Cargando…

The value of enhanced CT features and texture-signatures in assessing the inflammatory infiltration of pancreatic ductal adenocarcinoma

PURPOSE: To explore the predictive value of computed tomography (CT) imaging features and CT-based texture analysis in assessing inflammatory infiltration in pancreatic ductal adenocarcinoma (PDAC). METHODS: A total of 43 patients with PDAC confirmed by surgical pathology were included in the study....

Descripción completa

Detalles Bibliográficos
Autores principales: Wang, Fangqing, Guo, Hang, Li, Shunjia, Xu, Jianwei, Yu, Dexin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9936180/
https://www.ncbi.nlm.nih.gov/pubmed/36816950
http://dx.doi.org/10.3389/fonc.2023.1078861
_version_ 1784890180777279488
author Wang, Fangqing
Guo, Hang
Li, Shunjia
Xu, Jianwei
Yu, Dexin
author_facet Wang, Fangqing
Guo, Hang
Li, Shunjia
Xu, Jianwei
Yu, Dexin
author_sort Wang, Fangqing
collection PubMed
description PURPOSE: To explore the predictive value of computed tomography (CT) imaging features and CT-based texture analysis in assessing inflammatory infiltration in pancreatic ductal adenocarcinoma (PDAC). METHODS: A total of 43 patients with PDAC confirmed by surgical pathology were included in the study. The clinical, radiological, surgical, and pathological features of the patients were analyzed retrospectively using the chi-square test or Spearman’s correlation. Receiver operating characteristic (ROC) curves were utilized to assess the overall predictive ability of the tumor enhancement degree on triphasic contrast-enhanced CT images for the inflammatory infiltration degree in PDAC. Furthermore, all CT data were uploaded to the RadCloud platform for region of interest (ROI) delineation and feature extraction. Then, the Variance Threshold and SelectKBest algorithms were used to find the optimal CT features. Binary logistic regression was employed to analyze the selected features in all three contrast-enhanced CT phases, and regression equations were formulated. ROC analysis was performed to evaluate the predictive effectiveness of each equation. RESULTS: The analysis revealed a statistically significant correlation between the degree of differentiation and radiological findings such as necrosis and cystic degeneration, vascular invasion, and the presence of ascites (P < 0.05). The enhancement degree of the tumor in both the arterial and venous phases was significantly correlated with the inflammatory infiltration degree (P < 0.05); however, the areas under the ROC curve (AUCs) of arterial and venous enhancement were 0.570 and 0.542, respectively. Regression equations based on the texture features of triphasic contrast-enhanced tumors were formulated, and their AUCs were 0.982, 0.643, and 0.849. CONCLUSION: Conventional radiological features are not significantly correlated with the degree of inflammatory infiltration in PDAC. The enhancement degrees in both the arterial phase and venous phase were statistically correlated with the inflammatory infiltration level but had poor predictive value. The texture features of PDAC on contrast-enhanced CT may show a better assessment value, especially in the arterial phase.
format Online
Article
Text
id pubmed-9936180
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-99361802023-02-18 The value of enhanced CT features and texture-signatures in assessing the inflammatory infiltration of pancreatic ductal adenocarcinoma Wang, Fangqing Guo, Hang Li, Shunjia Xu, Jianwei Yu, Dexin Front Oncol Oncology PURPOSE: To explore the predictive value of computed tomography (CT) imaging features and CT-based texture analysis in assessing inflammatory infiltration in pancreatic ductal adenocarcinoma (PDAC). METHODS: A total of 43 patients with PDAC confirmed by surgical pathology were included in the study. The clinical, radiological, surgical, and pathological features of the patients were analyzed retrospectively using the chi-square test or Spearman’s correlation. Receiver operating characteristic (ROC) curves were utilized to assess the overall predictive ability of the tumor enhancement degree on triphasic contrast-enhanced CT images for the inflammatory infiltration degree in PDAC. Furthermore, all CT data were uploaded to the RadCloud