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

Deep Learning: Radiomics Imaging Pearls - Educational Tools | CT Scanning | CT Imaging | CT Scan Protocols - CTisus
Imaging Pearls ❯ Deep Learning ❯ Radiomics

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  • “Texture analysis has a potential role in distinguishing benign from malignant adrenal nodules on CECT and may decrease the need for additional imaging studies in the workup of incidentally discovered adrenal nodules.”
    Can Texture Analysis Be Used to Distinguish Benign From Malignant Adrenal Nodules on Unenhanced CT, Contrast-Enhanced CT, or In- Phase and Opposed-Phase MRI?
    Ho LM et al.
    AJR 2019; 212:554–561
  • “Current imaging methods can diagnose lipid-rich adenomas with the use of either unenhanced CT or chemical-shift MRI and can diagnose lipid-poor adenomas on the basis of calculation of the percentage washout on contrast-enhanced CT (CECT).”
    Can Texture Analysis Be Used to Distinguish Benign From Malignant Adrenal Nodules on Unenhanced CT, Contrast-Enhanced CT, or In- Phase and Opposed-Phase MRI?
    Ho LM et al.
    AJR 2019; 212:554–561
  • “Image-based texture analysis is a quantitative technique that provides a measure of lesion heterogeneity on the basis of local variations in image brightness. First-order statistics- based texture analysis evaluates the number of pixels that have a particular gray-level value within a defined ROI. First-order texture analysis does not account for the location of the pixels within the ROI. Second-order statistics- based texture analysis evaluates the location and spatial interrelationship s between pixels of variable gray-level values.”
    Can Texture Analysis Be Used to Distinguish Benign From Malignant Adrenal Nodules on Unenhanced CT, Contrast-Enhanced CT, or In- Phase and Opposed-Phase MRI?
    Ho LM et al.
    AJR 2019; 212:554–561
  • For example, first-order texture analysis can determine how many pixels have attenuation of 0 HU within an adrenal nodule. Second-order texture analysis can determine whether those pixels with an attenuation of 0 HU within an adrenal nodule are distributed evenly or are clustered in groups.
    Can Texture Analysis Be Used to Distinguish Benign From Malignant Adrenal Nodules on Unenhanced CT, Contrast-Enhanced CT, or In- Phase and Opposed-Phase MRI?
    Ho LM et al.
    AJR 2019; 212:554–561
  • “Texture analysis of CECT images showed higher diagnostic performance for the diagnosis of malignancy, compared with CECT attenuation. The performance of select individual CECT texture features (long-run high gray-level emphasis, entropy, and short-run low gray-level emphasis) were comparable to unenhanced attenuation on CT and the SII on MRI, which are the standard diagnostic imaging tests used to distinguish adrenal adenomas from metastases in clinical practice.”
    Can Texture Analysis Be Used to Distinguish Benign From Malignant Adrenal Nodules on Unenhanced CT, Contrast-Enhanced CT, or In- Phase and Opposed-Phase MRI?
    Ho LM et al.
    AJR 2019; 212:554–561
  • Increased tumor heterogeneity is the most likely reason for the ability of texture analysis to predict adrenal malignancy on CECT. As is seen in Figure 2, lipid-poor adenomas appeared homogeneous on CECT, compared with malignant lesions, which appeared heterogeneous. We speculate that the administration of contrast material may make lipid- poor adenomas appear more homogeneous because both lipid-rich and lipid-poor areas will have uptake of contrast medium.
    Can Texture Analysis Be Used to Distinguish Benign From Malignant Adrenal Nodules on Unenhanced CT, Contrast-Enhanced CT, or In- Phase and Opposed-Phase MRI?
    Ho LM et al.
    AJR 2019; 212:554–561
  • “Malignant adrenal lesions become more heterogeneous after contrast material administration because of tumor angiogenesis and increased conspicuity of tumor necrosis. In support of our theory, a recent study by Sasaguri et al. showed that adrenal metastases from renal carcinoma showed visibly higher internal heterogeneity, compared with benign adrenal masses on CECT.”
    Can Texture Analysis Be Used to Distinguish Benign From Malignant Adrenal Nodules on Unenhanced CT, Contrast-Enhanced CT, or In- Phase and Opposed-Phase MRI?
    Ho LM et al.
    AJR 2019; 212:554–561
  • Another limitation of the present study is the retrospective nature of the data acquisition. Because this is an observational study, the type of scanner used for each patient was not controlled. One cannot underestimate the potential impact of variation in CT and MR image quality on the results of texture analysis. This factor alone represents a major challenge when one considers the robustness of applying texture analysis in the clinical setting.
