Natural history and growth prediction model of pancreatic serous cystic neoplasms

Abstract

ObjectiveSerous cystic neoplasms (SCN) are benign pancreatic cystic neoplasms that may require resection based on local complications and rate of growth. We aimed to develop a predictive model for the growth curve of SCNs to aid in the clinical decision making of determining need for surgical resection. MethodsUtilizing a prospectively maintained pancreatic cyst database from a single institution, patients with SCNs were identified. Diagnosis confirmation included imaging, cyst aspiration, pathology, or expert opinion. Cyst size diameter was measured by radiology or surgery. Patients with interval imaging ≥3 months from diagnosis were included. Flexible restricted cubic splines were utilized for modeling of non-linearities in time and previous measurements. Model fitting and analysis were performed using R (V3.50, Vienna, Austria) with the rms package. ResultsAmong 203 eligible patients from 1998 to 2021, the mean initial cyst size was 31 mm (range 5–160 mm), with a mean follow-up of 72 months (range 3–266 months). The model effectively captured the non-linear relationship between cyst size and time, with both time and previous cyst size (not initial cyst size) significantly predicting current cyst growth (p < 0.01). The root mean square error for overall prediction was 10.74. Validation through bootstrapping demonstrated consistent performance, particularly for shorter follow-up intervals. ConclusionSCNs typically have a similar growth rate regardless of initial size. An accurate predictive model can be used to identify rapidly growing outliers that may warrant surgical intervention, and this free model (https://riskcalc.org/SerousCystadenomaSize/) can be incorporated in the electronic medical record.