Ming Liu1, Liheng Liu2, Erhu Jin3. 1. Department of Radiotherapy, Fudan University Shanghai Cancer Center, Shanghai, China. 2. Department of Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China. liu.liheng@zs-hospital.sh.cn. 3. Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
Abstract
OBJECTIVES: To determine CT features that can identify gastrointestinal stromal tumors (GISTs) among gastric sub-epithelial tumors (SETs) and to explore a practical scoring method. METHODS: Sixty-four patients with gastric SETs (51 GISTs and 13 non-GISTs) from hospital I were included for primary analyses, and 92 (67 GISTs and 25 non-GISTs) from hospital II constituted a validation cohort. Pre-operative CT images were reviewed for imaging features: lesion location, growth pattern, lesion margin, enhancement pattern, dynamic pattern, attenuation at each phasic images and presence of necrosis, superficial ulcer, calcification, and peri-lesion enlarged lymph node (LN). Clinical and CT features were compared between the two groups (GISTs versus non-GISTs) and a GIST-risk scoring method was developed; then, its performance for identifying GISTs was tested in the validation cohort. RESULTS: Seven clinical and CT features were significantly suggestive of GISTs rather than non-GISTs: older age (> 49 years), non-cardial location, irregular margin, lower attenuation on unenhanced images (≤ 43 HU), heterogeneous enhancement, necrosis, and absence of enlarged LN (p < 0.05). At validation step, the established scoring method with cut-off score dichotomized into ≥ 4 versus < 4 for identifying GISTs revealed an AUC of 0.97 with an accuracy of 92%, a sensitivity of 100% and a negative predictive value (NPV) of 100%. CONCLUSIONS: Gastric GISTs have special CT and clinical features that differ from non-GISTs. With a simple and practical scoring method based on the significant features, GISTs can be accurately differentiated from non-GISTs.
OBJECTIVES: To determine CT features that can identify gastrointestinal stromal tumors (GISTs) among gastric sub-epithelial tumors (SETs) and to explore a practical scoring method. METHODS: Sixty-four patients with gastric SETs (51 GISTs and 13 non-GISTs) from hospital I were included for primary analyses, and 92 (67 GISTs and 25 non-GISTs) from hospital II constituted a validation cohort. Pre-operative CT images were reviewed for imaging features: lesion location, growth pattern, lesion margin, enhancement pattern, dynamic pattern, attenuation at each phasic images and presence of necrosis, superficial ulcer, calcification, and peri-lesion enlarged lymph node (LN). Clinical and CT features were compared between the two groups (GISTs versus non-GISTs) and a GIST-risk scoring method was developed; then, its performance for identifying GISTs was tested in the validation cohort. RESULTS: Seven clinical and CT features were significantly suggestive of GISTs rather than non-GISTs: older age (> 49 years), non-cardial location, irregular margin, lower attenuation on unenhanced images (≤ 43 HU), heterogeneous enhancement, necrosis, and absence of enlarged LN (p < 0.05). At validation step, the established scoring method with cut-off score dichotomized into ≥ 4 versus < 4 for identifying GISTs revealed an AUC of 0.97 with an accuracy of 92%, a sensitivity of 100% and a negative predictive value (NPV) of 100%. CONCLUSIONS: Gastric GISTs have special CT and clinical features that differ from non-GISTs. With a simple and practical scoring method based on the significant features, GISTs can be accurately differentiated from non-GISTs.
Authors: Grace L Ma; James D Murphy; Maria E Martinez; Jason K Sicklick Journal: Cancer Epidemiol Biomarkers Prev Date: 2014-10-02 Impact factor: 4.254
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