• Yasuhiro Shimizu, Susumu Hijioka, Seiko Hirono, Toshifumi Kin, Takao Ohtsuka, Atsushi Kanno, Shinsuke Koshita, Keiji Hanada, Masayuki Kitano, Hiroyuki Inoue, Takao Itoi, Toshiharu Ueki, Keitaro Matsuo, Akio Yanagisawa, Hiroki Yamaue, Masanori Sugiyama, and Kazuichi Okazaki.
• Department of Gastroenterological Surgery, Aichi Cancer Center Hospital, Nagoya, Japan.
• Ann. Surg. 2020 Jul 1; 272 (1): 155-162.

ObjectiveTo 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.BackgroundNo predictive model for malignant IPMN has been widely applied in clinical practice.MethodsThe 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.ResultsOf 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.ConclusionThis 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.

26 keywords...

hide…