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Everything you need to know about Computed Tomography (CT) & CT Scanning

Kidney: Texture Analysis Imaging Pearls - Educational Tools | CT Scanning | CT Imaging | CT Scan Protocols - CTisus
Imaging Pearls ❯ Kidney ❯ Texture Analysis

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  • “Advances in the management of genitourinary neoplasms have resulted in a trend towards providing patients with personalized care. Texture analysis of medical images, is one of the tools that is being explored to provide information such as detection and characterization of tumors, determining their aggressiveness including grade and metastatic potential and for prediction of survival rates and risk of recurrence. In this article we review the basic principles of texture analysis and then detail its current role in imaging of individual neoplasms of the genitourinary system.”
    A review of the principles of texture analysis and its role in imaging of genitourinary neoplasms
    Richard Thomas et al.
    Abdominal Radiology (2019) 44:2501–2510
  • "Radiogenomics, which has been defined as a science of identifying associations between imaging features and genomic characteristics of a disease, assumes great importance in this era of precision medicine. There is an increasing interest in providing quantitative information along with subjective interpretation of radiologic images.”
    A review of the principles of texture analysis and its role in imaging of genitourinary neoplasms
    Richard Thomas et al.
    Abdominal Radiology (2019) 44:2501–2510
  • Texture segmentation is a technique to divide the image into various segments that differ in their broad textural properties. This can help delineate anatomical structures or pathological lesions and is often a part of computer aided diagnosis. Examples include lung nodule and colonic polyp detection. Some of the face detection softwares in our everyday social media platforms also use this technique.”
    A review of the principles of texture analysis and its role in imaging of genitourinary neoplasms
    Richard Thomas et al.
    Abdominal Radiology (2019) 44:2501–2510
  • Textural Analysis for Renal Pathology: Applications
    - Renal neoplasms (RCC vs AML)
    - Renal neoplasms (Clear Cell vs. Papillary)
    - Response to chemotherapy
    - Predicting survival
  • Limitations of texture analysis
    Texture analysis, like the field of radiogenomics in general, must deal with a problem of plenty. The numerous and varied parameters that can be assessed, combined with the small sample size in several of these studies, makes analysis both challenging and difficult to interpret. For the same reasons, these studies are prone to type I errors. Type I error is to falsely imply an association that does not exist. Chalkidou et el applied a statistical analysis method to fifteen studies that reported an association between patient outcomes and texture features on PET and CT, and identified probability of type I error to be as high as 76%.
  • Limitations of texture analysis
    - Scanner variability
    - Scan parameters including;
    -- slice thickness
    -- reconstruction filters
    -- Voxel size and gray levels
    -- radiation dose
    -- type of image reconstruction method used (filtered back projection or iterative reconstruction)
  • OBJECTIVE. The objective of our study was to investigate the potential influence of intra- and interobserver manual segmentation variability on the reliability of single-slice–based 2D CT texture analysis of renal masses.
    CONCLUSION. Single-slice–based 2D CT texture analysis of renal masses is sensitive to intra- and interobserver manual segmentation variability. Therefore, it may lead to nonreproducible results in radiomic analysis unless a reliability analysis is considered in the workflow.
    Reliability of Single-Slice–Based 2D CT Texture Analysis of Renal Masses: Influence of Intra- and Interobserver Manual Segmentation Variability on Radiomic Feature Reproducibility
    Kocak B et al.
    AJR 2019; 213:1–7
  • “Lately, texture analysis has been an active area of research in the field of radiomics, suggesting that it can be used in predicting tumor subtypes, tumor stage, tumor grade, response to treatment, genomic profile, and overall survival . Nonetheless, recent evi- dence also suggests that conclusions must be treated with caution because several texture parameters may have reproducibility problems, which is an important challenge for building reliable predictive models to be used in clinical practice.”
    Reliability of Single-Slice–Based 2D CT Texture Analysis of Renal Masses: Influence of Intra- and Interobserver Manual Segmentation Variability on Radiomic Feature Reproducibility
    Kocak B et al.
    AJR 2019; 213:1–7
  • Although whole-tumor segmentation is known to be the most representative for tumor texture , it is considered an impractical and time-consuming process to be used in clinical routine, particularly in large tumors such as renal masses. For renal tumors, there has been a trend toward using a single image slice along with manual segmentation in an attempt to bring texture analysis into a daily routine.
    Reliability of Single-Slice–Based 2D CT Texture Analysis of Renal Masses: Influence of Intra- and Interobserver Manual Segmentation Variability on Radiomic Feature Reproducibility
    Kocak B et al.
