Department of Hepato-Biliary-Pancreatic Surgery, The Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University, Changzhou, China.
Department of Hepatobiliary Surgery, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China.
J Gastroenterol Hepatol. 2022 Jul;37(7):1380-1388. doi: 10.1111/jgh.15841. Epub 2022 Apr 21.
Gallbladder polyps (GBPs) are relatively common. Many studies have attempted to distinguish between benign and neoplastic GBPs to identify early-stage gallbladder carcinoma. We have established an accurate neoplastic predictive model and evaluated the effectiveness of radiomics in predicting malignancy in patients with GBPs.
A total of 503 patients confirmed through postoperative pathology were included in this retrospective study. Clinical information and ultrasonographic findings were retrospectively analyzed. The model was constructed from independent risk factors using Spearman correlation and logistic regression analysis of a training cohort of 250 GBP patients, and its efficacy was verified using an internal validation group of 253 consecutive patients through the receiver operating characteristic curve (ROC). The area of GBPs was delimited manually, and the texture features of ultrasound images were analyzed using correlation and ROC analysis.
Independent predictors, including age, gallstones, carcinoembryonic antigen, polyp size, and sessile shape, were incorporated into the nomogram model for the neoplastic potential of GBPs. Compared with other proposed prediction methods, the established nomogram model showed good discrimination ability in the training group (area under the curve [AUC]: 0.865) and validation group (AUC: 0.845). Regarding ultrasonic radiomics, the minimum caliper diameter was identified as the only independent predictor (AUC: 0.841).
Our preoperative nomogram model can successfully evaluate the neoplastic potential of GBPs using simple clinical data, and our study verified the use of radiomics in GBP identification, which may be valuable for avoiding unnecessary surgery in patients.
胆囊息肉（GBP）较为常见。许多研究试图区分良性和肿瘤性 GBP，以识别早期胆囊癌。我们建立了一个准确的肿瘤预测模型，并评估了放射组学在预测 GBP 患者恶性肿瘤中的有效性。
本回顾性研究共纳入 503 例经术后病理证实的患者。回顾性分析临床资料和超声表现。使用 Spearman 相关性和逻辑回归分析从 250 例 GBP 患者的训练队列中构建模型，使用 253 例连续患者的内部验证组通过接受者操作特征曲线（ROC）验证其疗效。手动划定 GBP 区域，并使用相关性和 ROC 分析对超声图像的纹理特征进行分析。
包括年龄、胆囊结石、癌胚抗原、息肉大小和无蒂形状在内的独立预测因素被纳入 GBP 肿瘤潜能的列线图模型。与其他提出的预测方法相比，该列线图模型在训练组（曲线下面积[AUC]：0.865）和验证组（AUC：0.845）中均显示出良好的区分能力。关于超声放射组学，最小卡尺直径被确定为唯一的独立预测因子（AUC：0.841）。
我们的术前列线图模型可以使用简单的临床数据成功评估 GBP 的肿瘤潜能，我们的研究验证了放射组学在 GBP 识别中的应用，这可能有助于避免患者不必要的手术。
