Non-enhanced magnetic resonance imaging-based radiomics model for the differentiation of pancreatic adenosquamous carcinoma from pancreatic ductal adenocarcinoma

Abstract

PurposeTo evaluate the diagnostic performance of radiomics model based on fully automatic segmentation of pancreatic tumors from non-enhanced magnetic resonance imaging (MRI) for differentiating pancreatic adenosquamous carcinoma (PASC) from pancreatic ductal adenocarcinoma (PDAC).Materials and methodsIn this retrospective study, patients with surgically resected histopathologically confirmed PASC and PDAC who underwent MRI scans between January 2011 and December 2020 were included in the study. Multivariable logistic regression analysis was conducted to develop a clinical and radiomics model based on non-enhanced T1-weighted and T2-weighted images. The model performances were determined based on their discrimination and clinical utility. Kaplan-Meier and log-rank tests were used for survival analysis.ResultsA total of 510 consecutive patients including 387 patients (age: 61 ± 9 years; range: 28–86 years; 250 males) with PDAC and 123 patients (age: 62 ± 10 years; range: 36–84 years; 78 males) with PASC were included in the study. All patients were split into training (n=382) and validation (n=128) sets according to time. The radiomics model showed good discrimination in the validation (AUC, 0.87) set and outperformed the MRI model (validation set AUC, 0.80) and the ring-enhancement (validation set AUC, 0.74).ConclusionsThe radiomics model based on non-enhanced MRI outperformed the MRI model and ring-enhancement to differentiate PASC from PDAC; it can, thus, provide important information for decision-making towards precise management and treatment of PASC.

Prognostic Predicting Model of Pancreatic Body Tail Carcinoma Using Clinical and CT Radiomic Data

“…In the future, multicenter big data research would be conducted, and deep learning and artificial intelligence methods may be used to improve the prediction performance of the model. 31,32…”