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 outperformed 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.

/pdf/development-and-evaluation-of-an-open-source-software-1s0h12kp0o.pdf

Development and evaluation of an open-source software package "CGITA" for quantifying tumor heterogeneity with molecular images.

BACKGROUND AND PURPOSE: Human papillomavirus–associated oropharyngeal squamous cell carcinoma is increasing in prevalence and typically occurs in younger patients than human papillomavirus–negative squamous cell carcinoma. While imaging features of human papillomavirus–positive versus human papillomavirus–negative squamous cell carcinoma nodal metastases have been described, characteristics distinguishing human papillomavirus–positive from human papillomavirus–negative primary squamous cell carcinomas have not been well established. The purpose of this project was to evaluate the use of texture features to distinguish human papillomavirus–positive and human papillomavirus–negative primary oropharyngeal squamous cell carcinoma. MATERIALS AND METHODS: Following institutional review board approval, 40 patients with primary oropharyngeal squamous cell carcinoma and known human papillomavirus status who underwent contrast-enhanced CT between December 2009 and October 2013 were included in this study. Segmentation of the primary lesion was manually performed with a semiautomated graphical-user interface. Following segmentation, an in-house-developed texture analysis program extracted 42 texture features from each segmented volume. A t test was used to evaluate differences in texture parameters between human papillomavirus–positive and human papillomavirus–negative squamous cell carcinomas. RESULTS: Of the 40 included patients, 29 had human papillomavirus–positive oropharyngeal squamous cell carcinoma and 11 had human papillomavirus–negative oropharyngeal squamous cell carcinoma. Significant differences were seen in the histogram parameters median (P = .006) and entropy (P = .016) and squamous cell carcinoma entropy (P = .043). CONCLUSIONS: There are statistically significant differences in some texture features between human papillomavirus–positive and human papillomavirus–negative oropharyngeal tumors. Texture analysis may be considered an adjunct to the evaluation of human papillomavirus status and characterization of squamous cell carcinoma.

http://www.ajnr.org/content/ajnr/36/7/1343.full.pdf

Using Texture Analysis to Determine Human Papillomavirus Status of Oropharyngeal Squamous Cell Carcinomas on CT

Using texture analyses of contrast enhanced CT to assess hepatic fibrosis.

OBJECTIVES To evaluate the influence of MRI scanning parameters on texture analysis features. METHODS Publicly available data from the Reference Image Database to Evaluate Therapy Response (RIDER) project sponsored by The Cancer Imaging Archive included MRIs on a phantom comprised of 18 25-mm doped, gel-filled tubes, and 1 20-mm tube containing 0.25 mM Gd-DTPA (EuroSpinII Test Object5, Diagnostic Sonar, Ltd, West Lothian, Scotland). MRIs performed on a 1.5 T GE HD, 1.5 T Siemens Espree (VB13), or 3.0 T GE HD with TwinSpeed gradients with an eight-channel head coil included T1WIs with multiple flip angles (flip-angle = 2,5,10,15,20,25,30), TR/TE = 4.09-5.47/0.90-1.35 ms, NEX = 1 and DCE with 30° flip-angle, TR/TE=4.09-5.47/0.90-1.35, and NEX = 1,4. DICOM data were imported into an in-house developed texture analysis program which extracted 41-texture features including histogram, gray-level co-occurrence matrix (GLCM), and gray-level run-length (GLRL). Two-tailed t tests, corrected for multiple comparisons (Q values) were calculated to compare changes in texture features with variations in MRI scanning parameters (magnet strength, flip-angle, number of excitations (NEX), scanner platform). RESULTS Significant differences were seen in histogram features (mean, median, standard deviation, range) with variations in NEX (Q = 0.003-0.045) and scanner platform (Q < 0.0001), GLCM features (entropy, contrast, energy, and homogeneity) with NEX (Q = 0.001-0.018) and scanner platform (Q < 0.0001), GLRL features (long-run emphasis, high gray-level run emphasis, high gray-level emphasis) with magnet strength (Q = 0.0003), NEX (Q = 0.003-0.022) and scanner platform (Q < 0.0001). CONCLUSION Significant differences were seen in many texture features with variations in MRI acquisition emphasizing the need for standardized MRI technique.

https://aapm.onlinelibrary.wiley.com/doi/pdf/10.1002/acm2.12482

Quantitative variations in texture analysis features dependent on MRI scanning parameters: A phantom model

Quantifying liver fibrosis through the application of texture analysis to diffusion weighted imaging.

In preclinical research, allergic asthma is investigated in rats sensitised with the antigen ovalbumin (OVA), followed by a challenge with aerosolised OVA to induce an inflammatory reaction of the lower airways. This causes diffuse, nonfocal ventilation defects that lead to heterogeneously distributed signal intensities in hyperpolarised (HP) (3)He MR images, which are difficult to assess directly by diagnostic grading or volumetry. Texture analysis can characterise these changes and does not require segmentation of the lung structures prior to the analysis. The aim of this work was to evaluate a texture analysis approach to quantify changes in lung ventilation in HP (3)He MRI of OVA-challenged rats. OVA-challenged animals were treated with two different compound doses to evaluate the sensitivity of the texture analysis. Four groups were investigated using HP (3)He MRI at 4.7 T: controls, vehicle-treated, and low- and high-dose budesonide-treated rats. In addition, broncho-alveolar lavage was performed and the eosinophil cell count was used as a biological reference marker. First-order texture, geometrical features and features based on second-order statistics using run-length and grey-level co-occurrence matrices were calculated. In addition, wavelet transforms were applied to compute first-order statistics on multiple scales. The texture analysis was able to show significant differences between the control and untreated vehicle groups as well as between the vehicle and treatment groups. This is in agreement with the findings of the eosinophil cell counts, which were used as a marker for the severity of inflammation. However, not all features used in the different texture analysis methods could differentiate between the treatment groups. In conclusion, texture analysis can be used to quantify changes in lung ventilation as measured with HP (3)He MRI after therapeutic intervention with budesonide.

A texture analysis approach to quantify ventilation changes in hyperpolarised ³He MRI of the rat lung in an asthma model.

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A texture analysis approach to quantify ventilation changes in hyperpolarised ³He MRI of the rat lung in an asthma model.