    AJR 2019; 213:1–7
  • “In conclusion, single-slice–based 2D CT texture analysis of RCCs is sensitive to intra-and interobserver manual segmentation variability, which may lead to nonreproducible results in radiomic analysis. Therefore, a reliability analysis with as much and heterogeneous data as possible must be incorporated into every scientific research study using this technique. Otherwise, the radiomic studies of renal masses without a reliability analysis might lead to a chain of nonreproducible outcomes in terms of selected texture features and statistical models created, which might further influence the generalizability and replicability of the findings of the radiomic studies. bring texture analysis into a daily routine.”
    Reliability of Single-Slice–Based 2D CT Texture Analysis of Renal Masses: Influence of Intra- and Interobserver Manual Segmentation Variability on Radiomic Feature Reproducibility
    Kocak B et al.
    AJR 2019; 213:1–7
  • In addition, CECT provides more texture features with good to excellent interobserver reliability than unenhanced CT does. Filtered and transformed images might be useful for reducing the influence of manual segmentation variations on single-slice–based 2D CT texture analysis, yielding more features with good to excellent reliability than original images do.
    Reliability of Single-Slice–Based 2D CT Texture Analysis of Renal Masses: Influence of Intra- and Interobserver Manual Segmentation Variability on Radiomic Feature Reproducibility
    Kocak B et al.
    AJR 2019; 213:1–7
  • OBJECTIVE. The purpose of this study is to evaluate the potential value of machine learning (ML)–based high-dimensional quantitative CT texture analysis in predicting the mutation status of the gene encoding the protein polybromo-1 (PBRM1) in patients with clear cell renal cell carcinoma (RCC).
    CONCLUSION. ML-based high-dimensional quantitative CT texture analysis might be a feasible and potential method for predicting PBRM1 mutation status in patients with clear cell RCC.
    Radiogenomics in Clear Cell Renal Cell Carcinoma: Machine Learning–Based High-Dimensional Quantitative CT Texture Analysis in Predicting PBRM1 Mutation Status
    Burak Kocak et al.
    AJR 2019; 212:W55–W63
  • “Quantitative CT (QCT) texture analysis (TA) is an image processing method for measuring repetitive pixel or voxel gray-level patterns that may not be perceptible with the human eye. Several texture parameters can be produced by this method, which makes QCT TA high-dimensional. Although the field of high-dimensional QCT TA is still under development, the literature suggests that QCT TA can be used for characterizing lesions or tumors, predicting staging, nuclear grading, assessing the response to treatment, and predicting survival.”
    Radiogenomics in Clear Cell Renal Cell Carcinoma: Machine Learning–Based High-Dimensional Quantitative CT Texture Analysis in Predicting PBRM1 Mutation Status
    Burak Kocak et al.
    AJR 2019; 212:W55–W63
  • “Radiogenomics is a field of radiology in- vestigating the potential associations be- tween the imaging features of a disease and the underlying genetic patterns or molecular phenotype of that disease. The field has aimed to noninvasively obtain predictive data for diagnostic, prognostic, and, ultimately, optimal therapeutic assessment.”
    Radiogenomics in Clear Cell Renal Cell Carcinoma: Machine Learning–Based High-Dimensional Quantitative CT Texture Analysis in Predicting PBRM1 Mutation Status
    Burak Kocak et al.
    AJR 2019; 212:W55–W63
  • In conclusion, ML-based high-dimensional QCT TA is a feasible and potential method for predicting PBRM1 mutation status in patients with clear cell RCC. Nonetheless, more studies with more labeled data are absolutely required for further validation and improve- ment of the method for clinical use. We hope that the present study will provide the basis for new research.
    Radiogenomics in Clear Cell Renal Cell Carcinoma: Machine Learning–Based High-Dimensional Quantitative CT Texture Analysis in Predicting PBRM1 Mutation Status
    Burak Kocak et al.
    AJR 2019; 212:W55–W63
  • “First-order texture features were extracted by histogram analysis, specifically; Kurtosis (a measure of histogram flatness), Skewness (a measure of histogram asymmetry), and Entropy (a measure of histogram irregularity) as described previously. Manual contouring of tumors was independently repeated in 17% of patients (10/60) for 17 tumors by a second fellowship-trained abdominal radiologist (NS), to assess reproducibility of segmentation.”
    Differentiation of pancreatic neuroendocrine tumors from pancreas renal cell carcinoma metastases on CT using qualitative and quantitative features
    van der Pol CB et al.
    Abdominal Radiology 2019 (in press)
  • “With respect to texture analysis features studied, entropy was significantly higher in PNETs compared to RCC metastases (6.32 ± 0.49 vs. 5.96 ± 0.53, P = 0.004) with a trend towards higher levels of kurtosis and skewness, although the difference in the latter two features did not reach statistical significance between groups (P = 0.067 and 0.099, respectively).”
    Differentiation of pancreatic neuroendocrine tumors from pancreas renal cell carcinoma metastases on CT using qualitative and quantitative features
    van der Pol CB et al.
