Texture analysis of CT images in predicting malignancy risk of gastrointestinal stromal tumours. Issue 3 (March 2018)

• Record Type: Journal Article
• Authors: Liu, S.; Pan, X.; Liu, R.; Zheng, H.; Chen, L.; Guan, W.; Wang, H.; Sun, Y.; Tang, L.; Guan, Y.; Ge, Y.; He, J.; Zhou, Z.
• Is Part Of: Clinical radiology. Volume 73:Issue 3(2018)
• Page Start: 266
• Page End: 274
• Publication Date: 2018-03
• Subjects: Medical radiology -- Periodicals; Radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiology -- Periodicals; Societies, Medical -- Periodicals; Medical radiology; Radiotherapy; Electronic journals; Periodicals; 616.0757
• DOI: 10.1016/j.crad.2017.09.003
• Languages: English
• ISSNs: 0009-9260

Abstract

Abstract : Aim: To explore the role of texture analysis of computed tomography (CT) images in predicting the malignancy risk of gastrointestinal stromal tumours (GISTs). Materials and methods: Seventy-eight patients with histopathologically confirmed GISTs underwent preoperative CT. Texture analysis was performed on unenhanced and contrast-enhanced CT images, respectively. Fourteen CT texture parameters were obtained and compared among GISTs at different malignancy risks with one-way analysis of variance or independent-samples Kruskal–Wallis test. Correlations between CT texture parameters and malignancy risk were analysed with Spearman's correlation test. Diagnostic performance of CT texture parameters in differentiating GISTs at low/very low malignancy risk was tested with receiver operating characteristic (ROC) analysis. Results: Three parameters on unenhanced images ( r= –0.268–0.506), four parameters on arterial phase ( r= –0.365–0.508), and six parameters on venous phase ( r= –0.343–0.481) imaging correlated significantly with malignancy risk of GISTs, respectively (all p< 0.05). For identifying GISTs at low/very low malignancy risk, three parameters on unenhanced images (area under ROC curve [AUC], 0.676–0.802), four parameters on arterial phase (AUC, 0.637–0.811), and six parameters on venous phase (AUC, 0.636–0.791) imaging showed significant diagnostic performance, respectively (all p< 0.05), especially maximum frequency on both unenhanced and contrast-enhanced images (AUC, 0.791–0.811). Conclusion: Texture analysis of CT images holds great potential to predict the malignancy risk of GISTs preoperatively. Highlights: This is the first application of CT texture analysis in gastrointestinal stromal tumours. CT histogram analysis can quantitavely assess different features of gastrointestinal stromal tumours. Texture analysis of CT images holds great potential in predicting the malignancy risks of gastrointestinal stromal tumours preoperatively.