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Objective. Image texture information was extracted from enhanced magnetic resonance imaging (MRI) and pathological hematoxylin and eosin- (HE-) stained images of female breast cancer patients. We established models individually, and then, we combine the two kinds of data to establish model. Through this method, we verified whether sufficient information could be obtained from enhanced MRI and pathological slides to assist in the determination of epidermal growth factor receptor (EGFR) mutation status in patients. Methods. We obtained enhanced MRI data from patients with breast cancer before treatment and selected diffusion-weighted imaging (DWI), T1 fast-spin echo (T1 FSE), and T2 fast-spin echo (T2 FSE) as the data sources for extracting texture information. Imaging physicians manually outlined the 3D regions of interest (ROIs) and extracted texture features according to the gray level cooccurrence matrix (GLCM) of the images. For the HE staining images of the patients, we adopted a specific normalization algorithm to simulate the images dyed with only hematoxylin or eosin and extracted textures. We extracted texture features to predict the expression of EGFR. After evaluating the predictive power of each model, the models from the two data sources were combined for remodeling. Results. For enhanced MRI data, the modeling of texture information of T1 FSE had a good predictive effect for EGFR mutation status. For pathological images, eosin-stained images can achieve a better prediction effect. We selected these two classifiers as the weak classifiers of the final model and obtained good results (training group: AUC, 0.983; 95% CI, 0.95-1.00; accuracy, 0.962; specificity, 0.936; and sensitivity, 0.979; test group: AUC, 0.983; 95% CI, 0.94-1.00; accuracy, 0.943; specificity, 1.00; and sensitivity, 0.905). Conclusion. The EGFR mutation status of patients with breast cancer can be well predicted based on enhanced MRI data and pathological data. This helps hospitals that do not test the EGFR mutation status of patients with breast cancer. The technology gives clinicians more information about breast cancer, which helps them make accurate diagnoses and select suitable treatments. [ABSTRACT FROM AUTHOR]

Correlation of parametrized image texture features (ITF) analyses conducted in different regions of interest (ROIs) overcomes limitations and reliably reflects image quality. The aim of this study is to propose a nonparametrical method and classify the quality of a magnetic resonance (MR) image that has undergone controlled degradation by using textural features in the image. Images of 41 patients, 17 women and 24 men, aged between 23 and 56 years were analyzed. T2-weighted sagittal sequences of the lumbar spine, cervical spine, and knee and T2-weighted coronal sequences of the shoulder and wrist were generated. The implementation of parallel imaging with the use of GRAPPA2, GRAPPA3, and GRAPPA4 led to a substantial reduction in the scanning time but also degraded image quality. The number of degraded image textural features was correlated with the scanning time. Longer scan times correlated with markedly higher ITF image persistence in comparison with images computed with reduced scan times. Higher ITF preservation was observed in images of bones in the spine and femur as compared to images of soft tissues, i.e., tendons and muscles. Finally, a nonparametrized image quality assessment based on an analysis of the ITF, computed for different tissues, correlating with the changes in acquisition time of the MR images, was successfully developed. The correlation between acquisition time and the number of reproducible features present in an MR image was found to yield the necessary assumptions to calculate the quality index. [ABSTRACT FROM AUTHOR]

Purpose. To noninvasively assess liver fibrosis using combined-contrast-enhanced (CCE) magnetic resonance imaging (MRI) and texture analysis. Materials and Methods. In this IRB-approved, HIPAA-compliant prospective study, 46 adults with newly diagnosed HCV infection and recent liver biopsy underwent CCE liver MRI following intravenous administration of superparamagnetic iron oxides (ferumoxides) and gadolinium DTPA (gadopentetate dimeglumine). The image texture of the liver was quantified in regions-of-interest by calculating 165 texture features. Liver biopsy specimens were stained with Masson trichrome and assessed qualitatively (METAVIR fibrosis score) and quantitatively (% collagen stained area). Using L 1 regularization path algorithm, two texture-based multivariate linear models were constructed, one for quantitative and the other for quantitative histology prediction. The prediction performance of each model was assessed using receiver operating characteristics (ROC) and correlation analyses. Results. The texture-based predicted fibrosis score significantly correlated with qualitative (r = 0.698, P < 0.001) and quantitative (r = 0.757, P < 0.001) histology. The prediction model for qualitative histology had 0.814-0.976 areas under the curve (AUC), 0.659-1.000 sensitivity, 0.778-0.930 specificity, and 0.674-0.935 accuracy, depending on the binary classification threshold. The prediction model for quantitative histology had 0.742-0.950 AUC, 0.688-1.000 sensitivity, 0.679-0.857 specificity, and 0.696-0.848 accuracy, depending on the binary classification threshold. Conclusion. CCE MRI and texture analysis may permit noninvasive assessment of liver fibrosis. [ABSTRACT FROM AUTHOR]

Background. The quantification of tumor heterogeneity with molecular images, by analyzing the local or global variation in the spatial arrangements of pixel intensity with texture analysis, possesses a great clinical potential for treatment planning and prognosis. To address the lack of available software for computing the tumor heterogeneity on the public domain, we develop a software package, namely, Chang-Gung Image Texture Analysis (CGITA) toolbox, and provide it to the research community as a free, open-source project. Methods. With a user-friendly graphical interface, CGITA provides users with an easy way to compute more than seventy heterogeneity indices. To test and demonstrate the usefulness of CGITA, we used a small cohort of eighteen locally advanced oral cavity (ORC) cancer patients treated with definitive radiotherapies. Results. In our case study of ORC data, we found that more than ten of the current implemented heterogeneity indices outper formed SUVmean for outcome prediction in the ROC analysis with a higher area under curve (AUC). Heterogeneity indices provide a better area under the curve up to 0.9 than the SUVmean and TLG (0.6 and 0.52, resp.). Conclusions. CGITA is a free and open-source software package to quantify tumor heterogeneity from molecular images. CGITA is available for free for academic use at http://code.google.com/p/cgita. [ABSTRACT FROM AUTHOR]