Diagnosis of Sarcomatoid Renal Cell Carcinoma With CT: Evaluation by Qualitative Imaging Features and Texture Analysis

Schieda, Nicola; Thornhill, Rebecca E.; Al-Subhi, Maali; McInnes, Matthew D. F.; Shabana, Wael; Pol, Christian B. van der; Flood, Trevor A.

doi:10.2214/ajr.14.13279

Cited by 99 publications

(68 citation statements)

References 35 publications

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“…Most studies of CT texture analysis have focused on the assessment of neoplasms to determine tumor grade, treatment response, or survival. These studies have reported greater correspondence of heterogeneous tumor texture with higher grade of malignancy and lower overall treatment response and survival (11)(12)(13)(14)35).…”

Section: Musculoskeletal Imaging: Trabecular Ct Texture Analysis In Amentioning

confidence: 99%

Anorexia Nervosa: Analysis of Trabecular Texture with CT

et al. 2017

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Purpose:To determine indexes of skeletal integrity by using computed tomographic (CT) trabecular texture analysis of the lumbar spine in patients with anorexia nervosa and normal-weight control subjects and to determine body composition predictors of trabecular texture. Materials and Methods:This cross-sectional study was approved by the institutional review board and compliant with HIPAA. Written informed consent was obtained. The study included 30 women with anorexia nervosa (mean age 6 standard deviation, 26 years 6 6) and 30 normal-weight age-matched women (control group). All participants underwent lowdose single-section quantitative CT of the L4 vertebral body with use of a calibration phantom. Trabecular texture analysis was performed by using software. Skewness (asymmetry of gray-level pixel distribution), kurtosis (pointiness of pixel distribution), entropy (inhomogeneity of pixel distribution), and mean value of positive pixels (MPP) were assessed. Bone mineral density and abdominal fat and paraspinal muscle areas were quantified with quantitative CT. Women with anorexia nervosa and normal-weight control subjects were compared by using the Student t test. Linear regression analyses were performed to determine associations between trabecular texture and body composition. Results:Women with anorexia nervosa had higher skewness and kurtosis, lower MPP (P , .001), and a trend toward lower entropy (P = .07) compared with control subjects. Bone mineral density, abdominal fat area, and paraspinal muscle area were inversely associated with skewness and kurtosis and positively associated with MPP and entropy. Texture parameters, but not bone mineral density, were associated with lowest lifetime weight and duration of amenorrhea in anorexia nervosa. Conclusion:Patients with anorexia nervosa had increased skewness and kurtosis and decreased entropy and MPP compared with normal-weight control subjects. These parameters were associated with lowest lifetime weight and duration of amenorrhea, but there were no such associations with bone mineral density. These findings suggest that trabecular texture analysis might contribute information about bone health in anorexia nervosa that is independent of that provided with bone mineral density.q RSNA, 2016

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Section: Musculoskeletal Imaging: Trabecular Ct Texture Analysis In Amentioning

confidence: 99%

Anorexia Nervosa: Analysis of Trabecular Texture with CT

et al. 2017

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“…The use of a texture analysis applied to imaging studies including CT and MRI have been previously performed for the evaluation of multiple nonneoplastic disorders including the evaluation for mesial temporal sclerosis on MRI, evaluation of intervertebral disc disease on MRI, evaluation of hepatic fibrosis on both CT and MRI, evaluation of subchondral bone on MRI . Prior oncologic studies have also employed texture analyses to evaluate specific tumor features including the assessment of HPV status of oropharyngeal squamous cell carcinomas, prognosis of head and neck neoplasms, classification of gastric and colorectal tumors on CT, genomic mapping and predictive marker identification of gliomas on MRI, the identification of potentially prognostic predictors in lung cancer, evaluation of genitourinary neoplasms on both CT and MRI, and for the radiomic classifications of breast carcinoma subtypes …”

Section: Introductionmentioning

confidence: 99%

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

Buch

Kuno

Qureshi

et al. 2018

J Applied Clin Med Phys

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ObjectivesTo evaluate the influence of MRI scanning parameters on texture analysis features.MethodsPublicly 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).ResultsSignificant 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).ConclusionSignificant differences were seen in many texture features with variations in MRI acquisition emphasizing the need for standardized MRI technique.

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“…Texture analysis enables description of tissue heterogeneity, a property believed to influence the outcome of cancer treatment 5 , which has led to applications in treatment response evaluation 6, 7, 5, 8 . Haralick texture features 1, 9, 10 calculated from a gray level co-occurrence matrix (GLCM) is a common method to represent image texture, as it is simple to implement and results in a set of interpretable texture descriptors 1, 11 Although a large and increasing number of studies uses Haralick’s features to analyze texture in magnetic resonance images (MRI) and images from other modalities 9, 12–15 there is no standardized way of performing these analyzes 13 . For example, GLCM texture analysis requires that the images must be quantized to a given number of gray levels 1 .…”

Section: Introductionmentioning

confidence: 99%

Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters

Brynolfsson

Nilsson

Torheim

et al. 2017

Sci Rep

124

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In recent years, texture analysis of medical images has become increasingly popular in studies investigating diagnosis, classification and treatment response assessment of cancerous disease. Despite numerous applications in oncology and medical imaging in general, there is no consensus regarding texture analysis workflow, or reporting of parameter settings crucial for replication of results. The aim of this study was to assess how sensitive Haralick texture features of apparent diffusion coefficient (ADC) MR images are to changes in five parameters related to image acquisition and pre-processing: noise, resolution, how the ADC map is constructed, the choice of quantization method, and the number of gray levels in the quantized image. We found that noise, resolution, choice of quantization method and the number of gray levels in the quantized images had a significant influence on most texture features, and that the effect size varied between different features. Different methods for constructing the ADC maps did not have an impact on any texture feature. Based on our results, we recommend using images with similar resolutions and noise levels, using one quantization method, and the same number of gray levels in all quantized images, to make meaningful comparisons of texture feature results between different subjects.

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Diagnosis of Sarcomatoid Renal Cell Carcinoma With CT: Evaluation by Qualitative Imaging Features and Texture Analysis

Abstract: Large tumor size, the presence of peritumoral neovascularity, and larger peritumoral vessels are features that are more commonly associated with sarcomatoid RCCs than with clear cell RCCs. Sarcomatoid RCCs are also more heterogeneous by texture analysis than clear cell RCCs.

Cited by 99 publications

References 35 publications

Anorexia Nervosa: Analysis of Trabecular Texture with CT

Anorexia Nervosa: Analysis of Trabecular Texture with CT

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

Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters

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