Chae Hong Lim,Yong Jin Park,Muheon Shin,Young Seok Cho,Joon Young Choi,Kyung-Han Lee,Seung Hyup Hyun
出处
期刊:Clinical Nuclear Medicine [Ovid Technologies (Wolters Kluwer)] 日期:2019-01-30卷期号:45 (3): e128-e133被引量:15
标识
DOI:10.1097/rlu.0000000000002926
摘要
Purpose Considerable discrepancies are observed between clinical staging and pathological staging after surgical resection in patients with esophageal squamous cell carcinoma (ESCC). In this study, we examined the relationships between tumor SUVs on FDG PET/CT and aggressive pathological features in resected ESCC patients. Methods A total of 220 patients with surgically resected clinical stage I–II ESCC without neoadjuvant treatment were retrospectively analyzed. SUVmax of the primary tumor was measured on pretreatment FDG PET/CT. Pathological features included depth of tumor invasion, lymph node metastasis, tumor differentiation, lymphatic vessel tumor embolus, perineural invasion, Ki-67 index, and p53 protein expression. Receiver operating characteristic curve analysis was used to determine an optimal cutoff of SUVmax to predict pathologically advanced disease. Differences in pathological features associated with SUVmax were examined by t test or χ 2 test. Results The number of patients upstaged from clinical stage I–II to pathological stage III–IV was 43 (19.5%). Receiver operating characteristic curve analysis showed that the optimal cutoff SUVmax of 4.0 had good performance for predicting locally advanced disease (area under the receiver operating characteristic curve = 0.844, P < 0.001). Higher tumor SUVmax was significantly associated with advanced depth of tumor invasion (deeper than submucosa, P < 0.001), positive lymph node metastasis ( P < 0.001), presence of lymphatic vessel tumor embolus ( P < 0.001), presence of perineural invasion ( P < 0.001), higher Ki-67 index ( P = 0.025), and poor tumor differentiation ( P = 0.039). Conclusions SUVmax measured on pretreatment FDG PET/CT is significantly associated with aggressive pathological features and may help clinicians identify patients at risk of advanced disease.