Feasibility analysis of predicting expression of Ki67 in pancreatic cystic neoplasm based on radiomics.
10.7507/1001-5515.201805014
- Author:
Ran WEI
1
,
2
;
Kanru LIN
3
;
Yi GUO
1
,
4
;
Ji LI
3
;
Yuanyuan WANG
1
,
2
Author Information
1. Department of Electronic Engineering, Fudan University, Shanghai 200433, P.R.China
2. Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai 200433, P.R.China.
3. Department of Pancreatic Surgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200040, P.R.China.
4. Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai 200433, P.R.China.guoyi@fudan.edu.cn.
- Publication Type:Journal Article
- Keywords:
Ki67 molecular marker;
multi-detector computed tomography;
pancreatic cystic neoplasms;
radiomics
- From:
Journal of Biomedical Engineering
2019;36(1):1-6
- CountryChina
- Language:Chinese
-
Abstract:
This study aims to predict expression of Ki67 molecular marker in pancreatic cystic neoplasm using radiomics. We firstly manually segmented tumor area in multi-detector computed tomography (MDCT) images. Then 409 high-throughput features were automatically extracted and the least absolute shrinkage selection operator (LASSO) regression model was used for feature selection. After 200 bootstrapping repetitions of LASSO, 20 most frequently selected features made up the optimal feature set. Then 200 bootstrapping repetitions of support vector machine (SVM) classifier with 10-fold cross-validation were used to avoid overfitting and accurately predict the Ki67 expression. The highest prediction accuracy could achieve 85.29% and the highest area under the receiver operating characteristic curve (AUC) was 91.54% with a sensitivity (SENS) of 81.88% and a specificity (SPEC) of 86.75%. According to the results of experiment, the feasibility of predicting expression of Ki67 in pancreatic cystic neoplasm based on radiomics was verified.
