Higher volume growth rate is associated with development of worrisome features in patients with branch duct-intraductal papillary mucinous neoplasms

A Comparison of Single Dimension and Volume Measurements in the Risk Stratification of Pancreatic Cystic Lesions

The incidence of pancreatic cystic lesions (PCLs) has been rising due to improvements in imaging. Of these, intraductal papillary mucinous neoplasms (IPMNs) are the most common and are thought to contribute to almost 20% of pancreatic adenocarcinomas. All major society guidelines for the management of IPMNs use size defined by maximum diameter as the primary determinant of whether surveillance or surgical resection is recommended. However, there is no consensus on how these measurements should be obtained or whether a single imaging modality is superior. Furthermore, the largest diameter may fail to capture the complexity of PCLs, as most are not perfectly spherical. This article reviews current PCL measurement techniques in CT, MRI, and EUS and posits volume as a possible alternative to the largest diameter.

Branch duct-intraductal papillary mucinous neoplasms (BD-IPMNs): an MRI-based radiomic model to determine the malignant degeneration potential

BACKGROUND

Branch duct-intraductal papillary mucinous neoplasms (BD-IPMNs) are the most common pancreatic cystic tumors and have a low risk of malignant transformation. Features able to early identify high-risk BD-IPMNs are lacking, and guidelines currently rely on the occurrence of worrisome features (WF) and high-risk stigmata (HRS).

AIM

In our study, we aimed to use a magnetic resonance imaging (MRI) radiomic model to identify features linked to a higher risk of malignant degeneration, and whether these appear before the occurrence of WF and HRS.

METHODS

We retrospectively evaluated adult patients with a known BD-IPMN who had had at least two contrast-enhanced MRI studies at our center and a 24-month minimum follow-up time. MRI acquisition protocol for the two examinations included pre- and post-contrast phases and diffusion-weighted imaging (DWI)/apparent diffusion coefficient (ADC) map. Patients were divided into two groups according to the development of WF or HRS at the end of the follow-up (Group 0 = no WF or HRS; Group 1 = WF or HRS). We segmented the MRI images and quantitative features were extracted and compared between the two groups. Features that showed significant differences (SF) were then included in a LASSO regression method to build a radiomic-based predictive model.

RESULTS

We included 50 patients: 31 in Group 0 and 19 in Group 1. No patients in this cohort developed HRS. At baseline, 47, 67, 38, and 68 SF were identified for pre-contrast T1-weighted (T1-W) sequence, post-contrast T1-W sequence, T2-weighted (T2- W) sequence, and ADC map, respectively. At the end of follow-up, we found 69, 78, 53, and 91 SF, respectively. The radiomic-based predictive model identified 16 SF: more particularly, 5 SF for pre-contrast T1-W sequence, 6 for post-contrast T1-W sequence, 3 for T2-W sequence, and 2 for ADC.

CONCLUSION

We identified radiomic features that correlate significantly with WF in patients with BD-IPMNs undergoing contrast-enhanced MRI. Our MRI-based radiomic model can predict the occurrence of WF.