He Xiangyi, Fan Rong, Sun Jing, Ren Yanhao, Zhao Xuesong, Rui Weiwei, Yuan Yaozong, Zou Duowu
Department of Gastroenterology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
School of Mathematical Sciences, Fudan University, Shanghai, China.
Front Oncol. 2023 Jan 24;13:1087852. doi: 10.3389/fonc.2023.1087852. eCollection 2023.
BACKGROUND/OBJECTIVES: There is no predictive model available to address early stage malignant intraductal papillary mucinous neoplasm (IPMN) including high grade dysplasia (HGD) and pT1a (invasive component≤0.5 cm). The aim of this study was to establish an objective and sufficient model to predict the degree of malignancy in patients with IPMN, which can be easily applied in daily practice and adopted for any type of lesion.
A retrospective cohort study of 309 patients who underwent surgical resection for IPMN was performed. Members of the cohort were randomly allocated to the training or testing set. A detection tree model and random forest model were used for a 3-class classification to distinguish low grade dysplasia (LGD), HGD/pT1a IPMN, and invasive intraductal papillary mucinous cancer (I-IPMC) beyond pT1a.
Of the 309 patients, 54 (17.4%) had early stage malignancy (19 HGD, 35 pT1a), 49 (15.9%) had I-IPMC beyond pT1a, and 206 (66.7%) had LGD IPMN. We proposed a 3-class classification model using a random forest algorithm, and the model had an accuracy of 99.5% with the training set, and displayed an accuracy of 96.0% with the testing set. We used SHAP for interpretation of the model and showed the top five factors (mural nodule size, main pancreatic duct diameter, CA19-9 levels, lesion edge and common bile duct dilation) were most likely to influence the 3-class classification results in terms of interpretation of the random forest model.
This predictive model will help assess an individual's risk for different stages of IPMN malignancy and may help identify patients with IPMN who require surgery.
背景/目的：目前尚无预测模型可用于评估早期恶性导管内乳头状黏液性肿瘤（IPMN），包括高级别异型增生（HGD）和pT1a（浸润成分≤0.5 cm）。本研究的目的是建立一个客观且充分的模型，以预测IPMN患者的恶性程度，该模型可轻松应用于日常实践，并适用于任何类型的病变。
对309例行IPMN手术切除的患者进行回顾性队列研究。队列成员被随机分配到训练集或测试集。使用检测树模型和随机森林模型进行三类分类，以区分低级别异型增生（LGD）、HGD/pT1a IPMN和pT1a以上的浸润性导管内乳头状黏液癌（I-IPMC）。
在309例患者中，54例（17.4%）有早期恶性病变（19例HGD，35例pT1a），49例（15.9%）有pT1a以上的I-IPMC，206例（66.7%）有LGD IPMN。我们使用随机森林算法提出了一个三类分类模型，该模型在训练集上的准确率为99.5%，在测试集上的准确率为96.0%。我们使用SHAP对模型进行解释，并表明前五个因素（壁结节大小、主胰管直径、CA19-9水平、病变边缘和胆总管扩张）在随机森林模型的解释方面最有可能影响三类分类结果。
该预测模型将有助于评估个体患不同阶段IPMN恶性肿瘤的风险，并可能有助于识别需要手术的IPMN患者。