    Can Texture Analysis Be Used to Distinguish Benign From Malignant Adrenal Nodules on Unenhanced CT, Contrast-Enhanced CT, or In- Phase and Opposed-Phase MRI?
    Ho LM et al.
    AJR 2019; 212:554–561
  • “The introduction of radiomics has brought with it the vast expansion of the promise of quantitative and objective assessment of images. Interpretations are no longer limited to features like area, volume, and histogram-derived metrics; they can include hundreds of different features including shape, gray-level run-length matrices, Haralick texture, het- erogeneity, coarseness, or busyness.”
    How Can Radiomics Be Consistently Applied across Imagers and Institutions?
    Steiger P, Sood R
    Radiology (in press)
  • It allows for correction of radiomic measurements on the basis of their distribution and knowledge of covariates. The authors tested their method with one publicly available phantom data set and two patient data sets from patients with lung cancer. They convincingly showed that their method reduced im- ager-induced variability without sacrificing diagnostic sensitivity. Their article explains the method clearly and pro- vides all the references needed to replicate the work. This should encourage others to apply this method and test it in other radiomics studies and applications.
    How Can Radiomics Be Consistently Applied across Imagers and Institutions?
    Steiger P, Sood R
    Radiology (in press)
  • “In summary, the results of the present study indicate that the use of texture analysis for evaluation of adrenal nodules works best with CECT. This finding suggests that CT texture analysis may have a potential role in distinguishing benign lipid-poor ad- enomas from adrenal malignancy on single- phase CECT. Furthermore, the application of texture analysis may potentially decrease the need for additional imaging studies to workup incidentally discovered adrenal nodules.”
    Can Texture Analysis Be Used to Distinguish Benign From Malignant Adrenal Nodules on Unenhanced CT, Contrast-Enhanced CT, or In- Phase and Opposed-Phase MRI?
    Ho LM et al.
    AJR 2019; 212:554–561
  • “Radiomics holds the promise to become a tool at the disposal of the radiologist to expand the qualitative interpretation of the image, with additional quantitative information that can provide functional and prospective information not evident from the image alone. More studies are needed to fulfill this promise. The proposed algorithm has been shown to be effective in both thin- and thick-section CT images.”
    How Can Radiomics Be Consistently Applied across Imagers and Institutions?
    Steiger P, Sood R
    Radiology (in press)
  • “Therefore, the general success of radiomics in lung cancer and oncology will in part depend on the development and adoption of tailored image acquisition techniques for quantitative feature analysis. Radiomics will benefit from an extension of efforts already underway to standardize quantitative imaging, spearheaded by the Quantitative Imaging Biomarkers Alliance.”
    How Can Radiomics Be Consistently Applied across Imagers and Institutions?
    Steiger P, Sood R
    Radiology (in press)
  • ”Substantial hurdles remain until radiomics can become a routine tool in the radiology reading room of the future, as eloquently explained by Gillies et al. Among them is the need to validate any radiomics biomarkers in prospective multicenter studies. The variability introduced by the wide variety of avail- able equipment and imaging protocols must be controlled to allow these radiomic biomarkers to be used in a broader manner. The method presented by Orlhac et al. may have an important role in this research.”
    How Can Radiomics Be Consistently Applied across Imagers and Institutions?
    Steiger P, Sood R
    Radiology (in press)
  • “Substantial hurdles remain until radiomics can become a routine tool in the radiology reading room of the future, as eloquently explained by Gillies et al (1). Among them is the need to validate any radiomics biomarkers in prospective multicenter studies. The variability introduced by the wide variety of available equipment and imaging protocols must be controlled to allow these radiomic biomarkers to be used in a broader manner. The method presented by Orlhac et al (2) may have an important role in this research.”
    How Can Radiomics Be Consistently Applied across Imagers and Institutions?
    Peter Steiger, Rohit Sood
    Radiology 2019; 00:1–2
