Abstract：
OBJECTIVE: Serous 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.
METHODS: Utilizing 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.
RESULTS: Among 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.
CONCLUSIONS: SCNs 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.
METHODS: Utilizing 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.
RESULTS: Among 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.
CONCLUSIONS: SCNs 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.
摘要：
目的：浆液性囊性肿瘤（SCN）是良性胰腺囊性肿瘤，可能需要根据局部并发症和生长速度进行切除。我们旨在开发SCN生长曲线的预测模型，以帮助确定是否需要手术切除的临床决策。
方法：利用来自单一机构的前瞻性维护的胰腺囊肿数据库，有SCNs的患者被确定。诊断确认包括影像学检查,囊肿抽吸术,病理学,或专家意见。通过放射学或手术测量囊肿大小直径。纳入诊断后间隔影像学≥3个月的患者。柔性受限三次样条用于时间和先前测量的非线性建模。使用R(V3.50，维也纳，奥地利)与均方根包。
结果：在1998年至2021年的203例符合条件的患者中，平均初始囊肿大小为31毫米（范围为5-160毫米），平均随访72个月（范围3-266个月）。该模型有效地捕获了囊肿大小与时间之间的非线性关系,时间和先前的囊肿大小（不是初始囊肿大小）显着预测当前囊肿的生长（p<0.01）。总体预测的均方根误差为10.74。通过引导验证证明了一致的性能，特别是对于较短的随访间隔。
结论：SCN通常具有相似的生长速率，无论初始大小如何。准确的预测模型可用于识别可能需要手术干预的快速增长的异常值，这个免费的模型（https://riskcalc.org/SerousCystadenomaSize/）可以合并到电子病历中。
方法：利用来自单一机构的前瞻性维护的胰腺囊肿数据库，有SCNs的患者被确定。诊断确认包括影像学检查,囊肿抽吸术,病理学,或专家意见。通过放射学或手术测量囊肿大小直径。纳入诊断后间隔影像学≥3个月的患者。柔性受限三次样条用于时间和先前测量的非线性建模。使用R(V3.50，维也纳，奥地利)与均方根包。
结果：在1998年至2021年的203例符合条件的患者中，平均初始囊肿大小为31毫米（范围为5-160毫米），平均随访72个月（范围3-266个月）。该模型有效地捕获了囊肿大小与时间之间的非线性关系,时间和先前的囊肿大小（不是初始囊肿大小）显着预测当前囊肿的生长（p<0.01）。总体预测的均方根误差为10.74。通过引导验证证明了一致的性能，特别是对于较短的随访间隔。
结论：SCN通常具有相似的生长速率，无论初始大小如何。准确的预测模型可用于识别可能需要手术干预的快速增长的异常值，这个免费的模型（https://riskcalc.org/SerousCystadenomaSize/）可以合并到电子病历中。
