S Liu1, X Pan1, R Liu2, H Zheng1, L Chen3, W Guan4, H Wang4, Y Sun5, L Tang5, Y Guan6, Y Ge7, J He8, Z Zhou9. 1. Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, China. 2. Department of Radiology, Xi'an Central Hospital, Affiliated to Xi'an Jiaotong University, Xi'an, 710004, China. 3. Department of Pathology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, China. 4. Department of General Surgery, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, China. 5. Department of Radiology, Peking University Cancer Hospital & Institute, Beijing, 100142, China. 6. School of Electronic Science and Engineering, Nanjing University, Nanjing, 210046, China. 7. School of Electronic Science and Engineering, Nanjing University, Nanjing, 210046, China. Electronic address: geyunnju@126.com. 8. Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, China. Electronic address: ,hjxueren@126.com. 9. Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, China. Electronic address: zyzhou@nju.edu.cn.
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.
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.
Authors: Martijn P A Starmans; Milea J M Timbergen; Melissa Vos; Michel Renckens; Dirk J Grünhagen; Geert J L H van Leenders; Roy S Dwarkasing; François E J A Willemssen; Wiro J Niessen; Cornelis Verhoef; Stefan Sleijfer; Jacob J Visser; Stefan Klein Journal: J Digit Imaging Date: 2022-01-27 Impact factor: 4.056