Background and purpose: MRI, applying dynamic contrast-enhanced (DCE) and diffusion-weighted (DW) sequences, and 18F-fluorodeoxyglucose (18F-FDG) PET/CT provide information about tumor aggressiveness that is unexploited in treatment of locally advanced cervical cancer (LACC). We investigated the potential of a multimodal combination of imaging parameters for classifying patients according to their risk of recurrence.
Materials and methods: Eighty-two LACC patients with diagnostic MRI and FDG-PET/CT, treated with chemoradiotherapy, were collected. Thirty-eight patients with MRI only were included for validation of MRI results. Endpoints were survival (disease-free, cancer-specific, overall) and tumor control (local, locoregional, distant). Ktrans, reflecting vascular function, apparent diffusion coefficient (ADC), reflecting cellularity, and standardized uptake value (SUV), reflecting glucose uptake, were extracted from DCE-MR, DW-MR and FDG-PET images, respectively. By applying an oxygen consumption and supply-based method, ADC and Ktrans parametric maps were voxel-wise combined into hypoxia images that were used to determine hypoxic fraction (HF).
Results: HF showed a stronger association with outcome than the single modality parameters. This association was confirmed in the validation cohort. Low HF identified low-risk patients with 95% precision. Based on the 50th SUV-percentile (SUV50), patients with high HF were divided into an intermediate- and high-risk group with high and low SUV50, respectively. This defined a multimodality biomarker, HF/SUV50. HF/SUV50 increased the precision of detecting high-risk patients from 41% (HF alone) to 57% and showed prognostic significance in multivariable analysis for all endpoints.
Conclusion: Multimodal combination of MR- and FDG-PET/CT-images improves classification of LACC patients compared to single modality images and clinical factors.
Keywords: Cervical cancer; Hypoxia; Multimodal imaging; Prognostic biomarkers; SUV.
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