Share Email Print
cover

Proceedings Paper

Feasibility of predicting pancreatic neuroendocrine tumor grade using deep features from unsupervised learning
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

This paper aimed to investigate if deep image features extracted via sparse autoencoder (SAE) could be used to preoperatively predict histologic grade in pancreatic neuroendocrine tumors (pNETs). In this study, a total of 114 patients from two institutions were involved. The deep image features were extracted based on the sparse autoencoder network via a 2000-time iteration. Considering the possible prediction error due to the small patient data size, we performed 10-fold cross-validation. To find the optimal hidden size, we set the size as a range of 6-10. The maximum relevance minimum redundancy (mRMR) features selection algorithm was used to select the most histologic graderelated image features. Then the radiomics signature was generated by using the selected features with Support Vector Machine (SVM), multivariable logistic regression (MLR) and artificial neural networks (ANN) methods. The prediction performance was evaluated using AUC value.

Paper Details

Date Published: 2 March 2020
PDF: 7 pages
Proc. SPIE 11318, Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications, 113180O (2 March 2020); doi: 10.1117/12.2548723
Show Author Affiliations
Yidong Wan, Zhejiang Univ. School of Medicine (China)
Zhejiang Univ. (China)
Lei Xu, Zhejiang Univ. School of Medicine (China)
Zhejiang Univ. (China)
Pengfei Yang, Zhejiang Univ. School of Medicine (China)
Zhejiang Univ. (China)
Zuozhen Cao, Zhejiang Univ. School of Medicine (China)
Zhejiang Univ. (China)
Chen Luo, Zhejiang Univ. School of Medicine (China)
Zhejiang Univ. (China)
Xiaoyong Shen, First Affiliated Hospital, Zhejiang Univ. School of Medicine (China)
Yan Wu, Second Affiliated Hospital, Zhejiang Univ. School of Medicine (China)
Dan Ruan, David Geffen School of Medicine, Univ. of California, Los Angeles (United States)
Tianye Niu, Georgia Institute of Technology (United States)
Zhejiang Univ. School of Medicine (China)
Zhejiang Univ. (China)


Published in SPIE Proceedings Vol. 11318:
Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications
Po-Hao Chen; Thomas M. Deserno, Editor(s)

© SPIE. Terms of Use
Back to Top
PREMIUM CONTENT
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?
close_icon_gray