OBJECTIVE: The purposes of this study were to assess whether CT texture analysis and CT features are predictive of pancreatic neuroendocrine tumor (PNET) grade based on the World Health Organization (WHO) classification and to identify features related to disease progression after surgery. MATERIALS AND METHODS: Preoperative contrast-enhanced CT images of 101 patients with PNETs were assessed. The images were evaluated for tumor location, tumor size, tumor pattern, predominantly solid or cystic composition, presence of calcification, presence of heterogeneous enhancement on contrast-enhanced images, presence of pancreatic duct dilatation, presence of pancreatic atrophy, presence of vascular involvement by the tumor, and presence of lymphadenopathy. Texture features were also extracted from CT images. Surgically verified tumors were graded according to the WHO classification, and patients underwent CT or MRI follow-up after surgical resection. Data were analyzed with chi-square tests, kappa statistics, logistic regression analysis, and Kaplan-Meier curves. RESULTS: The CT features predictive of a more aggressive tumor (grades 2 and 3) were size larger than 2.0 cm (odds ratio [OR], 3.3; p = 0.014), presence of vascular involvement (OR, 25.2; p = 0.003), presence of pancreatic ductal dilatation (OR, 6.0; p = 0.002), and presence of lymphadenopathy (OR, 6.8; p = 0.002). The texture parameter entropy (OR, 3.7; p = 0.008) was also predictive of more aggressive tumors. Differences in progression-free survival distribution were found for grade 1 versus grades 2 and 3 tumors (χ2 [df, 1] = 21.6; p < 0.001); for PNETs with vascular involvement (χ2 [df, 1] = 20.8; p < 0.001); and for tumors with entropy (spatial scale filter 2) values greater than 4.65 (χ2 (df, 1) = 4.4; p = 0.037). CONCLUSION: CT texture analysis and CT features are predictive of PNET aggressiveness and can be used to identify patients at risk of early disease progression after surgical resection.
OBJECTIVE: The purposes of this study were to assess whether CT texture analysis and CT features are predictive of pancreatic neuroendocrine tumor (PNET) grade based on the World Health Organization (WHO) classification and to identify features related to disease progression after surgery. MATERIALS AND METHODS: Preoperative contrast-enhanced CT images of 101 patients with PNETs were assessed. The images were evaluated for tumor location, tumor size, tumor pattern, predominantly solid or cystic composition, presence of calcification, presence of heterogeneous enhancement on contrast-enhanced images, presence of pancreatic duct dilatation, presence of pancreatic atrophy, presence of vascular involvement by the tumor, and presence of lymphadenopathy. Texture features were also extracted from CT images. Surgically verified tumors were graded according to the WHO classification, and patients underwent CT or MRI follow-up after surgical resection. Data were analyzed with chi-square tests, kappa statistics, logistic regression analysis, and Kaplan-Meier curves. RESULTS: The CT features predictive of a more aggressive tumor (grades 2 and 3) were size larger than 2.0 cm (odds ratio [OR], 3.3; p = 0.014), presence of vascular involvement (OR, 25.2; p = 0.003), presence of pancreatic ductal dilatation (OR, 6.0; p = 0.002), and presence of lymphadenopathy (OR, 6.8; p = 0.002). The texture parameter entropy (OR, 3.7; p = 0.008) was also predictive of more aggressive tumors. Differences in progression-free survival distribution were found for grade 1 versus grades 2 and 3 tumors (χ2 [df, 1] = 21.6; p < 0.001); for PNETs with vascular involvement (χ2 [df, 1] = 20.8; p < 0.001); and for tumors with entropy (spatial scale filter 2) values greater than 4.65 (χ2 (df, 1) = 4.4; p = 0.037). CONCLUSION: CT texture analysis and CT features are predictive of PNET aggressiveness and can be used to identify patients at risk of early disease progression after surgical resection.
Entities:
Keywords:
CT texture analysis; WHO classification; pancreatic neuroendocrine tumors
Authors: Ameya Kulkarni; Ivan Carrion-Martinez; Nan N Jiang; Srikanth Puttagunta; Leyo Ruo; Brandon M Meyers; Tariq Aziz; Christian B van der Pol Journal: Eur Radiol Date: 2020-01-17 Impact factor: 5.315