1) CT texture analysis: a potential tool for prediction of survival in patients with metastatic clear cell carcinoma treated with sunitinib.
Haider MA, et al. Cancer Imaging. 2017.
BACKGROUND: To assess CT texture based quantitative imaging biomarkers in the prediction of progression free survival (PFS) and overall survival (OS) in patients with clear cell renal cell carcinoma undergoing treatment with Sunitinib.
METHODS: In this retrospective study, measurable lesions of 40 patients were selected based on RECIST criteria on standard contrast enhanced CT before and 2 months after treatment with Sunitinib. CT Texture analysis was performed using TexRAD research software (TexRAD Ltd, Cambridge, UK). Using a Cox regression model, correlation of texture parameters with measured time to progression and overall survival were assessed. Evaluation of combined International Metastatic Renal-Cell Carcinoma Database Consortium Model (IMDC) score with texture parameters was also performed.
RESULTS: Size normalized standard deviation (nSD) alone at baseline and follow-up after treatment was a predictor of OS (Hazard ratio (HR) = 0.01 and 0.02; 95% confidence intervals (CI): 0.00 – 0.29 and 0.00 – 0.39; p = 0.01 and 0.01). Entropy following treatment and entropy change before and after treatment were both significant predictors of OS (HR = 2.68 and 87.77; 95% CI = 1.14 – 6.29 and 1.26 – 6115.69; p = 0.02 and p = 0.04). nSD was also a predictor of PFS at baseline and follow-up (HR = 0.01 and 0.01: 95% CI: 0.00 – 0.31 and 0.001 – 0.22; p = 0.01 and p = 0.003). When nSD at baseline or at follow-up was combined with IMDC, it improved the association with OS and PFS compared to IMDC alone.
CONCLUSION: Size normalized standard deviation from CT at baseline and follow-up scans is correlated with OS and PFS in clear cell renal cell carcinoma treated with Sunitinib.
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2) CT texture analysis can help differentiate between malignant and benign lymph nodes in the mediastinum in patients suspected for lung cancer.
Andersen MB, et al. Acta Radiol. 2016.
BACKGROUND: In patients with non-small-cell lung carcinoma NSCLC the lymph node staging in the mediastinum is important due to impact on management and prognosis. Computed tomography texture analysis (CTTA) is a postprocessing technique that can evaluate the heterogeneity of marked regions in images.
PURPOSE: To evaluate if CTTA can differentiate between malignant and benign lymph nodes in a cohort of patients with suspected lung cancer.
MATERIAL AND METHODS: With tissue sampling as reference standard, 46 lymph nodes from 29 patients were analyzed using CTTA. For each lymph node, CTTA was performed using a research software “TexRAD” by drawing a region of interest (ROI) on all available axial contrast-enhanced computed tomography (CT) slices covering the entire volume of the lymph node. Lymph node CTTA comprised image filtration-histogram analysis undertakes two stages: the first step comprised an application of a Laplacian of Gaussian filter to highlight fine to coarse textures within the ROI, followed by a quantification of textures via histogram analysis using mean gray-level intensity from the entire volume of the lymph nodes.
RESULTS: CTTA demonstrated a statistically significant difference between the malignant and the benign lymph nodes (P = 0.001), and by binary logistic regression we obtained a sensitivity of 53% and specificity of 97% in the test population. The area under the receiver operating curve was 83.4% and reproducibility was excellent.
CONCLUSION: CTTA may be helpful in differentiating between malignant and benign lymph nodes in the mediastinum in patients suspected for lung cancer, with a low intra-observer variance.
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© The Foundation Acta Radiologica 2015.