CT texture analysis in differential diagnosis of pancreatic ductal adenocarcinoma, pancreatic neuroendocrine tumor and solid pseudopapillary tumor
10.13929/j.issn.1003-3289.2020.04.018
- VernacularTitle: CT纹理分析鉴别诊断胰腺导管腺癌,胰腺神经内分泌肿瘤及实性假乳头状肿瘤
- Author:
Jun WANG
1
Author Information
1. Department of Radiology, the First Hospital of China Medical University
- Publication Type:Journal Article
- Keywords:
Artificial intelligence;
Diagnosis;
Pancreatic neoplasms;
Tomography, X-ray computed
- From:
Chinese Journal of Medical Imaging Technology
2020;36(4):554-558
- CountryChina
- Language:Chinese
-
Abstract:
Objective: To explore the feasibility of CT texture analysis in diagnosis and differential diagnosis of pancreatic ductal adenocarcinoma (PDAC), pancreatic neuroendocrine tumors (PNET) and solid pseudopapillary tumor of pancreas (SPTP). Methods: CT data of 98 patients with PDAC, 62 patients with SPTP and 39 patients with PNET proved by pathologically were retrospectively analyzed. ROI was manually delineated along tumor boundary at the largest level of tumor cross-section, and 46 texture features were extracted. All data were categorized according to dichotomies (PDAC vs rest; SPTP vs rest; PNET vs rest) and tri classification (PDAC vs SPTP vs PNET) methods. The single factor regression was used to analyze the diagnostic efficiency of each texture feature in differentiating dichotomy groups, and the AUC was calculated. After feature selection based on random forest algorithm, 6 machine learning classificators (LDA, K-NN, RF, Adabost, NB, NN) were used to classify dichotomous and triadic groups. The diagnostic efficiency of the classifier was analyzed using multi-factor regression analysis, and the AUC was calculated based on ten-fold cross validation. Results: For single texture feature identifying pancreatic tumors, low intensity small area emphasis and grey level nonuniformity were good for identifying PDAC vs rest and SPTP vs rest, respectively (AUC=0.73, 0.79, both P<0.01), while sum average was excellent for differentiating PNET vs rest (AUC=0.90, P<0.01). The diagnostic efficiency of classificator identifying PDAC vs rest, SPTP vs rest and PNET vs rest were very good or excellent, and the maximum AUC was 0.88 (RF), 0.86 (RF) and 0.94 (Adaboost), respectively. The classification accuracy of all classifiers for classifying PDAC vs SPTP vs PNET was good, and that of RF was the highest (0.80). Conclusion: CT texture analysis can be used to differentiate PDAC, SPTP and PNET. Machine learning algorithm can further improve the performance.
