IPMN-LEARN: A linear support vector machine learning model for predicting low-grade intraductal papillary mucinous neoplasms

Abstract

Backgrounds/aims: We aimed to build a machine learning tool to help predict low-grade intraductal papillary mucinous neoplasms (IPMNs) in order to avoid unnecessary surgical resection. IPMNs are precursors to pancreatic cancer. Surgical resection remains the only recognized treatment for IPMNs yet carries some risks of morbidity and potential mortality. Existing clinical guidelines are imperfect in distinguishing low-risk cysts from high-risk cysts that warrant resection.

Methods: We built a linear support vector machine (SVM) learning model using a prospectively maintained surgical database of patients with resected IPMNs. Input variables included 18 demographic, clinical, and imaging characteristics. The outcome variable was the presence of low-grade or high-grade IPMN based on post-operative pathology results. Data were divided into a training/validation set and a testing set at a ratio of 4:1. Receiver operating characteristics analysis was used to assess classification performance.

Results: A total of 575 patients with resected IPMNs were identified. Of them, 53.4% had low-grade disease on final pathology. After classifier training and testing, a linear SVM-based model (IPMN-LEARN) was applied on the validation set. It achieved an accuracy of 77.4%, with a positive predictive value of 83%, a specificity of 72%, and a sensitivity of 83% in predicting low-grade disease in patients with IPMN. The model predicted low-grade lesions with an area under the curve of 0.82.

Conclusions: A linear SVM learning model can identify low-grade IPMNs with good sensitivity and specificity. It may be used as a complement to existing guidelines to identify patients who could avoid unnecessary surgical resection.

Keywords: Intraductal papillary mucinous neoplasm; Machine learning; Pancreatic cyst; Pancreatic neoplasm.

Fig. 1

Data analysis scheme used to predict IPMN grade. A total of 575 patients were enrolled in the study. Data were divided into a training/testing set that included 460 patients and an independent validation set that included 115 patients. Eighteen variables including demographic patient characteristics, clinical information, and IPMN imaging descriptors were used to build a linear SVM-based machine learning model to predict low-grade IPMN status post-surgical resection in this patient population. IPMN, intraductal papillary mucinous neoplasm; SVM, support vector machine.

Fig. 2

IPMN-LEARN, A linear SVM model predictive of low-grade IPMN grade. A linear SVM model was trained and validated using data from 460 patients. The model was subsequently tested on a held-out patient group consisting of 115 patients. The ROC curve illustrates the performance of the model in predicting IPMN grade in the testing set. The AUC was 0.82. IPMN, intraductal papillary mucinous neoplasm; ROC, receiver operating characteristics; SVM, support vector machine; AUC, area under the curve.