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. 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. 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. 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 () can be incorporated in the electronic medical record.

中文翻译：

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胰腺浆液性囊性肿瘤的自然史和生长预测模型

浆液性囊性肿瘤（SCN）是良性胰腺囊性肿瘤，根据局部并发症和生长速度可能需要切除。我们的目标是开发 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。利用单个机构前瞻性维护的胰腺囊肿数据库，确定了患有 SCN 的患者。诊断确认包括影像学、囊肿抽吸、病理学或专家意见。通过放射学或手术测量囊肿大小直径。诊断后间隔成像≥3个月的患者被纳入。利用灵活的受限三次样条对时间和先前测量的非线性进行建模。使用 R（V3.50，维也纳，奥地利）和 rms 包进行模型拟合和分析。在 1998 年至 2021 年的 203 名符合条件的患者中，平均初始囊肿大小为 31 毫米（范围 5-160 毫米），平均随访时间为 72 个月（范围 3-266 个月）。该模型有效地捕捉了囊肿大小和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）显着预测了当前的囊肿生长（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性能的一致性，特别是对于较短的随访间隔。无论初始大小如何，SCN 通常都具有相似的增长率。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，并且该免费模型 () 可以合并到电子病历中。