New Model for Predicting Malignancy in Patients With Intraductal Papillary Mucinous Neoplasm
New Model for Predicting Malignancy in Patients With Intraductal Papillary Mucinous Neoplasm
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DOI：
10.1097/sla.0000000000003108
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发表时间：
2020-07-01
影响因子：
9
通讯作者：
Okazaki, Kazuichi
中科院分区：
文献类型：
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作者：
Shimizu, Yasuhiro;Hijioka, Susumu;Okazaki, Kazuichi
目的：建立一个简单、客观的预测导管内乳头状粘液瘤（IPMN）患者是否存在恶性肿瘤的模型，该模型易于在日常实践中应用，重要的是可适用于任何类型的病变。背景：恶性IPMN的预测模型尚未广泛应用于临床实践。方法：回顾性分析3家医院466例行胰腺切除术的IPMN患者的临床资料，建立模型。然后，该模型在日本8家医院的664例手术切除患者中进行了验证。在术前检查中，内镜超声检查（EUS）被认为是观察壁结节在模型开发和外部验证集必不可少的。恶性IPMNs被定义为伴有高度不典型增生和相关浸润性癌的IPMNs。结果：466例患者中，258例（55%）为恶性IPMNs（高级别不典型增生158例，侵袭性癌100例），208例（45%）为良性IPMNs。经Logistic回归分析，选择壁结节大小、主胰管直径、囊肿大小3个变量构建模型。该模型的受试者工作特征曲线下面积为0.763。在外部验证集中，病理诊断为恶性和良性IPMN分别为351例（53%）和313例（47%）。对于外部验证，该模型的恶性肿瘤预测能力对应的AUC为0.725。结论：该预测模型为医生和患者评估个体恶性肿瘤风险提供了重要信息，并可能有助于确定需要手术的患者。
Objective: To create a simple, objective model to predict the presence of malignancy in patients with intraductal papillary mucinous neoplasm (IPMN), which can be easily applied in daily practice and, importantly, adopted for any lesion types. Background: No predictive model for malignant IPMN has been widely applied in clinical practice. Methods: The clinical details of 466 patients with IPMN who underwent pancreatic resection at 3 hospitals were retrospectively analyzed for model development. Then, the model was validated in 664 surgically resected patients at 8 hospitals in Japan. In the preoperative examination, endoscopic ultrasonography (EUS) was considered to be essential to observe mural nodules in both the model development and external validation sets. Malignant IPMNs were defined as those with high-grade dysplasia and associated invasive carcinoma. Results: Of the 466 patients, 258 (55%) had malignant IPMNs (158 high-grade dysplasia, 100 invasive carcinoma), and 208 (45%) had benign IPMNs. Logistic regression analysis resulted in 3 variables (mural nodule size, main pancreatic duct diameter, and cyst size) being selected to construct the model. The area under the receiver operating characteristic curve (AUC) for the model was 0.763. In external validation sets, the pathological diagnosis was malignant and benign IPMN in 351 (53%) and 313 (47%) cases, respectively. For the external validation, the malignancy prediction ability of the model corresponded to an AUC of 0.725. Conclusion: This predictive model provides important information for physicians and patients in assessing an individual's risk for malignancy and may help to identify patients who need surgery.