PMID- 35004272 OWN - NLM STAT- PubMed-not-MEDLINE LR - 20220429 IS - 2234-943X (Print) IS - 2234-943X (Electronic) IS - 2234-943X (Linking) VI - 11 DP - 2021 TI - Pancreatic Serous Cystic Neoplasms and Mucinous Cystic Neoplasms: Differential Diagnosis by Combining Imaging Features and Enhanced CT Texture Analysis. PG - 745001 LID - 10.3389/fonc.2021.745001 [doi] LID - 745001 AB - OBJECTIVE: To establish a diagnostic model by combining imaging features with enhanced CT texture analysis to differentiate pancreatic serous cystadenomas (SCNs) from pancreatic mucinous cystadenomas (MCNs). MATERIALS AND METHODS: Fifty-seven and 43 patients with pathology-confirmed SCNs and MCNs, respectively, from one center were analyzed and divided into a training cohort (n = 72) and an internal validation cohort (n = 28). An external validation cohort (n = 28) from another center was allocated. Demographic and radiological information were collected. The least absolute shrinkage and selection operator (LASSO) and recursive feature elimination linear support vector machine (RFE_LinearSVC) were implemented to select significant features. Multivariable logistic regression algorithms were conducted for model construction. Receiver operating characteristic (ROC) curves for the models were evaluated, and their prediction efficiency was quantified by the area under the curve (AUC), 95% confidence interval (95% CI), sensitivity and specificity. RESULTS: Following multivariable logistic regression analysis, the AUC was 0.932 and 0.887, the sensitivity was 87.5% and 90%, and the specificity was 82.4% and 84.6% with the training and validation cohorts, respectively, for the model combining radiological features and CT texture features. For the model based on radiological features alone, the AUC was 0.84 and 0.91, the sensitivity was 75% and 66.7%, and the specificity was 82.4% and 77% with the training and validation cohorts, respectively. CONCLUSION: This study showed that a logistic model combining radiological features and CT texture features is more effective in distinguishing SCNs from MCNs of the pancreas than a model based on radiological features alone. CI - Copyright (c) 2021 Chen, Deng, Pan, Chen, Liu, Chen, Yang, Zheng, Yang, Liu, Shao and Yu. FAU - Chen, Hai-Yan AU - Chen HY AD - Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China. AD - Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China. FAU - Deng, Xue-Ying AU - Deng XY AD - Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China. AD - Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China. FAU - Pan, Yao AU - Pan Y AD - Department of Radiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China. FAU - Chen, Jie-Yu AU - Chen JY AD - Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China. AD - Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China. FAU - Liu, Yun-Ying AU - Liu YY AD - Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China. AD - Department of Pathology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China. FAU - Chen, Wu-Jie AU - Chen WJ AD - Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China. AD - Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China. FAU - Yang, Hong AU - Yang H AD - Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China. AD - Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China. FAU - Zheng, Yao AU - Zheng Y AD - Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China. AD - Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China. FAU - Yang, Yong-Bo AU - Yang YB AD - Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China. AD - Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China. FAU - Liu, Cheng AU - Liu C AD - Research Institute of Artificial Intelligence in Healthcare, Hangzhou YITU Healthcare Technology Co. Ltd., Hangzhou, China. FAU - Shao, Guo-Liang AU - Shao GL AD - Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China. AD - Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China. AD - Clinical Research Center of Hepatobiliary and Pancreatic Diseases of Zhejiang Province, Hangzhou, China. FAU - Yu, Ri-Sheng AU - Yu RS AD - Department of Radiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China. LA - eng PT - Journal Article DEP - 20211223 PL - Switzerland TA - Front Oncol JT - Frontiers in oncology JID - 101568867 PMC - PMC8733460 OTO - NOTNLM OT - mucinous cystadenoma OT - pancreatic neoplasms OT - serous cystadenoma OT - texture analysis OT - tomography COIS- Author CL was employed by Hangzhou YITU Healthcare Technology Co. Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. EDAT- 2022/01/11 06:00 MHDA- 2022/01/11 06:01 PMCR- 2021/01/01 CRDT- 2022/01/10 09:23 PHST- 2021/07/21 00:00 [received] PHST- 2021/11/29 00:00 [accepted] PHST- 2022/01/10 09:23 [entrez] PHST- 2022/01/11 06:00 [pubmed] PHST- 2022/01/11 06:01 [medline] PHST- 2021/01/01 00:00 [pmc-release] AID - 10.3389/fonc.2021.745001 [doi] PST - epublish SO - Front Oncol. 2021 Dec 23;11:745001. doi: 10.3389/fonc.2021.745001. eCollection 2021.