Radiomics advances in the evaluation of pancreatic cystic neoplasms

Abstract

With the development of medical imaging, the detection rate of pancreatic cystic neoplasms (PCNs) has increased greatly. Serous cystic neoplasm, solid pseudopapillary neoplasm, intraductal papillary mucinous neoplasm and mucinous cystic neoplasm are the main subtypes of PCN, and their treatment options vary greatly due to the different biological behaviours of the tumours. Different from conventional qualitative imaging evaluation, radiomics is a promising noninvasive approach for the diagnosis, classification, and risk stratification of diseases involving high-throughput extraction of medical image features. We present a review of radiomics in the diagnosis of serous cystic neoplasm and mucinous cystic neoplasm, risk classification of intraductal papillary mucinous neoplasm and prediction of solid pseudopapillary neoplasm invasiveness compared to conventional imaging diagnosis. Radiomics is a promising tool in the field of medical imaging, providing a noninvasive, high-performance model for preoperative diagnosis and risk stratification of PCNs and improving prospects regarding management of these diseases. Further studies are warranted to investigate MRI image radiomics in connection with PCNs to improve the diagnosis and treatment strategies in the management of PCN patients.

Keywords: Intraductal papillary mucinous neoplasm; Mucinous cystic neoplasm; Pancreatic cystic neoplasm; Radiomics; Serous cystic neoplasm.

Fig. 1

Workflow for radiomics. LASSO: Least absolute shrinkage and selection operator; SVM: Support vector machine; RF: Random Forest.

Fig. 2

Examples of serous cystic neoplasm (SCN) and mucinous cystic neoplasm (MCN) on CT. Pre-contrast CT for SCN (A) and MCN (B); Portal phase CT for SCN (C) and MCN (D); 3D tumours for SCN (E) and MCN (F); Intensity histograms for SCN (G) and MCN (H). CT and tumour shape analysis revealed comparable image findings for the two cases; however, a notable distinction was observed between the SCN and MCN in the histograms.