Prediction of Pancreatic Neuroendocrine Tumor Grade Based on CT Features and Texture Analysis
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. Preoperative contras...
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| Vydané v: | American journal of roentgenology (1976) Ročník 210; číslo 2; s. 341 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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01.02.2018
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| ISSN: | 1546-3141, 1546-3141 |
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| Abstract | 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.
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.
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 (χ
[df, 1] = 21.6; p < 0.001); for PNETs with vascular involvement (χ
[df, 1] = 20.8; p < 0.001); and for tumors with entropy (spatial scale filter 2) values greater than 4.65 (χ
(df, 1) = 4.4; p = 0.037).
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. |
|---|---|
| AbstractList | 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.
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.
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 (χ
[df, 1] = 21.6; p < 0.001); for PNETs with vascular involvement (χ
[df, 1] = 20.8; p < 0.001); and for tumors with entropy (spatial scale filter 2) values greater than 4.65 (χ
(df, 1) = 4.4; p = 0.037).
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. 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.OBJECTIVEThe 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.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.MATERIALS AND METHODSPreoperative 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.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).RESULTSThe 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).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.CONCLUSIONCT 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. |
| Author | Parakh, Anushri Canellas, Rodrigo Burk, Kristine S Sahani, Dushyant V |
| Author_xml | – sequence: 1 givenname: Rodrigo surname: Canellas fullname: Canellas, Rodrigo organization: 1 Department of Radiology, Division of Abdominal Imaging and Interventional Radiology, Massachusetts General Hospital, White 270, 55 Fruit St, Boston, MA 02114 – sequence: 2 givenname: Kristine S surname: Burk fullname: Burk, Kristine S organization: 1 Department of Radiology, Division of Abdominal Imaging and Interventional Radiology, Massachusetts General Hospital, White 270, 55 Fruit St, Boston, MA 02114 – sequence: 3 givenname: Anushri surname: Parakh fullname: Parakh, Anushri organization: 1 Department of Radiology, Division of Abdominal Imaging and Interventional Radiology, Massachusetts General Hospital, White 270, 55 Fruit St, Boston, MA 02114 – sequence: 4 givenname: Dushyant V surname: Sahani fullname: Sahani, Dushyant V organization: 1 Department of Radiology, Division of Abdominal Imaging and Interventional Radiology, Massachusetts General Hospital, White 270, 55 Fruit St, Boston, MA 02114 |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29140113$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | Contrast Media Disease Progression Female Humans Magnetic Resonance Imaging Male Middle Aged Neoplasm Grading Neuroendocrine Tumors - diagnostic imaging Neuroendocrine Tumors - pathology Neuroendocrine Tumors - surgery Pancreatic Neoplasms - diagnostic imaging Pancreatic Neoplasms - pathology Pancreatic Neoplasms - surgery Postoperative Complications - diagnostic imaging Predictive Value of Tests Radiographic Image Interpretation, Computer-Assisted Retrospective Studies Tomography, X-Ray Computed |
| Title | Prediction of Pancreatic Neuroendocrine Tumor Grade Based on CT Features and Texture Analysis |
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