Texture analysis of iodine-based material decomposition images with spectral CT imaging for predicting microsatellite instability status in colorectal cancer / 中国医学影像技术

ABSTRACT

Objective: To investigate the value of texture analysis of iodine-based material decomposition images with spectral CT imaging for predicting microsatellite instability (MSI) status in colorectal cancer (CRC). Methods: Data of 23 patients with MSI status CRC and 46 patients with microsatellite stability (MSS) status CRC confirmed by postoperative pathology were retrospectively analyzed. All CRC patients underwent preoperative abdominal gemstone spectral imaging. Iodine-based material decomposition images in arterial and venous phases were produced with Viewer software, and the images were imported into Omni-Kinetics software for ROI sketching and feature extraction. The texture parameters included minimum intensity, maximum intensity, mean intensity, median intensity, standard deviation, kewness, kurtosis, uniformity, energy and entropy. The differences of parameters between the two groups were compared. Logistic regression was used to combine texture parameters. Diagnostic performances of various texture parameters and the combination of multiple parameters were studied with ROC analysis. Results: Both in arterial and venous phases, the minimum, maximum, mean, median, and uniformity in MSI group were significantly lower than those in MSS group (all P0.05). In venous phase, entropy in MSI group was significantly higher than that in MSS group (t=1.81, P=0.04). In arterial phase, there was no significant difference in entropy between the two groups (t=0.22, P=0.80). ROC analysis showed that the range of AUC for predicting MSI status in CRC patients using single texture parameter as minimum, maximum, mean, median, uniformity in arterial and venous phase or entropy in venous phase was 0.64~0.82. Multi-parameter combined diagnosis Logistic regression model was -2.598-0.124×arterial phase minimum-0.039×arterial phase maximum-0.774×arterial phase median+1×arterial phase mean-1.892×arterial phase uniformity+0.14×venous phase minimum+0.2×venous phase maximum+0.343×venous phase median-0.61×venous phase mean+13.711×venous phase uniformity-2.598×venous phase entropy. When combined multiple texture parameters, the AUC was 0.83. Conclusion: Texture analysis of iodine-based material decomposition image with spectral CT can serve as a preoperative non-invasive method for predicting MSI status in CRC patients. And the optimal predictive value was observed when combined all significant texture parameters.