Computer-Aided Diagnosis of Pancreas Serous Cystic Neoplasms: A Radiomics Method on Preoperative MDCT Images. (19th July 2019)

• Record Type: Journal Article
• Authors: Wei, Ran; Lin, Kanru; Yan, Wenjun; Guo, Yi; Wang, Yuanyuan; Li, Ji; Zhu, Jianqing
• Is Part Of: Technology in cancer research & treatment. Volume 18(2019)
• Publication Date: 2019-07-19
• Subjects: pancreatic cancer -- pancreatic serous cystic neoplasms -- computer-aided diagnosis -- radiomics -- MDCT image; Oncology -- Periodicals; Cancer -- Diagnosis -- Periodicals; Cancer -- Treatment -- Technological innovations -- Periodicals; 616.994
• DOI: 10.1177/1533033818824339
• Languages: English
• ISSNs: 1533-0346

Abstract

Objective: Our aim was to propose a preoperative computer-aided diagnosis scheme to differentiate pancreatic serous cystic neoplasms from other pancreatic cystic neoplasms, providing supportive opinions for clinicians and avoiding overtreatment. Materials and Methods: In this retrospective study, 260 patients with pancreatic cystic neoplasm were included. Each patient underwent a multidetector row computed tomography scan and pancreatic resection. In all, 200 patients constituted a cross-validation cohort, and 60 patients formed an independent validation cohort. Demographic information, clinical information, and multidetector row computed tomography images were obtained from Picture Archiving and Communication Systems. The peripheral margin of each neoplasm was manually outlined by experienced radiologists. A radiomics system containing 24 guideline-based features and 385 radiomics high-throughput features was designed. After the feature extraction, least absolute shrinkage selection operator regression was used to select the most important features. A support vector machine classifier with 5-fold cross-validation was applied to build the diagnostic model. The independent validation cohort was used to validate the performance. Results: Only 31 of 102 serous cystic neoplasm cases in this study were recognized correctly by clinicians before the surgery. Twenty-two features were selected from the radiomics system after 100 bootstrapping repetitions of the least absolute shrinkage selection operator regression. The diagnostic scheme performed accurately and robustly, showing the area under the receiver operating characteristic curve = 0.767, sensitivity = 0.686, and specificity = 0.709. In the independent validation cohort, we acquired similar results with receiver operating characteristic curve = 0.837, sensitivity = 0.667, and specificity = 0.818. Conclusion: The proposed radiomics-based computer-aided diagnosis scheme could increase preoperative diagnostic accuracy and assist clinicians in making accurate management decisions.