  • “The introduction of radiomics has brought with it the vast expansion of the promise of quantitative and objective assessment of images. Interpretations are no longer limited to features like area, volume, and histogram-derived metrics; they can include hundreds of different features including shape, gray-level run-length matrices, Haralick texture, heterogeneity, coarseness, or busyness (1). Putting such higher dimension image characteristics into the context of increasingly accessible clinical information about patients holds promise for evidence-based clinical decision support.”
    How Can Radiomics Be Consistently Applied across Imagers and Institutions?
    Peter Steiger, Rohit Sood
    Radiology 2019; 00:1–2
  • In this issue of Radiology, Orlhac et al (2) adapt a method originally used in genomics to correct variations in radiomic measurements caused by different imagers and imaging protocols (2). The proposed method is based on a statistical method called ComBat, which is readily available in the open-source R statistical programming language (R Foundation for Statistical Computing, Vienna, Austria). Unlike other previously published methods, this approach does not require images to be modified.
    How Can Radiomics Be Consistently Applied across Imagers and Institutions?
    Peter Steiger, Rohit Sood
    Radiology 2019; 00:1–2
  • Background: Radiomics extracts features from medical images more precisely and more accurately than visual assessment. However, radiomics features are affected by CT scanner parameters such as reconstruction kernel or section thickness, thus obscuring underlying biologically important texture features.
    Conclusion: Image compensation successfully realigned feature distributions computed from different CT imaging protocols and should facilitate multicenter radiomic studies.
    Validation of A Method to Compensate Multicenter Effects Affecting CT Radiomics
    Orlhac F et al.
    Radiology 2019; 00:1–7
  • “Radiomics extracts features from medical images that quantify tumor shape, intensity histogram, and texture of the lesions more precisely and more accurately than visual assessment by a radiologist to build models that involve features to assist patient treat- ment. In particular, texture analysis from CT images has led to promising results to distinguish between tumor lesions with different histopathologic characteristics and to predict treatment response or patient survival.”
    Validation of A Method to Compensate Multicenter Effects Affecting CT Radiomics
    Orlhac F et al.
    Radiology 2019; 00:1–7
  • Key points
    * Radiomic feature values obtained by using different CT imaging protocols or scanners can be corrected for the protocol or scanner effect by using the proposed compensation method.
    * The use of realigned features will enable multicentric studies to pool data from different sites to build reliable radiomic models based on large databases.
    * The proposed compensation method is easily available, fast, and requires neither phantom acquisition nor feature recalculation.
    Validation of A Method to Compensate Multicenter Effects Affecting CT Radiomics
    Orlhac F et al.
    Radiology 2019; 00:1–7
  • “Nonbiological differences related to CT scanner type can be removed from radiomic feature values, allowing radiomics features to be combined in multicenter or multivendor studies.”
    Validation of A Method to Compensate Multicenter Effects Affecting CT Radiomics
    Orlhac F et al.
    Radiology 2019; 00:1–7
  • “To correct for differences in features caused by the various imaging protocols, we used the ComBat function (https://github.com/Jfortin1/ComBatHarmonization) compensation method.”
    Validation of A Method to Compensate Multicenter Effects Affecting CT Radiomics
    Orlhac F et al.
    Radiology 2019; 00:1–7
  • “In conclusion, ComBat makes it possible to pool radiomic features from different CT protocols. This method appears promising to address the center effect in multicenter radiomic studies and to possibly raise the statistical power of those studies. ComBat is data driven, which means that the transformations identified by ComBat to set all data in a common space should be estimated for each study involving data from different cen- ters and protocols.”
    Validation of A Method to Compensate Multicenter Effects Affecting CT Radiomics
    Orlhac F et al.
    Radiology 2019; 00:1–7
  • “Moreover, radiomics-predicted lymph node metastasis emerged as a preoperative predictor of both disease-specific survival and recurrence-free survival after curative intent resection of biliary tract cancers (hazard ratios, 3.37 and 1.98, respectively). Overall, there was important personalized information for medical decision support.”
    CT-based Radiomics for Biliary Tract Cancer: A Possible Solution for Predicting Lymph Node Metastases
    Laghi A, Voena C
