Gray Level Co-occurrence Matrix Moment Research Articles

Prediction of Therapeutic Response of Hepatocellular Carcinoma to Transcatheter Arterial Chemoembolization Based on Pretherapeutic Dynamic CT and Textural Findings.

The objective of our study was to assess the value of CT texture analysis for prediction of therapeutic response of hepatocellular carcinoma (HCC) to transcatheter arterial chemoembolization (TACE) with pretherapeutic dynamic CT. We retrospectively analyzed 132 HCCs in 96 patients treated with TACE who underwent dynamic CT before initial TACE. Imaging findings and arterial enhancement ratios were analyzed. All HCCs were manually segmented, and their texture features were quantitatively extracted using in-house software. CT texture was quantified with 2D and 3D analysis. HCCs were classified as with and without complete response (CR) according to modified Response Evaluation Criteria in Solid Tumors. Predictive factors for CR were assessed with multivariate analysis. Radiologic responses were correlated with time to progression (TTP). Of the 132 HCCs, CR was achieved in 75 (56.8%). Tumor size, subjective arterial tumor attenuation, and arterial enhancement ratios were significantly associated with CR. On 2D and 3D analysis, tumors with CR showed significantly lower homogeneity and higher mean attenuation, gray-level co-occurrence matrix (GLCM) moments, and CT number percentiles (p < 0.05). On multivariate analysis, higher subjective tumor attenuation (adjusted odds ratio [OR] = 23.35), arterial enhancement ratio (OR = 14.07), GLCM moments (OR = 6.57), smaller tumor size (OR = 17.26), and lower homogeneity (OR = 0.69) were significant predictors of CR compared with incomplete response (p < 0.05). Median survival value for TTP was significantly longer in tumors with CR (p < 0.001). Pretherapeutic dynamic CT texture analysis can be valuable to predict CR of HCC to TACE. Higher arterial enhancement and GLCM moments, lower homogeneity, and smaller tumor size are significant predictors of CR after TACE.

Differentiation of Urinary Stone and Vascular Calcifications on Non-contrast CT Images: An Initial Experience using Computer Aided Diagnosis

The purpose of this study was to develop methods for the differentiation of urinary stones and vascular calcifications using computer-aided diagnosis (CAD) of non-contrast computed tomography (CT) images. From May 2003 to February 2004, 56 patients that underwent a pre-contrast CT examination and subsequently diagnosed as ureter stones were included in the study. Fifty-nine ureter stones and 53 vascular calcifications on pre-contrast CT images of the patients were evaluated. The shapes of the lesions including disperseness, convex hull depth, and lobulation count were analyzed for patients with ureter stones and vascular calcifications. In addition, the internal textures including edge density, skewness, difference histogram variation (DHV), and the gray-level co-occurrence matrix moment were also evaluated for the patients. For evaluation of the diagnostic accuracy of the shape and texture features, an artificial neural network (ANN) and receiver operating characteristics curve (ROC) analyses were performed. Of the several shape factors, disperseness showed a statistical difference between ureter stones and vascular calcifications (p < 0.05). For the internal texture features, skewness and DHV showed statistical differences between ureter stones and vascular calcifications (p < 0.05). The performance of the ANN was evaluated by examining the area under the ROC curves (AUC, A (z)). The A (z) value was 0.85 for the shape parameters and 0.88 for the texture parameters. In this study, several parameters regarding shape and internal texture were statistically different between ureter stones and vascular calcifications. The use of CAD would make it possible to differentiate ureter stones from vascular calcifications by a comparison of these parameters.