Abstract
The aim of this work was to investigate radiomic analysis of contrast and non-contrast enhanced planning CT images of oesophageal cancer (OC) patients in terms of stability, dimensionality and contrast agent dependency. The prognostic significance of CT-based radiomic features was also evaluated. Different 2D and 3D radiomic features were extracted from contrast and non-contrast enhanced CT images of 213 patients from the multi-centre SCOPE1 randomised controlled trial (RCT) in OC. Feature stability was evaluated by randomly dividing patients into three groups and identifying textures with similar distributions among groups with a Kruskal-Wallis analysis. A paired two-sided Wilcoxon signed rank test was used to assess for significant differences in the remaining corresponding 2D and 3D stable features. A prognostic model was constructed using clinical characteristics and remaining filtered features. The discriminative ability of significant variables was tested using Kaplan-Meier analysis. A total of 238 2D and 3D radiomic features were computed from oesophageal CT images. More than 75 features were stable if extracted from homogeneous cohort (contrast or non-contrast enhanced CT images) and inhomogeneous cohort (contrast and non-contrast enhanced CT images). Among the remaining corresponding stable features computed from both cohorts, only 4 features did not show a statistically significant difference if obtained in 2D or in 3D (p-value < 0.05). A Cox regression model constructed using 5 clinical variables (age, sex, tumour, node and metastasis (TNM) stage, WHO performance status and contrast administration) and 4 radiomic variables (inverse varianceGLCM, large distance emphasisGLDZM, zone distance non uniformity normGLDZM, zone distance varianceGLDZM), identified one radiomic feature (zone distance varianceGLDZM) that was significantly associated with overall survival (p-value = 0.032, HR = 1.25, 95% CI = 1.02–1.52). A significant difference in overall survival between groups was found when considering a threshold of zone distance varianceGLDZM equals to 1.70 (X2 = 7.692, df = 1, p-value = 0.006). Zone distance varianceGLDZM was identified as the only stable CT radiomic feature statistically correlated with overall survival, independent of dimensionality and contrast administration. This feature was able to identify high-risk patients and if validated, could be the subject of a future clinical trial aiming to improve clinical decision making and personalise OC treatment.
Highlights
Oesophageal cancer (OC) is the eighth most common malignancy worldwide with a 5-year overall survival rate between 15% and 25% [1]
Zone distance varianceGLDZM was identified as the only stable CT radiomic feature statistically correlated with overall survival, independent of dimensionality and contrast administration
A total of 238 2D and 3D radiomic features were extracted from the CT images of the oesophagus
Summary
Oesophageal cancer (OC) is the eighth most common malignancy worldwide with a 5-year overall survival rate between 15% and 25% [1]. If patients are deemed to have potentially curable disease, a combination of chemotherapy, radiotherapy and surgery is used, depending on their tumour, node and metastasis (TNM) stage, physiological fitness and personal choice[2, 3]. Despite these treatment options, many patients still have a poor prognosis, suggesting that current treatment efficacy is suboptimal and clinical decision making can be improved to better select which treatment to use for each patient. Radiological staging largely informs the likely patient prognosis [4], so techniques that identify prognostic imaging biomarkers from staging investigations may improve subsequent clinical treatment decisions. Radiomics have been investigated in OC [7, 8]and could be used to inform future decision support systems [9]
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