Abstract
BackgroundTextural features extracted from MRI potentially provide prognostic information additional to volume for influencing surgical management of cervical cancer. PurposeTo identify textural features that differ between cervical tumors above and below the volume threshold of eligibility for trachelectomy and determine their value in predicting recurrence in patients with low-volume tumors. MethodsOf 378 patients with Stage1–2 cervical cancer imaged prospectively (3T, endovaginal coil), 125 had well-defined, histologically-confirmed squamous or adenocarcinomas with >100 voxels (>0.07 cm3) suitable for radiomic analysis. Regions-of-interest outlined the whole tumor on T2-W images and apparent diffusion coefficient (ADC) maps. Textural features based on grey-level co-occurrence matrices were compared (Mann-Whitney test with Bonferroni correction) between tumors greater (n = 46) or less (n = 79) than 4.19 cm3. Clustering eliminated correlated variables. Significantly different features were used to predict recurrence (regression modelling) in surgically-treated patients with low-volume tumors and compared with a model using clinico-pathological features. ResultsTextural features (Dissimilarity, Energy, ClusterProminence, ClusterShade, InverseVariance, Autocorrelation) in 6 of 10 clusters from T2-W and ADC data differed between high-volume (mean ± SD 15.3 ± 11.7 cm3) and low-volume (mean ± SD 1.3 ± 1.2 cm3) tumors. (p < 0.02). In low-volume tumors, predicting recurrence was indicated by: Dissimilarity, Energy (ADC-radiomics, AUC = 0.864); Dissimilarity, ClusterProminence, InverseVariance (T2-W-radiomics, AUC = 0.808); Volume, Depth of Invasion, LymphoVascular Space Invasion (clinico-pathological features, AUC = 0.794). Combining ADC-radiomic (but not T2-radiomic) and clinico-pathological features improved prediction of recurrence compared to the clinico-pathological model (AUC = 0.916, p = 0.006). Findings were supported by bootstrap re-sampling (n = 1000). ConclusionTextural features from ADC maps and T2-W images differ between high- and low-volume tumors and potentially predict recurrence in low-volume tumors.
Highlights
Patients in the high- and low–volume tumor groups were well matched by age, and the low-volume tumors were by definition lower stage, there were more adenocarcinomas and lymphovascular space invasion (LVSI) in this group, both of which adversely affect outcome
Radiomic differences between high and low-volume tumors were largely similar for both the apparent diffusion coefficient (ADC) and T2-W data regression models identified different combinations of features as being contributory to prediction of recurrence in each case
Radiomic features differed between tumors with and without LVSI, they did not differ between other histological parameters of poor prognosis, indicating that they are likely to be independent
Summary
Different features were used to predict recurrence (regression modelling) in surgically-treated patients with low-volume tumors and compared with a model using clinico-pathological features. Results: Textural features (Dissimilarity, Energy, ClusterProminence, ClusterShade, InverseVariance, Autocorrelation) in 6 of 10 clusters from T2-W and ADC data differed between high-volume (mean ± SD 15.3 ± 11.7 cm3) and low-volume (mean ± SD 1.3 ± 1.2 cm3) tumors. In low-volume tumors, predicting recurrence was indicated by: Dissimilarity, Energy (ADC-radiomics, AUC 1⁄4 0.864); Dissimilarity, ClusterProminence, InverseVariance (T2-W-radiomics, AUC 1⁄4 0.808); Volume, Depth of Invasion, LymphoVascular Space Invasion (clinico-pathological features, AUC 1⁄4 0.794). Conclusion: Textural features from ADC maps and T2-W images differ between high- and low-volume tumors and potentially predict recurrence in low-volume tumors
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