Scientific Reports · 2018 · 105 citations · 51 references
MedicineOncologySurgeryDiagnostic ImagingDiagnostic RadiologySurvival AnalysisCancer Survival AnalysisImage TextureMedical Image AnalysisMedical ImagingPancreatic CancerTexture AnalysisComputed Tomography PrognosisCox RegressionDisease-free Survival AnalysisDisease-free Survival AssessmentGray-level Co-occurrence MatricesIntra-tumor HeterogeneityLymph Node Metastasis PrognosisTexture Features
The value of image based texture features as a powerful method to predict prognosis and assist clinical management in cancer patients has been established recently. However, texture analysis using histograms and grey-level co-occurrence matrix in pancreas cancer patients has rarely been reported. We aimed to analyze the association of survival outcomes with texture features in pancreas head cancer patients. Eighty-eight pancreas head cancer patients who underwent preoperative CT images followed by curative resection were included. Texture features using different filter values were obtained. The texture features of average, contrast, correlation, and standard deviation with no filter, and fine to medium filter values as well as the presence of nodal metastasis were significantly different between the recurred (n = 70, 79.5%) and non-recurred group (n = 18, 20.5%). In the multivariate Cox regression analysis, lower standard deviation and contrast and higher correlation with lower average value representing homogenous texture were significantly associated with poorer DFS (disease free survival), along with the presence of lymph node metastasis. Texture parameters from routinely performed pre-operative CT images could be used as an independent imaging tool for predicting the prognosis in pancreas head cancer patients who underwent curative resection.
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Dong Xu, Xingxing He, Ying Chang et al. · Hepatogastroenterology · 2012 · 2K citations
Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice?
F. Davnall, Connie Yip, Gunnar Ljungqvist et al. · Insights into Imaging · 2012 · 878 citations · Full text