How does the pancreatic solid pseudopapillary neoplasm confuse us: Analyzing from the point view of MRI-based...

To construct MRI-based radiomics logistic model in differentiating solid pseudopapillary neoplasm (SPN) from three differential diseases containing adenocarcinoma, neuroendocrine tumor (NET), and cystadenoma of pancreas.

A total of 21 SPNs and 140 differential diseases were enrolled. The MRI images of T1WI, T2WI, DWI, and contrast-enhanced (CE) sequences were taken to delineate the volume of interest, and the corresponding radiomics features were calculated. After the preprocess of data balance and image standardize, the data was divided into training set (6 SPNs and 42 differential diseases) and validation set (15 SPNs and 98 differential diseases) with a proportion of 7:3, randomly. Then after feature selection, four MRI-based logistic models included T1WI, T2WI, DWI, CE, and sum logistic models (Log-T1WI, Log-T2WI, Log-DWI, Log-CE, and Log-sum) were established. The receiver operation curve (ROC) was depicted to evaluate the efficacy of each model.

To the single MRI sequence, the AUCs of Log-T1WI, Log-T2WI, Log-DWI, and Log-CE were similar. Seemingly the AUCs of Log-T2WI were slightly higher with 0. 876 (95%CI, 0.797-0.956) in the training set and 0.853 (95%CI, 0.708-0.998) in the validation set. The Log-sum of four MRI sequences displayed better differentiating efficiency, with AUCs of 0.929 (95%CI, 0.877-0.980) in the training set and 0.925 (95%CI, 0.845-1.000) in the validation set. The Log-Ra/Clin model combined clinical information and radiomics showed the highest AUC of 0.962 (95%CI, 0.919-0.985).

MRI-based radiomics analysis helped to discern SPNs from radiologically misdiagnosed adenocarcinoma, neuroendocrine tumor, and cystadenoma of pancreas. The efficacy of single sequence logistic model was similar. The Log-sum combined four sequences and Log-Ra/Clin combined clinical information and radiomics demonstrated the better performance in distinction.