known to critically influence ultimate treatment outcome: primary tumor control and survival. This study applied dynamic contrast-enhanced (DCE) functional MRI to characterize tumors' heterogeneous subregions with low DCE values, at risk for treatment failure, and to quantify the functional risk volume (FRV) for personalized early prediction of treatment outcome. Methods and Materials: DCE-MRI was performed in 102 stage IB 2-IVA cervical cancer patients to assess tumor perfusion heterogeneity before and during radiation/chemotherapy. FRV represents the total volume of tumor voxels with critically low DCE signal intensity (<2.1 compared with precontrast image, determined by previous receiver operator characteristic analysis). FRVs were correlated with treatment outcome (follow-up: 0.2-9.4, mean 6.8 years) and compared with ATVs (Mann-Whitney, Kaplan-Meier, and multivariate analyses). Results: Before and during therapy at 2-2.5 and 4-5 weeks of RT, FRVs >20, >13, and >5 cm 3, respectively, significantly predicted unfavorable 6-year primary tumor control (p = 0.003, 7.3 × 10 -8, 2.0 × 10 -8) and disease-specific survival (p = 1.9 × 10 -4, 2.1 × 10 -6, 2.5 × 10 -7, respectively). The FRVs were superior to the ATVs as early predictors of outcome, and the differentiating power of FRVs increased during treatment. Discussion: Our preliminary results suggest that functional tumor heterogeneity can be characterized by DCE-MRI to quantify FRV for predicting ultimate long-term treatment outcome. FRV is a novel functional imaging heterogeneity parameter, superior to ATV, and can be clinically translated for personalized early outcome prediction before or as early as 2-5 weeks into treatment.

Original languageEnglish (US)
Pages (from-to)972-979
Number of pages8
JournalInternational Journal of Radiation Oncology Biology Physics
Volume83
Issue number3
DOIs
StatePublished - Jul 1 2012

Keywords

  • Anatomic tumor volume
  • Cervical cancer
  • Dynamic contrast enhanced (DCE) MRI
  • Functional tumor imaging
  • Magnetic resonance imaging (MRI)
  • Tumor heterogeneity

ASJC Scopus subject areas

  • Radiation
  • Oncology
  • Radiology Nuclear Medicine and imaging
  • Cancer Research

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