Abstract

BackgroundPerfusion CT can offer functional information about tumor angiogenesis, and 18F-FDG PET/CT quantifies the glucose metabolic activity of tumors. This prospective study aims to investigate the value of biologically relevant imaging biomarkers for predicting treatment response and survival outcomes in patients with locally advanced esophageal squamous cell cancer (LA ESCC).MethodsTwenty-seven patients with pathologically proven ESCC were included. All patients had undergone perfusion CT and 18F-FDG PET/CT using separate imaging systems before receiving definitive chemoradiotherapy (dCRT). The perfusion parameters included blood flow (BF), blood volume (BV), and time to peak (TTP), and the metabolic parameters included maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG). The flow-metabolism ratio (FMR) was defined as BF divided by SUVmax. Statistical methods used included Spearman’s rank correlation, Mann–Whitney U test or two-sample t test, receiver operating characteristic (ROC) curve analysis, the Kaplan–Meier method, and Cox proportional hazards models.ResultsThe median overall survival (OS) and progression-free survival (PFS) were 18 and 11.6 months, respectively. FMR was significantly positively correlated with BF (r = 0.886, p < 0.001) and negatively correlated with SUVmax (r = − 0.547, p = 0.003) and TTP (r = − 0.462, p = 0.015) in the tumors. However, there was no significant correlation between perfusion and PET parameters. After dCRT, 14 patients (51.9%) were identified as responders, and another 13 were nonresponders. The BF and FMR of the responders were significantly higher than those of the nonresponders (42.05 ± 16.47 vs 27.48 ± 8.55, p = 0.007; 3.18 ± 1.15 vs 1.84 ± 0.65, p = 0.001). The ROC curves indicated that the FMR [area under the curve (AUC) = 0.846] was a better biomarker for predicting treatment response than BF (AUC = 0.802). Univariable Cox analysis revealed that of all imaging parameters, only the FMR was significantly correlated with overall survival (OS) (p = 0.015) and progression-free survival (PFS) (p = 0.017). Specifically, patients with a lower FMR had poorer survival. Multivariable analysis showed that after adjusting for age, clinical staging, and treatment response, the FMR remained an independent predictor of OS (p = 0.026) and PFS (p = 0.014).ConclusionsThe flow-metabolism mismatch demonstrated by a low FMR shows good potential in predicting chemoradiotherapy sensitivity and prognosis in ESCC.

Highlights

  • Perfusion computed tomography (CT) can offer functional information about tumor angiogenesis, and 18F-FDG 18F-fluoroglucose glucose positron emission tomography (PET)/CT quantifies the glucose metabolic activity of tumors

  • The flow-metabolism mismatch demonstrated by a low flow-metabolism ratio (FMR) shows good potential in predicting chemoradiotherapy sensitivity and prognosis in Esophageal squamous cell carcinoma (ESCC)

  • Correlations between perfusion and metabolic parameters According to the Spearman’s correlation analysis, FMR was significantly positively correlated with blood flow (BF) (r = 0.886, p < 0.001) and negatively correlated with Maximum standardized uptake value (SUVmax) (r = − 0.547, p = 0.003) and time to peak (TTP) (r = − 0.462, p = 0.015) in the tumors

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Summary

Introduction

Perfusion CT can offer functional information about tumor angiogenesis, and 18F-FDG PET/CT quantifies the glucose metabolic activity of tumors. This prospective study aims to investigate the value of biologically relevant imaging biomarkers for predicting treatment response and survival outcomes in patients with locally advanced esophageal squamous cell cancer (LA ESCC). Definitive chemoradiotherapy (dCRT) is the standard therapy for patients with locally advanced ESCC (LA ESCC) who have unresectable tumors, advanced age, or severe heart and lung disease or who refuse surgery [2] For these patients, to choose the appropriate type and intensity of treatment for individualization, it is important to find reliable biomarkers to predict prognosis and chemoradiotherapy sensitivity. In ESCC, the data are limited [10, 11]

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