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

Autor: Fei Miao, Hui Zhu, Zi Lai Pan, Xudong Li, Wei Huan Fang, Xiaohua Qian, Xiao Zhu Lin
Rok vydání: 2019
Předmět:
lcsh:Medical physics. Medical radiology. Nuclear medicine
Adult
Male
medicine.medical_specialty
lcsh:R895-920
Feature selection
Metastases
lcsh:RC254-282
Likelihood ratios in diagnostic testing
030218 nuclear medicine & medical imaging
Pancreatic ductal adenocarcinoma
03 medical and health sciences
0302 clinical medicine
Pancreatic cancer
medicine
Cutoff
Humans
Radiology
Nuclear Medicine and imaging
Lymph node
Computed tomography
Aged
Retrospective Studies
Aged
80 and over
Radiological and Ultrasound Technology
Receiver operating characteristic
business.industry
General Medicine
Middle Aged
lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens
medicine.disease
Computer-assisted image processing
Pancreatic Neoplasms
medicine.anatomical_structure
Oncology
Texture analysis
Feature (computer vision)
030220 oncology & carcinogenesis
Lymphatic Metastasis
Female
Radiology
Lymph
business
Tomography
X-Ray Computed
Carcinoma
Pancreatic Ductal
Research Article
Zdroj: Cancer Imaging
Cancer Imaging, Vol 20, Iss 1, Pp 1-10 (2020)
ISSN: 1470-7330
Popis: Background To explore the relationship between the lymph node status and preoperative computed tomography images texture features in pancreatic cancer. Methods 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. Results 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. Conclusions Preoperative computed tomography high order texture features provide a useful imaging signature for the prediction of nodal involvement in pancreatic cancer.
Databáze: OpenAIRE
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