Pancreatic Ductal Adenocarcinoma at CT: A Combined Nomogram Model to Preoperatively Predict Cancer Stage and Survival Outcome

OBJECTIVES: To construct a nomogram model that combines clinical characteristics and radiomics signatures to preoperatively discriminate pancreatic ductal adenocarcinoma (PDAC) in stage I-II and III-IV and predict overall survival. METHODS: A total of 135 patients with histopathologically confirmed...

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Autores principales: Cen, Chunyuan, Liu, Liying, Li, Xin, Wu, Ailan, Liu, Huan, Wang, Xinrong, Wu, Heshui, Wang, Chunyou, Han, Ping, Wang, Siqi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8183382/
https://www.ncbi.nlm.nih.gov/pubmed/34109107
http://dx.doi.org/10.3389/fonc.2021.594510
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author Cen, Chunyuan
Liu, Liying
Li, Xin
Wu, Ailan
Liu, Huan
Wang, Xinrong
Wu, Heshui
Wang, Chunyou
Han, Ping
Wang, Siqi
author_facet Cen, Chunyuan
Liu, Liying
Li, Xin
Wu, Ailan
Liu, Huan
Wang, Xinrong
Wu, Heshui
Wang, Chunyou
Han, Ping
Wang, Siqi
author_sort Cen, Chunyuan
collection PubMed
description OBJECTIVES: To construct a nomogram model that combines clinical characteristics and radiomics signatures to preoperatively discriminate pancreatic ductal adenocarcinoma (PDAC) in stage I-II and III-IV and predict overall survival. METHODS: A total of 135 patients with histopathologically confirmed PDAC who underwent contrast-enhanced CT were included. A total of 384 radiomics features were extracted from arterial phase (AP) or portal venous phase (PVP) images. Four steps were used for feature selection, and multivariable logistic regression analysis were used to build radiomics signatures and combined nomogram model. Performance of the proposed model was assessed by using receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA). Kaplan-Meier analysis was applied to analyze overall survival in the stage I-II and III-IV PDAC groups. RESULTS: The AP+PVP radiomics signature showed the best performance among the three radiomics signatures [training cohort: area under the curve (AUC) = 0.919; validation cohort: AUC = 0.831]. The combined nomogram model integrating AP+PVP radiomics signature with clinical characteristics (tumor location, carcinoembryonic antigen level, and tumor maximum diameter) demonstrated the best discrimination performance (training cohort: AUC = 0.940; validation cohort: AUC = 0.912). Calibration curves and DCA verified the clinical usefulness of the combined nomogram model. Kaplan-Meier analysis showed that overall survival of patients in the predicted stage I-II PDAC group was longer than patients in stage III-IV PDAC group (p<0.0001). CONCLUSIONS: We propose a combined model with excellent performance for the preoperative, individualized, noninvasive discrimination of stage I-II and III-IV PDAC and prediction of overall survival.
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spelling pubmed-81833822021-06-08 Pancreatic Ductal Adenocarcinoma at CT: A Combined Nomogram Model to Preoperatively Predict Cancer Stage and Survival Outcome Cen, Chunyuan Liu, Liying Li, Xin Wu, Ailan Liu, Huan Wang, Xinrong Wu, Heshui Wang, Chunyou Han, Ping Wang, Siqi Front Oncol Oncology OBJECTIVES: To construct a nomogram model that combines clinical characteristics and radiomics signatures to preoperatively discriminate pancreatic ductal adenocarcinoma (PDAC) in stage I-II and III-IV and predict overall survival. METHODS: A total of 135 patients with histopathologically confirmed PDAC who underwent contrast-enhanced CT were included. A total of 384 radiomics features were extracted from arterial phase (AP) or portal venous phase (PVP) images. Four steps were used for feature selection, and multivariable logistic regression analysis were used to build radiomics signatures and combined nomogram model. Performance of the proposed model was assessed by using receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA). Kaplan-Meier analysis was applied to analyze overall survival in the stage I-II and III-IV PDAC groups. RESULTS: The AP+PVP radiomics signature showed the best performance among the three radiomics signatures [training cohort: area under the curve (AUC) = 0.919; validation cohort: AUC = 0.831]. The combined nomogram model integrating AP+PVP radiomics signature with clinical characteristics (tumor location, carcinoembryonic antigen level, and tumor maximum diameter) demonstrated the best discrimination performance (training cohort: AUC = 0.940; validation cohort: AUC = 0.912). Calibration curves and DCA verified the clinical usefulness of the combined nomogram model. Kaplan-Meier analysis showed that overall survival of patients in the predicted stage I-II PDAC group was longer than patients in stage III-IV PDAC group (p<0.0001). CONCLUSIONS: We propose a combined model with excellent performance for the preoperative, individualized, noninvasive discrimination of stage I-II and III-IV PDAC and prediction of overall survival. Frontiers Media S.A. 2021-05-24 /pmc/articles/PMC8183382/ /pubmed/34109107 http://dx.doi.org/10.3389/fonc.2021.594510 Text en Copyright © 2021 Cen, Liu, Li, Wu, Liu, Wang, Wu, Wang, Han and Wang 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
Cen, Chunyuan
Liu, Liying
Li, Xin
Wu, Ailan
Liu, Huan
Wang, Xinrong
Wu, Heshui
Wang, Chunyou
Han, Ping
Wang, Siqi
Pancreatic Ductal Adenocarcinoma at CT: A Combined Nomogram Model to Preoperatively Predict Cancer Stage and Survival Outcome
title Pancreatic Ductal Adenocarcinoma at CT: A Combined Nomogram Model to Preoperatively Predict Cancer Stage and Survival Outcome
title_full Pancreatic Ductal Adenocarcinoma at CT: A Combined Nomogram Model to Preoperatively Predict Cancer Stage and Survival Outcome
title_fullStr Pancreatic Ductal Adenocarcinoma at CT: A Combined Nomogram Model to Preoperatively Predict Cancer Stage and Survival Outcome
title_full_unstemmed Pancreatic Ductal Adenocarcinoma at CT: A Combined Nomogram Model to Preoperatively Predict Cancer Stage and Survival Outcome
title_short Pancreatic Ductal Adenocarcinoma at CT: A Combined Nomogram Model to Preoperatively Predict Cancer Stage and Survival Outcome
title_sort pancreatic ductal adenocarcinoma at ct: a combined nomogram model to preoperatively predict cancer stage and survival outcome
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8183382/
https://www.ncbi.nlm.nih.gov/pubmed/34109107
http://dx.doi.org/10.3389/fonc.2021.594510
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