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Clinical Data-CT Radiomics-Based Model for Predicting Prognosis of Patients with Gastrointestinal Pancreatic Neuroendocrine Neoplasms (GP-NENs)

PURPOSE: Based on computerized tomography (CT) radiomics and clinical data, a model was established to predict the prognosis of patients with gastrointestinal pancreatic neuroendocrine neoplasms (GP-NENs). METHODS: In the data collection, the clinical imaging and survival follow-up data of 225 GP-NE...

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Autores principales: An, Peng, Zhang, Junyan, Li, Mingqun, Duan, Peng, He, Zhibing, Wang, Zhongq, Feng, Guoyan, Guo, Hongyan, Li, Xiumei, Qin, Ping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9410919/
https://www.ncbi.nlm.nih.gov/pubmed/36035279
http://dx.doi.org/10.1155/2022/4186305
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author An, Peng
Zhang, Junyan
Li, Mingqun
Duan, Peng
He, Zhibing
Wang, Zhongq
Feng, Guoyan
Guo, Hongyan
Li, Xiumei
Qin, Ping
author_facet An, Peng
Zhang, Junyan
Li, Mingqun
Duan, Peng
He, Zhibing
Wang, Zhongq
Feng, Guoyan
Guo, Hongyan
Li, Xiumei
Qin, Ping
author_sort An, Peng
collection PubMed
description PURPOSE: Based on computerized tomography (CT) radiomics and clinical data, a model was established to predict the prognosis of patients with gastrointestinal pancreatic neuroendocrine neoplasms (GP-NENs). METHODS: In the data collection, the clinical imaging and survival follow-up data of 225 GP-NENs patients admitted to Xiangyang No.1 People's Hospital and Jiangsu Province Hospital of Chinese Medicine from August 2015 to February 2021 were collected. According to the follow-up results, they were divided into the nonrecurrent group (n = 108) and the recurrent group (n = 117), based on which a training set and a test set were established at a ratio of 7/3. In the training set, a variety of models were established with significant clinical and imaging data (P < 0.05) to predict the prognosis of GP-NENs patients, and then these models were verified in the test set. RESULTS: Our newly developed combined prediction model had high predictive efficacy. Univariate analysis showed that Radscore 1/2/3, age, Ki-67 index, tumor pathological type, tumor primary site, and TNM stage were risk factors for the prognosis of GP-NENs patients (all P < 0.05). The area under the receiver operating characteristic (ROC) curves (AUC) of the combined model was significantly higher [AUC:0.824, 95% CI 0.0342 (0.751-0.883)] than that of the clinical data model [AUC:0.786, 95% CI 0.0384(0.709-0.851)] and the radiomics model [AUC:0.712, 95% CI 0.0426(0.631-0.785)]. The decision curve also confirmed that the combined model had a higher clinical net benefit. The same results were achieved in the test set. CONCLUSION: The prognosis of patients with GP-NENs is generally poor. The combined model based on clinical data and CT radiomics can help to early predict the prognosis of patients with GP-NENs, and then necessary interventions could be provided to improve the survival rate and quality of life of patients.
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spelling pubmed-94109192022-08-26 Clinical Data-CT Radiomics-Based Model for Predicting Prognosis of Patients with Gastrointestinal Pancreatic Neuroendocrine Neoplasms (GP-NENs) An, Peng Zhang, Junyan Li, Mingqun Duan, Peng He, Zhibing Wang, Zhongq Feng, Guoyan Guo, Hongyan Li, Xiumei Qin, Ping Comput Math Methods Med Research Article PURPOSE: Based on computerized tomography (CT) radiomics and clinical data, a model was established to predict the prognosis of patients with gastrointestinal pancreatic neuroendocrine neoplasms (GP-NENs). METHODS: In the data collection, the clinical imaging and survival follow-up data of 225 GP-NENs patients admitted to Xiangyang No.1 People's Hospital and Jiangsu Province Hospital of Chinese Medicine from August 2015 to February 2021 were collected. According to the follow-up results, they were divided into the nonrecurrent group (n = 108) and the recurrent group (n = 117), based on which a training set and a test set were established at a ratio of 7/3. In the training set, a variety of models were established with significant clinical and imaging data (P < 0.05) to predict the prognosis of GP-NENs patients, and then these models were verified in the test set. RESULTS: Our newly developed combined prediction model had high predictive efficacy. Univariate analysis showed that Radscore 1/2/3, age, Ki-67 index, tumor pathological type, tumor primary site, and TNM stage were risk factors for the prognosis of GP-NENs patients (all P < 0.05). The area under the receiver operating characteristic (ROC) curves (AUC) of the combined model was significantly higher [AUC:0.824, 95% CI 0.0342 (0.751-0.883)] than that of the clinical data model [AUC:0.786, 95% CI 0.0384(0.709-0.851)] and the radiomics model [AUC:0.712, 95% CI 0.0426(0.631-0.785)]. The decision curve also confirmed that the combined model had a higher clinical net benefit. The same results were achieved in the test set. CONCLUSION: The prognosis of patients with GP-NENs is generally poor. The combined model based on clinical data and CT radiomics can help to early predict the prognosis of patients with GP-NENs, and then necessary interventions could be provided to improve the survival rate and quality of life of patients. Hindawi 2022-08-05 /pmc/articles/PMC9410919/ /pubmed/36035279 http://dx.doi.org/10.1155/2022/4186305 Text en Copyright © 2022 Peng An et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
An, Peng
Zhang, Junyan
Li, Mingqun
Duan, Peng
He, Zhibing
Wang, Zhongq
Feng, Guoyan
Guo, Hongyan
Li, Xiumei
Qin, Ping
Clinical Data-CT Radiomics-Based Model for Predicting Prognosis of Patients with Gastrointestinal Pancreatic Neuroendocrine Neoplasms (GP-NENs)
title Clinical Data-CT Radiomics-Based Model for Predicting Prognosis of Patients with Gastrointestinal Pancreatic Neuroendocrine Neoplasms (GP-NENs)
title_full Clinical Data-CT Radiomics-Based Model for Predicting Prognosis of Patients with Gastrointestinal Pancreatic Neuroendocrine Neoplasms (GP-NENs)
title_fullStr Clinical Data-CT Radiomics-Based Model for Predicting Prognosis of Patients with Gastrointestinal Pancreatic Neuroendocrine Neoplasms (GP-NENs)
title_full_unstemmed Clinical Data-CT Radiomics-Based Model for Predicting Prognosis of Patients with Gastrointestinal Pancreatic Neuroendocrine Neoplasms (GP-NENs)
title_short Clinical Data-CT Radiomics-Based Model for Predicting Prognosis of Patients with Gastrointestinal Pancreatic Neuroendocrine Neoplasms (GP-NENs)
title_sort clinical data-ct radiomics-based model for predicting prognosis of patients with gastrointestinal pancreatic neuroendocrine neoplasms (gp-nens)
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9410919/
https://www.ncbi.nlm.nih.gov/pubmed/36035279
http://dx.doi.org/10.1155/2022/4186305
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