    Radiology 2019; 290:99–100
  • There are limitations. Although the model was built with rigorous methodologic structure, a multicentric study collecting a larger number of patients would be necessary to check for the generalizability of the radiomics signature. The influence of different CT parameters (eg, kilovolt, milliampere-seconds, and reconstruction filters) on extraction of radiomics features was not among the objectives of this study, although this is a relevant variable that might affect data consistency and limit the extensive use of the model.
    CT-based Radiomics for Biliary Tract Cancer: A Possible Solution for Predicting Lymph Node Metastases
    Laghi A, Voena C
    Radiology 2019; 290:99–100
  • A correlation with genomic profile of biliary tract cancers may have been desirable, especially in the era of target therapy where specific genomic profiles are associated with either response or resistance to a specific drug. Nevertheless, radiomics approaches seem to have a bright future, especially if collaborative multidisciplinary teams are involved. Ultimately, to achieve personalized medicine, personalized imaging must be involved.
    CT-based Radiomics for Biliary Tract Cancer: A Possible Solution for Predicting Lymph Node Metastases
    Laghi A, Voena C
    Radiology 2019; 290:99–100
  • “Ultimately, to achieve personalized medicine, personalized imaging must be involved."
    CT-based Radiomics for Biliary Tract Cancer: A Possible Solution for Predicting Lymph Node Metastases
    Laghi A, Voena C
    Radiology 2019; 290:99–100
  • “The further goal of radiomics analytics is to develop decision support tools, such as predictive models, by incorporating radiomics signature and other morphologic features. Radiomics models providing individualized risk estimation of LN metastasis have been developed and validated in studies focused on esophageal, colorectal, and bladder cancers with good results."
    CT-based Radiomics for Biliary Tract Cancer: A Possible Solution for Predicting Lymph Node Metastases
    Laghi A, Voena C
    Radiology 2019; 290:99–100
  • "Radiomics uses advanced image-processing techniques to extract a large number of quantitative parameters from imaging data, and its potential to improve diagnostic accuracy is increasingly being studied . Initial studies have reported promising performance of radiomics with and without the use of machine learning in the prediction of the prostate cancer Gleason score."
    Radiomic Machine Learning for Characterization of Prostate Lesions with MRI: Comparison to ADC Values
    Bonekamp D et al.
    Radiology 2018 (in press)
  • "In conclusion, this study compared the use of mean ADC and radiomics with machine learning for the characterization of lesions that were prospectively detected during routine clinical interpretation.
    Quantitative assessment of the mean ADC was more accurate than qualitative PI-RADS assessment in classifying a lesion as clinically significant prostate cancer. Radiomics provided additional data that ADC metrics (including mean ADC) were more valuable than other MRI features. In fact, at the current cohort size, no added benefit of the radiomic approach was found, and mean ADC is suggested as the best choice for quantitative prostate assessment."
    Radiomic Machine Learning for Characterization of Prostate Lesions with MRI: Comparison to ADC Values
    Bonekamp D et al.
    Radiology 2018 (in press)
  • Purpose: To determine the feasibility of using a deep learning approach to detect cartilage lesions (including cartilage softening, fibrillation, fissuring, focal defects, diffuse thinning due to cartilage degeneration, and acute cartilage injury) within the knee joint on MR images.
    Conclusion: is study demonstrated the feasibility of using a fully automated deep learning–based cartilage lesion detection system to evaluate the articular cartilage of the knee joint with high diagnostic performance and good intraobserver agreement for detecting cartilage degeneration and acute cartilage injury.
    Deep Learning Approach for Evaluating Knee MR Images: Achieving High Diagnostic Performance for Cartilage Lesion Detection
    FangLiu et al.
    Radiology 2018 (in press)
  • “Radiomics is a process that extracts a large number of quantitative features from medical images. It can potentially be applied to any medical condition, but it is currently applied mostly in oncology for quantification of tumour phenotype and for development of decision support tools. Deep learning and convolutional neural networks have the potential to automatically extract the significant features from images to help predict an important outcome (eg, cancer-specific mortality).”

