Computer-Aided Diagnosis of Pancreas Serous Cystic Neoplasms: A Radiomics Method on Preoperative MDCT Images

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.

Fully end-to-end deep-learning-based diagnosis of pancreatic tumors

“…Wei et al . presented a support vector machine system containing 24 guideline-based features and 385 radiomics high-throughput features combined with regions of interest (ROI) marked by a radiologist to diagnose pancreatic serous cystic neoplasms (SCN) 8 . With the development of deep-learning frameworks 9 , researchers have been able to construct effective deep encoder-decoder networks 10 for pancreas segmentation, boosting diagnostic accuracy 11 - 14 .…”

Artificial intelligence for the management of pancreatic diseases

“…Wei et al developed a ML-based model to differentiate between SCNs and non-SCNs based on radiomic features from preoperative CT images. 30 In the validation cohort, the model achieved an AUC of 0.84 and outperformed clinicians and guideline-based features. Yang et al published a preliminary study on a ML model that distinguishes SCN from MCN on CT, reporting a diagnostic accuracy of 83%.…”

“…Wei et al . developed a ML‐based model to differentiate between SCNs and non‐SCNs based on radiomic features from preoperative CT images 30 . In the validation cohort, the model achieved an AUC of 0.84 and outperformed clinicians and guideline‐based features.…”

Machine Learning: Applications and Advanced Progresses of Radiomics in Endocrine Neoplasms

“…Two previous CT-based radiomic studies had shown that radiomics could predict the malignant potential of IPMNs and had important application values in making a clinical decision [ 97 , 98 ]. Clinicians correctly diagnosed only 31 of 102 cases of serous cystic neoplasms, while CT-based radiomic methods achieved a sensitivity over 65% and a specificity over 70% in a recent study, which had improved diagnostic accuracy and helped clinicians making better decisions [ 99 ]. However, it would lead to misdiagnosis inevitably, which may limit the applications of radiomics in this field.…”