Risk prediction for malignant intraductal papillary mucinous neoplasm of the pancreas: logistic regression versus machine learning.

Published on Nov 18, 2020in Scientific Reports3.998
· DOI :10.1038/S41598-020-76974-7
Jae Seung Kang1
Estimated H-index: 1
(SNU: Seoul National University),
Chanhee Lee1
Estimated H-index: 1
(SNU: Seoul National University)
+ 58 AuthorsJin Young Jang (SNU: Seoul National University)
Most models for predicting malignant pancreatic intraductal papillary mucinous neoplasms were developed based on logistic regression (LR) analysis. Our study aimed to develop risk prediction models using machine learning (ML) and LR techniques and compare their performances. This was a multinational, multi-institutional, retrospective study. Clinical variables including age, sex, main duct diameter, cyst size, mural nodule, and tumour location were factors considered for model development (MD). After the division into a MD set and a test set (2:1), the best ML and LR models were developed by training with the MD set using a tenfold cross validation. The test area under the receiver operating curves (AUCs) of the two models were calculated using an independent test set. A total of 3,708 patients were included. The stacked ensemble algorithm in the ML model and variable combinations containing all variables in the LR model were the most chosen during 200 repetitions. After 200 repetitions, the mean AUCs of the ML and LR models were comparable (0.725 vs. 0.725). The performances of the ML and LR models were comparable. The LR model was more practical than ML counterpart, because of its convenience in clinical use and simple interpretability.
#1Yasuhiro ShimizuH-Index: 67
#1Yasuhiro ShimizuH-Index: 1
Last. Kazuichi OkazakiH-Index: 85
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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
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#1W. Jung (Ajou University)H-Index: 1
#1Woohyun Jung (Ajou University)H-Index: 17
Last. Jun Young Jang (SNU: Seoul National University)H-Index: 11
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