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Differential diagnosis of pancreatic cystic neoplasms through a radiomics-assisted system
Pancreatic cystic neoplasms (PCNs) are a group of heterogeneous diseases with distinct prognosis. Existing differential diagnosis methods require invasive biopsy or prolonged monitoring. We sought to develop an inexpensive, non-invasive differential diagnosis system for PCNs based on radiomics featu...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9802410/ https://www.ncbi.nlm.nih.gov/pubmed/36591475 http://dx.doi.org/10.3389/fonc.2022.941744 |
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author | Dong, Zhenglin Chen, Xiahan Cheng, Zhaorui Luo, Yuanbo He, Min Chen, Tao Zhang, Zijie Qian, Xiaohua Chen, Wei |
author_facet | Dong, Zhenglin Chen, Xiahan Cheng, Zhaorui Luo, Yuanbo He, Min Chen, Tao Zhang, Zijie Qian, Xiaohua Chen, Wei |
author_sort | Dong, Zhenglin |
collection | PubMed |
description | Pancreatic cystic neoplasms (PCNs) are a group of heterogeneous diseases with distinct prognosis. Existing differential diagnosis methods require invasive biopsy or prolonged monitoring. We sought to develop an inexpensive, non-invasive differential diagnosis system for PCNs based on radiomics features and clinical characteristics for a higher total PCN screening rate. We retrospectively analyzed computed tomography images and clinical data from 129 patients with PCN, including 47 patients with intraductal papillary mucinous neoplasms (IPMNs), 49 patients with serous cystadenomas (SCNs), and 33 patients with mucinous cystic neoplasms (MCNs). Six clinical characteristics and 944 radiomics features were tested, and nine features were finally selected for model construction using DXScore algorithm. A five-fold cross-validation algorithm and a test group were applied to verify the results. In the five-fold cross-validation section, the AUC value of our model was 0.8687, and the total accuracy rate was 74.23%, wherein the accuracy rates of IPMNs, SCNs, and MCNs were 74.26%, 78.37%, and 68.00%, respectively. In the test group, the AUC value was 0.8462 and the total accuracy rate was 73.61%. In conclusion, our research constructed an end-to-end powerful PCN differential diagnosis system based on radiomics method, which could assist decision-making in clinical practice. |
format | Online Article Text |
id | pubmed-9802410 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-98024102022-12-31 Differential diagnosis of pancreatic cystic neoplasms through a radiomics-assisted system Dong, Zhenglin Chen, Xiahan Cheng, Zhaorui Luo, Yuanbo He, Min Chen, Tao Zhang, Zijie Qian, Xiaohua Chen, Wei Front Oncol Oncology Pancreatic cystic neoplasms (PCNs) are a group of heterogeneous diseases with distinct prognosis. Existing differential diagnosis methods require invasive biopsy or prolonged monitoring. We sought to develop an inexpensive, non-invasive differential diagnosis system for PCNs based on radiomics features and clinical characteristics for a higher total PCN screening rate. We retrospectively analyzed computed tomography images and clinical data from 129 patients with PCN, including 47 patients with intraductal papillary mucinous neoplasms (IPMNs), 49 patients with serous cystadenomas (SCNs), and 33 patients with mucinous cystic neoplasms (MCNs). Six clinical characteristics and 944 radiomics features were tested, and nine features were finally selected for model construction using DXScore algorithm. A five-fold cross-validation algorithm and a test group were applied to verify the results. In the five-fold cross-validation section, the AUC value of our model was 0.8687, and the total accuracy rate was 74.23%, wherein the accuracy rates of IPMNs, SCNs, and MCNs were 74.26%, 78.37%, and 68.00%, respectively. In the test group, the AUC value was 0.8462 and the total accuracy rate was 73.61%. In conclusion, our research constructed an end-to-end powerful PCN differential diagnosis system based on radiomics method, which could assist decision-making in clinical practice. Frontiers Media S.A. 2022-12-16 /pmc/articles/PMC9802410/ /pubmed/36591475 http://dx.doi.org/10.3389/fonc.2022.941744 Text en Copyright © 2022 Dong, Chen, Cheng, Luo, He, Chen, Zhang, Qian and Chen 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 Dong, Zhenglin Chen, Xiahan Cheng, Zhaorui Luo, Yuanbo He, Min Chen, Tao Zhang, Zijie Qian, Xiaohua Chen, Wei Differential diagnosis of pancreatic cystic neoplasms through a radiomics-assisted system |
title | Differential diagnosis of pancreatic cystic neoplasms through a radiomics-assisted system |
title_full | Differential diagnosis of pancreatic cystic neoplasms through a radiomics-assisted system |
title_fullStr | Differential diagnosis of pancreatic cystic neoplasms through a radiomics-assisted system |
title_full_unstemmed | Differential diagnosis of pancreatic cystic neoplasms through a radiomics-assisted system |
title_short | Differential diagnosis of pancreatic cystic neoplasms through a radiomics-assisted system |
title_sort | differential diagnosis of pancreatic cystic neoplasms through a radiomics-assisted system |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9802410/ https://www.ncbi.nlm.nih.gov/pubmed/36591475 http://dx.doi.org/10.3389/fonc.2022.941744 |
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