platform for region of interest (ROI) delineation and feature extraction. Then, the Variance Threshold and SelectKBest algorithms were used to find the optimal CT features. Binary logistic regression was employed to analyze the selected features in all three contrast-enhanced CT phases, and regression equations were formulated. ROC analysis was performed to evaluate the predictive effectiveness of each equation. RESULTS: The analysis revealed a statistically significant correlation between the degree of differentiation and radiological findings such as necrosis and cystic degeneration, vascular invasion, and the presence of ascites (P < 0.05). The enhancement degree of the tumor in both the arterial and venous phases was significantly correlated with the inflammatory infiltration degree (P < 0.05); however, the areas under the ROC curve (AUCs) of arterial and venous enhancement were 0.570 and 0.542, respectively. Regression equations based on the texture features of triphasic contrast-enhanced tumors were formulated, and their AUCs were 0.982, 0.643, and 0.849. CONCLUSION: Conventional radiological features are not significantly correlated with the degree of inflammatory infiltration in PDAC. The enhancement degrees in both the arterial phase and venous phase were statistically correlated with the inflammatory infiltration level but had poor predictive value. The texture features of PDAC on contrast-enhanced CT may show a better assessment value, especially in the arterial phase. Frontiers Media S.A. 2023-02-03 /pmc/articles/PMC9936180/ /pubmed/36816950 http://dx.doi.org/10.3389/fonc.2023.1078861 Text en Copyright © 2023 Wang, Guo, Li, Xu and Yu https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Wang, Fangqing
Guo, Hang
Li, Shunjia
Xu, Jianwei
Yu, Dexin
The value of enhanced CT features and texture-signatures in assessing the inflammatory infiltration of pancreatic ductal adenocarcinoma
title The value of enhanced CT features and texture-signatures in assessing the inflammatory infiltration of pancreatic ductal adenocarcinoma
title_full The value of enhanced CT features and texture-signatures in assessing the inflammatory infiltration of pancreatic ductal adenocarcinoma
title_fullStr The value of enhanced CT features and texture-signatures in assessing the inflammatory infiltration of pancreatic ductal adenocarcinoma
title_full_unstemmed The value of enhanced CT features and texture-signatures in assessing the inflammatory infiltration of pancreatic ductal adenocarcinoma
title_short The value of enhanced CT features and texture-signatures in assessing the inflammatory infiltration of pancreatic ductal adenocarcinoma
title_sort value of enhanced ct features and texture-signatures in assessing the inflammatory infiltration of pancreatic ductal adenocarcinoma
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9936180/
https://www.ncbi.nlm.nih.gov/pubmed/36816950
http://dx.doi.org/10.3389/fonc.2023.1078861
work_keys_str_mv AT wangfangqing thevalueofenhancedctfeaturesandtexturesignaturesinassessingtheinflammatoryinfiltrationofpancreaticductaladenocarcinoma
AT guohang thevalueofenhancedctfeaturesandtexturesignaturesinassessingtheinflammatoryinfiltrationofpancreaticductaladenocarcinoma
AT lishunjia thevalueofenhancedctfeaturesandtexturesignaturesinassessingtheinflammatoryinfiltrationofpancreaticductaladenocarcinoma
AT xujianwei thevalueofenhancedctfeaturesandtexturesignaturesinassessingtheinflammatoryinfiltrationofpancreaticductaladenocarcinoma
AT yudexin thevalueofenhancedctfeaturesandtexturesignaturesinassessingtheinflammatoryinfiltrationofpancreaticductaladenocarcinoma
AT wangfangqing valueofenhancedctfeaturesandtexturesignaturesinassessingtheinflammatoryinfiltrationofpancreaticductaladenocarcinoma
AT guohang valueofenhancedctfeaturesandtexturesignaturesinassessingtheinflammatoryinfiltrationofpancreaticductaladenocarcinoma
AT lishunjia valueofenhancedctfeaturesandtexturesignaturesinassessingtheinflammatoryinfiltrationofpancreaticductaladenocarcinoma
AT xujianwei valueofenhancedctfeaturesandtexturesignaturesinassessingtheinflammatoryinfiltrationofpancreaticductaladenocarcinoma
AT yudexin valueofenhancedctfeaturesandtexturesignaturesinassessingtheinflammatoryinfiltrationofpancreaticductaladenocarcinoma