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Is the radiomics-clinical combined model helpful in distinguishing between pancreatic cancer and mass-forming pancreatitis?

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单位： [1]Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China [2]Biomedical Engineering Department, College of Life Sciences and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China [3]Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China [4]Musketeers Foundation Institute of Data Science, The University of Hong Kong, Hong Kong Special Administrative Region [5]The Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region [6]Johns Hopkins Hospital, Russell H Morgan Department of Radiology & Radiological Science, 600 N Wolfe St, Baltimore, MD 21205, USA

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关键词： Pancreatic cancer Pancreatitis chronic Multidetector computed tomography Biopsy Fine needle

摘要：

To develop CT-based radiomics models for distinguishing between resectable PDAC and mass-forming pancreatitis (MFP) and to provide a non-invasive tool for cases of equivocal imaging findings with EUS-FNA needed.A total of 201 patients with resectable PDAC and 54 patients with MFP were included. Development cohort: patients without preoperative EUS-FNA (175 PDAC cases, 38 MFP cases); validation cohort: patients with EUS-FNA (26 PDAC cases, 16 MFP cases). Two radiomic signatures (LASSOscore, PCAscore) were developed based on the LASSO model and principal component analysis. LASSOCli and PCACli prediction models were established by combining clinical features with CT radiomic features. ROC analysis and decision curve analysis (DCA) were performed to evaluate the utility of the model versus EUS-FNA in the validation cohort.In the validation cohort, the radiomic signatures (LASSOscore, PCAscore) were both effective in distinguishing between resectable PDAC and MFP (AUCLASSO = 0.743, 95% CI: 0.590-0.896; AUCPCA = 0.788, 95% CI: 0.639-0.938) and improved the diagnostic accuracy of the baseline onlyCli model (AUConlyCli = 0.760, 95% CI: 0.614-0.960) after combination with variables including age, CA19-9, and the double-duct sign (AUCPCACli = 0.880, 95% CI: 0.776-0.983; AUCLASSOCli = 0.825, 95% CI: 0.694-0.955). The PCACli model showed comparable performance to FNA (AUCFNA = 0.810, 95% CI: 0.685-0.935). In DCA, the net benefit of the PCACli model was superior to that of EUS-FNA, avoiding biopsies in 70 per 1000 patients at a risk threshold of 35%.The PCACli model showed comparable performance with EUS-FNA in discriminating resectable PDAC from MFP.Copyright © 2023 Elsevier B.V. All rights reserved.

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出版当年[2022]版：

大类 | 3 区医学

小类 | 3 区核医学

最新[2025]版：

大类 | 2 区医学

小类 | 2 区核医学

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出版当年[2021]版：

Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

最新[2023]版：

Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

影响因子： 3.2 最新[2023版] 3.4 最新五年平均 4.531 出版当年[2021版] 4.218 出版当年五年平均 3.528 出版前一年[2020版] 3.3 出版后一年[2022版]

第一作者：

第一作者单位： [1]Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China

通讯作者：

通讯机构： [1]Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China [*1]Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Qiaokou District Wuhan, 430030, Hubei, China.

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Is the radiomics-clinical combined model helpful in distinguishing between pancreatic cancer and mass-forming pancreatitis?

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