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A review of potential applications by TexRAD users from leading Universities across the USA – Feedback (FDBK)
Authors: Lubber MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ.
ABSTRACT: This review discusses potential oncologic and nononcologic applications of CT texture analysis ( CTTA CT texture analysis ), an emerging area of “radiomics” that extracts, analyzes, and interprets quantitative imaging features. CTTA CT texture analysis allows objective assessment of lesion and organ heterogeneity beyond what is possible with subjective visual interpretation and may reflect information about the tissue microenvironment. CTTA CT texture analysis has shown promise in lesion characterization, such as differentiating benign from malignant or more biologically aggressive lesions. Pretreatment CT texture features are associated with histopathologic correlates such as tumor grade, tumor cellular processes such as hypoxia or angiogenesis, and genetic features such as KRAS or epidermal growth factor receptor (EGFR) mutation status. In addition, and likely as a result, these CT texture features have been linked to prognosis and clinical outcomes in some tumor types. CTTA CT texture analysis has also been used to assess response to therapy, with decreases in tumor heterogeneity generally associated with pathologic response and improved outcomes.
A variety of nononcologic applications of CTTA CT texture analysis are emerging, particularly quantifying fibrosis in the liver and lung. Although CTTA CT texture analysis seems to be a promising imaging biomarker, there is marked variability in methods, parameters reported, and strength of associations with biologic correlates. Before CTTA CT texture analysis can be considered for widespread clinical implementation, standardization of tumor segmentation and measurement techniques, image filtration and postprocessing techniques, and methods for mathematically handling multiple tumors and time points is needed, in addition to identification of key texture parameters among hundreds of potential candidates, continued investigation and external validation of histopathologic correlates, and structured reporting of findings.
Copyright © 2017 The RSNA. All rights reserved.
Original article link here
Correlation of CT Derived Texture Features with Mutational Status of Clear Cell Renal Carcinoma using TexRAD
Correlation of CT Derived Texture Features with Mutational Status of Clear Cell Renal Carcinoma using TexRAD – Preliminary Findings
Sahand Sohrabi and Raghunandan Vikram – Diagnostic Imaging, MD Anderson Cancer Center, Houston, Texas, USA
Purpose: To investigate role of CT Texture analysis (CTTA) and mutational status of clear cell renal cell carcinoma (ccRCC).
Materials and Methods: Multi-phase contrast enhanced CT (CECT) including Non contrast (NC), Corticomedullary (CM), nephrographic (N) and excretory (E) of 107 patients with ccRCC from the Cancer Imaging achive underwent filtration-histigram based CTTA using a commercially available research software (TexRAD Ltd). Using the DICOM images, filtration step extracted texture features using different spatial scale filters corresponding to fine, medium and coarse texture scales followed by histogram quantification: Mean gray-level pixel intensity, Entropy, Standard Deviation (SD), Mean of positive pixels (MPP), Kurtosis and Skewness. Non-parametric Mann Whitney test was used to test for significant associations in CTTA with mutational status (VHL, BAP1, PBRM1, SETD2, P13K pathway regulators.
Results: NC study showed statistically significant differences in the CT derived textual features between the mutational status of BAP1 (entropy p=0.045; SD p=0.033; MPP p=0.03), VHL (mean p=0.049, SD0.031), P13 pathway regulator (mean p=0.021, SD p=0.03, MPP p=0.03, skewness p=0.048) and PBRM1 (skewness p=0.048). Differences in CTAA measurements were seen in all phases in patients with VHL mutational status. These were most marked in the CM phase (mean p=0.031, SD p=0.006, entropy p=0.03, MPP p=0.005 and Kurtosis p=0.049). CTA measurements in Excretory phase images were useful in differentiating between BAP one mutations (entropy p=0.045, skewness p=0.035) and PBRM1 mutations (entropy p=0.023).
Conclusions: A software-based CT Texture Analysis can derive features that may help identify patients with genetic mutations in patients with ccRCC.
Dr Balaji Ganeshan, Director of New Business at Feedback Plc stated, “In our trading update of March 30th 2017, we stated that purchase orders for the well-established research version of TexRAD continued at a good rate ahead of the impending release of the first clinical version. This study is the latest in a number of published papers that serves to demonstrate the value and diversity of TexRAD for CTTA applications, and further underscores the progress highlighted in the trading update. We remain focussed on obtaining the CE mark for TexRAD Lung by the target date of May 2017.”
Abdominal Radiology – Study using texture analysis (TexRAD) of the liver at MDCT for assessing hepatic fibrosis – Feedback plc
By Lubner MG, et al. Abdom Radiol (NY). 2017. Source PubMed.
PURPOSE: To evaluate CT texture analysis (CTTA) for staging of hepatic fibrosis (stages F0-F4) METHODS: Quantitative texture analysis (QTA) of the liver was performed on abdominal MDCT scans using commercially available software (TexRAD), which uses a filtration-histogram statistic-based technique. Single-slice ROI measurements of the total liver, Couinaud segments IV-VIII, and segments I-III were obtained. CTTA parameters were correlated against fibrosis stage (F0-F4), with biopsy performed within one year for all cases with intermediate fibrosis (F1-F3).
RESULTS: The study cohort consisted of 289 adults (158M/131W; mean age, 51 years), including healthy controls (F0, n = 77), and patients with increasing stages of fibrosis (F1, n = 42; F2 n = 37; F3 n = 53; F4 n = 80). Mean gray-level intensity increased with fibrosis stage, demonstrating an ROC AUC of 0.78 at medium filtration for F0 vs F1-4, with sensitivity and specificity of 74% and 74% at cutoff 0.18. For significant fibrosis (≥F2), mean showed AUCs ranging from 0.71-0.73 across medium- and coarse- filtered textures with sensitivity and specificity of 71% and 68% at cutoff of 0.3, with similar performance also observed for advanced fibrosis (≥F3). Entropy showed a similar trend. Conversely, kurtosis and skewness decreased with increasing fibrosis, particularly in cirrhotic patients. For cirrhosis (≥F4), kurtosis and skewness showed AUCs of 0.86 and 0.87, respectively, at coarse-filtered scale, with skewness showing a sensitivity and specificity of 84% and 75% at cutoff of 1.3.
CONCLUSION: CTTA may be helpful in detecting the presence of hepatic fibrosis and discriminating between stages of fibrosis, particularly at advanced levels.
Dr Ganeshan, Director of New Business at Feedback Plc stated, “This study, and a number of others recently published in the run up to the European Congress of Radiology at the start of March serves to demonstrate the broad range of existing and potential new applications for TexRAD software. The recent high profile press coverage of MRI scan analysis for prostate cancer highlighted TexRAD’s potential to enhance early discovery of significant cancers. Similarly this study of texture analysis of the liver on routinely acquired CT scans highlights the potential of TexRAD to act as a non-invasive biomarker (tool) in the diagnosis of hepatic fibrosis and assessment of severity of the disease. This can potentially assist in minimising the need for frequent liver biopsies and additional examinations.”
Link here to view the PubMed article
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.
Original article link here
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.
Original article link here
© The Foundation Acta Radiologica 2015.