Resectable pancreatic ductal adenocarcinoma: association between preoperative CT texture features and metastatic nodal involvement

• Published in: Cancer imaging 2020-02, Vol.20 (1), p.17-17, Article 17
• Main Authors: Fang, Wei Huan, Li, Xu Dong, Zhu, Hui, Miao, Fei, Qian, Xiao Hua, Pan, Zi Lai, Lin, Xiao Zhu
• Format: Article
• Language: English
• Subjects: Adenocarcinoma; Algorithms; Biopsy; Cancer; Cancer metastasis; CAT scans; Computed tomography; Computer-assisted image processing; Diagnostic imaging; FDA approval; Frequencies; Hospitals; Image processing; Likelihood ratio; Lymph; Lymphatic system; Magnetic resonance imaging; Medical imaging; Medical imaging equipment; Medical research; Metastases; Metastasis; Nodes; Noise; Pancreatic cancer; Pancreatic ductal adenocarcinoma; Pathology; Povidone; Scanners; Software; Texture; Texture analysis; Tomography; Transportation equipment industry; Tumors; Wavelet analysis
• ISSN: 1470-7330; 1740-5025; 1470-7330
• DOI: 10.1186/s40644-020-0296-3

To explore the relationship between the lymph node status and preoperative computed tomography images texture features in pancreatic cancer. A total of 155 operable pancreatic cancer patients (104 men, 51 women; mean age 63.8 ± 9.6 years), who had undergone contrast-enhanced computed tomography in the arterial and portal venous phases, were enrolled in this retrospective study. There were 73 patients with lymph node metastases and 82 patients without nodal involvement. Four different data sets, with thin (1.25 mm) and thick (5 mm) slices (at arterial phase and portal venous phase) were analysed. Texture analysis was performed by using MaZda software. A combination of feature selection algorithms was used to determine 30 texture features with the optimal discriminative performance for differentiation between lymph node positive and negative groups. The prediction performance of the selected feature was evaluated by receiver operating characteristic (ROC) curve analysis. There were 10 texture features with significant differences between two groups and significance in ROC analysis were identified. They were WavEnLH_s-2(wavelet energy with rows and columns are filtered with low pass and high pass frequency bands with scale factors 2) from wavelet-based features, 135dr_LngREmph (long run emphasis in 135 direction) and 135dr_Fraction (fraction of image in runs in 135 direction) from run length matrix-based features, and seven variables of sum average from coocurrence matrix-based features (SumAverg). The ideal cutoff value for predicting lymph node metastases was 270 for WavEnLH_s-2 (positive likelihood ratio 2.08). In addition, 135dr_LngREmph and 135dr_Fraction were correlated with the ratio of metastatic to examined lymph nodes. Preoperative computed tomography high order texture features provide a useful imaging signature for the prediction of nodal involvement in pancreatic cancer.