ArticleCancer imaging : the official publication of the International Cancer Imaging Society2020
Resectable pancreatic ductal adenocarcinoma: association between preoperative CT texture features and metastatic nodal involvement.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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13 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Pancreas image mining: a systematic review of radiomics.European radiology · 2021Pooled it
- Development and validation of a radiomics model of magnetic resonance for predicting liver metastasis in resectable pancreatic ductal adenocarcinoma patients.Radiation oncology (London, England) · 2023Trial
- Artificial intelligence strategies based on random forests for detecting ischemia-reperfusion injury changes in kidney tissue during intravital imaging.Scientific reports · 2025Article
- Delta-Radiomics Approach Using Contrast-Enhanced and Noncontrast-Enhanced Computed Tomography Images for Predicting Distant Metastasis in Patients With Borderline Resectable Pancreatic Carcinoma.Advances in radiation oncology · 2025Article
- Radiomic features based on pyradiomics predict CD276 expression associated with breast cancer prognosis.Heliyon · 2024Article
- Contrast-enhanced CT radiomics combined with multiple machine learning algorithms for preoperative identification of lymph node metastasis in pancreatic ductal adenocarcinoma.Frontiers in oncology · 2024Article
- Noninvasive assessment of significant liver fibrosis in rabbits by spectral CT parameters and texture analysis.Japanese journal of radiology · 2023Article
- Liver metastases in pancreatic ductal adenocarcinoma: a predictive model based on CT texture analysis.La Radiologia medica · 2022Article
- Pancreatic cancer, radiomics and artificial intelligence.The British journal of radiology · 2022Review
- Artificial Intelligence Applied to Pancreatic Imaging: A Narrative Review.Healthcare (Basel, Switzerland) · 2022Review
- Analysis of the value of enhanced CT combined with texture analysis in the differential diagnosis of pulmonary sclerosing pneumocytoma and atypical peripheral lung cancer: a feasibility study.BMC medical imaging · 2022Article
- Myocardial area at risk and salvage in reperfused acute MI measured by texture analysis of cardiac T2 mapping and its prediction value of functional recovery in the convalescent stage.The international journal of cardiovascular imaging · 2021Article
- Texture analysis of MR images to identify the differentiated degree in hepatocellular carcinoma: a retrospective study.BMC cancer · 2020Article
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Authors and funding
7 authors at 2 institutions in 1 country.
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Abstract
backgroundTo explore the relationship between the lymph node status and preoperative computed tomography images texture features in pancreatic cancer.
methodsA 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.
resultsThere 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.
conclusionsPreoperative computed tomography high order texture features provide a useful imaging signature for the prediction of nodal involvement in pancreatic cancer.
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