    
Canadian Association of Radiologists White Paper 
on Artificial Intelligence in Radiology
An Tang et al. 
Canadian Association of Radiologists Journal 69 (2018) 120e135 

  • “With technological advances in computer science, it is anticipated that an increasing number of repetitive tasks will be automated over time. The PACS of all hospitals contain large imaging datasets with matching descriptions within radiology reports that can be used to perform ML on very large scale. The interactions between radiology images and their reports have been used to train ML for automated detection of disease in images [56]. Of note, a recent review of deep learning revealed that many recent applications in medical image analysis focus on 2D convolutional neural networks which do not directly leverage 3D information [57]. While 3D convolutional neural networks are emerging for analysis of multiplanar imaging (eg, CT), further research will be required to analyze multiparametric imaging examinations (eg, MRI).”


    Canadian Association of Radiologists White Paper 
on Artificial Intelligence in Radiology
An Tang et al. 
Canadian Association of Radiologists Journal 69 (2018) 120e135 

  • “AI techniques have been steadily developed since 1955 but recently have undergone a resurgence due to breakthrough performance arising from a combination of factors: wide availability of labeled data, advances in neural network architectures, and availability of parallel computing hardware. In radiology, AI applications currently focus on anomaly detection, segmentation, and classification of images. Familiarity with the terminology and key concepts in this field will allow the radiology community to critically analyze the opportunities, pitfalls, and challenges associated with the introduction of these new tools. Radiologists should become actively involved in research and development in collaboration with key stakeholders, scientists, and industrial partners to ensure radiologist oversight in the definition of use cases and validation process, and in the clinical application for patient care. Residency programs should integrate health informatics and computer science courses in AI in their curriculum.”


    Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology
An Tang et al. 
Canadian Association of Radiologists Journal 69 (2018) 120e135 

  • “Radiomics (or radiogenomics) is the correlation between the imaging appearance of cancer and the genomics of such. Advances in traditional machine learning and more novel deep learning approaches in this area have shown promising results. Moreover, deep learning techniques has achieved state-of-the-art results in biomedical image segmentation, which can be used to automatically segment and extract volumes of organs, specific tissues, and regions of interest. The radiology report of the future may automatically include such quantitative information, which could be used to assess disease and guide treatment decisions.”


    Machine Learning in Radiology: 
Applications Beyond Image Interpretation 
Paras Lakhani et al.
J Am Coll Radiol (in press)
  • “Ultimately, machine learning has the potential to dramatically improve patient care. Importantly for radiologists, machine learning algorithms can help address many problems in current-day radiology practices that do not involve image interpretation. Although much of the attention in the machine learning space has focused on the ability of machines to classify image findings, there are many other useful applications of machine learning that will improve efficiency and utilization of radiology practices today.”


    Machine Learning in Radiology: 
Applications Beyond Image Interpretation 
Paras Lakhani et al.
J Am Coll Radiol (in press)
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