Predictive Value of the Texture Analysis of Enhanced Computed Tomographic Images for Preoperative Pancreatic Carcinoma Differentiation

Frontiers in Bioengineering and Biotechnology
Zhang LonglongWei Wei

Abstract

To assess the utility of texture analysis for predicting the pathological degree of differentiation of pancreatic carcinoma (PC). Eighty-three patients with PC who went through postoperative pathology diagnose and CT examination were selected at Anhui Provincial Hospital. Among them, 34 cases were moderately differentiated, 13 cases were poorly differentiated, and 36 cases were moderately poorly differentiated. The images in the arterial and venous phase (VP) with the lesions at their largest cross section were selected to manually outline the region of interest (ROI) to delineate lesions using open-source software. A total of 396 features were extracted from the ROI using AK software. Spearman correlation analysis and random forest selection by filter (rfSBF) in the caret package of R studio were used to select the discriminating features. The receiver operating characteristic ROC analysis was used to evaluate their discriminative performance. Twelve and six features were selected in the arterial and VPs, respectively. The areas under the ROC curve (AUC) in the arterial phase (AP) for diagnosing poorly differentiated, moderately differentiated and moderate-poorly differentiated cases were 0.80, 1, and 0.80 in the training grou...Continue Reading

References

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Citations

Jun 22, 2021·Frontiers in Oncology·Wen ChenMing Zhang

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Methods Mentioned

BETA
feature extraction

Software Mentioned

R
SmartPrep
ITK
SNAP
random
pROC
AK

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