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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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  • “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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