    Abdominal Radiology 2019 (in press)
  • “The presence of tumor calcification and main pancreatic duct dilation were specific features for PNETs, whereas pancreatic RCC metastases tended to be smaller and were more frequently multiple. PNETs appeared subjectively and quantitatively more heterogeneous using texture analysis. Our results suggest that enhanced CT imaging features may accurately differentiate between PNET and pancreatic RCC metastases which may potentially obviate the need for histological sampling in select cases.”
    Differentiation of pancreatic neuroendocrine tumors from pancreas renal cell carcinoma metastases on CT using qualitative and quantitative features
    van der Pol CB et al.
    Abdominal Radiology 2019 (in press)
  • “Quantitative CT (QCT) texture analysis (TA) is an image processing method for measuring repetitive pixel or voxel gray-level patterns that may not be perceptible with the human eye. Several texture parameters can be produced by this method, which makes QCT TA high-dimensional.”
    Radiogenomics in Clear Cell
    Renal Cell Carcinoma: Machine Learning–Based High-Dimensional Quantitative CT Texture Analysis in Predicting PBRM1 Mutation Status
    Kocak B et al.
    AJR 2019; 212:1–9
  • The second most commonly identified mutation in clear cell RCC involves the tumor suppressor PBRM1 gene. A recent meta-analysis of 2942 patients from seven studies reported that a mutation in PBRM1 or decreased expression of the gene is associated with poor survival, advanced TNM categories and tumor stage, and a higher Fuhrman nuclear grade in patients with RCC.
    Radiogenomics in Clear Cell
    Renal Cell Carcinoma: Machine Learning–Based High-Dimensional Quantitative CT Texture Analysis in Predicting PBRM1 Mutation Status
    Kocak B et al.
    AJR 2019; 212:1–9
  • “In the present study, we investigated the potential value of ML-based high-dimensional QCT TA in predicting the PBRM1 mutation status of patients with clear cell RCC. The results of our study suggest that high-dimensional QCT TA using different ML classifiers (ANN and RF algorithms) has potential in distinguishing clear cell RCCs with and without the PBRM1 mutation. “
    Radiogenomics in Clear Cell
    Renal Cell Carcinoma: Machine Learning–Based High-Dimensional Quantitative CT Texture Analysis in Predicting PBRM1 Mutation Status
    Kocak B et al.
    AJR 2019; 212:1–9
  • “In conclusion, ML-based high-dimension- al QCT TA is a feasible and potential method for predicting PBRM1 mutation status in patients with clear cell RCC. Nonetheless, more studies with more labeled data are absolutely required for further validation and improvement of the method for clinical use. We hope that the present study will provide the basis for new research.”
    Radiogenomics in Clear Cell
    Renal Cell Carcinoma: Machine Learning–Based High-Dimensional Quantitative CT Texture Analysis in Predicting PBRM1 Mutation Status
    Kocak B et al.
    AJR 2019; 212:1–9
  • “CT texture features (in particular, entropy, the mean of positive pixels, and the SD of the pixel distribution histogram) are associated with tumor histologic findings, nuclear grade, and outcome measures. The contrast phase does seem to affect heterogeneity measures.”


    CT Textural Analysis of Large Primary Renal Cell Carcinomas: Pretreatment Tumor Heterogeneity Correlates With Histologic Findings and Clinical Outcomes 
Lubner MG et al
AJR 2016; 207:96–105
  • “CT texture analysis can be used to accurately differentiate fp-AML from RCC on unenhanced CT images.”

    Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images?
    Hodgdon T et al.
    Radiology. 2015 Apr 23:142215. [Epub ahead of print
  • “CTTA software was used to analyze 20 clear cell renal cell carcinomas, 20 papillary renal cell carcinomas, 20 oncocytomas, and 20 renal cysts.  Regions of interest were drawn around each mass on multiple slices in the arterial, venous, and delayed phases on renal mass protocol CT scans. Unfiltered images and spatial band-pass filtered images were analyzed to quantify heterogeneity.  Random forest method was used to construct a predictive model to classify lesions using quantitative parameters.”
    CT Texture Analysis of Renal Masses:
     Pilot study utilizing random forest classification for prediction of pathology
    Raman SP, Chen Y, Schroeder JL,Huang P, Fishman EK
    Academic Radiol (in press)
  • “The random forest model correctly categorized oncocytomas in 89% of cases (sensitivity=89%, specificity=99%), clear cell renal cell carcinomas in 91% of cases (sensitivity=91%, specificity=97%), cysts in 100% of cases (sensitivity=100%, specificity=100%), and papillary renal cell carcinomas in 100% of cases (sensitivity=100%, specificity=98%).”
    CT Texture Analysis of Renal Masses:
     Pilot study utilizing random forest classification for prediction of pathology
    Raman SP, Chen Y, Schroeder JL,Huang P, Fishman EK
    Academic Radiol (in press)
  • Renal Mass Analysis
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