Characterizing Tumor Heterogeneity With Functional Imaging and Quantifying High-Risk Tumor Volume for Early Prediction of Treatment Outcome: Cervical Cancer as a Model

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 IB2–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 cm3, 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.

Cluster analysis of dynamic contrast enhanced MRI reveals tumor subregions related to locoregional relapse for cervical cancer patients

“…Tumor regions of low enhancement most likely reflect areas of low perfusion, low oxygen concentration and thus high radioresistance. These characteristics have previously been found to be negatively associated with outcome [15,16], in line with the findings of this study. The low-enhancement cluster was also found to be significantly correlated to tumor volume.…”

The role of texture analysis in imaging as an outcome predictor and potential tool in radiotherapy treatment planning

“…Assessing tumour heterogeneity by utilizing dynamic contrast enhanced (DCE)-MRI in defining tumour functional risk volume (FRV) to predict treatment outcome in cervical cancer was investigated by Mayr et al 49 in 2012. FRV is defined as a region of low-contrast uptake, where the signal intensity is ,2.1 when compared with that of the image before contrast injection.…”

“…Pre-treatment FRV was least predictive, where the power of prediction increased from 24.3% to 42.5% for FRV 2 and 45.2% for FRV 3 in discriminating 6-year actuarial tumour control rate and recurrences of primary tumour. 49,50 GLCMbased texture analysis in DCE-MRI 51 was reported to be successful in discriminating responders and non-responders for 89 patients diagnosed with locally advanced breast cancer and receiving neoadjuvant chemotherapy, its predictive value for patients undergoing radiotherapy is yet to be determined. Nevertheless, from 14 extracted features, only contrast, difference variance and difference entropy reached significance.…”

miR‑197 is downregulated in cervical carcinogenesis and suppresses cell proliferation and invasion through targeting forkhead box M1

“…Despite marked progress in the diagnosis, surgical methods and application of comprehensive therapy, prognosis remains unsatisfactory (5). The 5-year overall survival rate for patients with locally advanced cervical cancer is between 30 and 50% compared with between 5 and 15% for patients with metastatic disease (6). Therefore, it is imperative to fully understand the underlying molecular mechanism of cervical cancer and provide novel diagnostic and prognostic markers, and identify novel therapeutic targets for treatment of this disease.…”