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

Deep Learning: Deep Learning and the Gu Tract Imaging Pearls - Educational Tools | CT Scanning | CT Imaging | CT Scan Protocols - CTisus
Imaging Pearls ❯ Deep Learning ❯ Deep Learning and the GU Tract

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  • OBJECTIVE. The objective of our study was to systematically review the literature about the application of artificial intelligence (AI) to renal mass characterization with a focus on the methodologic quality items.
    MATERIALS AND METHODS. A systematic literature search was conducted using PubMed to identify original research studies about the application of AI to renal mass characterization. Besides baseline study characteristics, a total of 15 methodologic quality items were extracted and evaluated on the basis of the following four main categories: modeling, performance evaluation, clinical utility, and transparency items. The qualitative synthesis was presented using descriptive statistics with an accompanying narrative.
    CONCLUSION. To bring AI-based renal mass characterization from research to practice, future studies need to improve modeling and performance evaluation strategies and pay attention to clinical utility and transparency issues.
    Artificial Intelligence in Renal Mass Characterization: A Systematic Review of Methodologic Items Related to Modeling, Performance Evaluation, Clinical Utility, and Transparency
    Kocak B et al.
    AJR 2020; 215:1113–1122
  • OBJECTIVE. The objective of our study was to systematically review the literature about the application of artificial intelligence (AI) to renal mass characterization with a focus on the methodologic quality items.
    CONCLUSION. To bring AI-based renal mass characterization from research to practice, future studies need to improve modeling and performance evaluation strategies and pay attention to clinical utility and transparency issues.
    Artificial Intelligence in Renal Mass Characterization: A Systematic Review of Methodologic Items Related to Modeling, Performance Evaluation, Clinical Utility, and Transparency
    Kocak B et al.
    AJR 2020; 215:1113–1122
  • “As a broad concept, artificial intelligence (AI) covers a wide variety of machine learning (ML) methods or algorithms that create models without strict rule-based programming beforehand. These algorithms can improve and correct themselves through experience. The goal of AI tools is to predict certain outcomes using multiple variables. In the field of medical imaging, there has been extensive interest in AI tools.”
    Artificial Intelligence in Renal Mass Characterization: A Systematic Review of Methodologic Items Related to Modeling, Performance Evaluation, Clinical Utility, and Transparency
    Kocak B et al.
    AJR 2020; 215:1113–1122
  • "In this study, we systematically reviewed 30 studies about the application of AI to re- nal mass characterization. Our focus was on the methodologic quality items related to modeling, performance evaluation, clinical utility, and transparency. The quality items were favorable for modeling and perfor- mance evaluation categories for most stud- ies. On the other hand, they were poor in terms of clinical utility evaluation and transparency for most studies.”
    Artificial Intelligence in Renal Mass Characterization: A Systematic Review of Methodologic Items Related to Modeling, Performance Evaluation, Clinical Utility, and Transparency
    Kocak B et al.
    AJR 2020; 215:1113–1122

  • IMPORTANCE For prostate cancer, Gleason grading of the biopsy specimen plays a pivotal role in determining case management. However, Gleason grading is associated with substantial interobserver variability, resulting in a need for decision support tools to improve the reproducibility of Gleason grading in routine clinical practice.
    OBJECTIVE To evaluate the ability of a deep learning system (DLS) to grade diagnostic prostate biopsy specimens.
    CONCLUSIONS AND RELEVANCE In this study, the DLS showed higher proficiency than general pathologists at Gleason grading prostate needle core biopsy specimens and generalized to an independent institution. Future research is necessary to evaluate the potential utility of using the DLS as a decision support tool in clinical workflows and to improve the quality of prostate cancer grading for therapy decisions.
    Development and Validation of a Deep Learning Algorithm for Gleason Grading of Prostate Cancer From Biopsy Specimens
    Kunal Nagpal et al.
    JAMA Oncol. doi:10.1001/jamaoncol.2020.2485 Published online July 23, 2020.
  • RESULTS For grading tumor-containing biopsy specimens in the validation set (n = 498), the rate of agreement with subspecialists was significantly higher for the DLS (71.7%; 95% CI, 67.9%-75.3%) than for general pathologists (58.0%; 95% CI, 54.5%-61.4%) (P < .001). In subanalyses of biopsy specimens from an external validation set (n = 322), the Gleason grading performance of the DLS remained similar. For distinguishing nontumor from tumor-containing biopsy specimens (n = 752), the rate of agreement with subspecialists was 94.3% (95% CI, 92.4%-95.9%) for the DLS and similar at 94.7% (95% CI, 92.8%-96.3%) for general pathologists (P = .58).
    Development and Validation of a Deep Learning Algorithm for Gleason Grading of Prostate Cancer From Biopsy Specimens
    Kunal Nagpal et al.
    JAMA Oncol. doi:10.1001/jamaoncol.2020.2485 Published online July 23, 2020.
  • Key Points
    Question: How does a deep learning system for assessing prostate biopsy specimens compare with interpretations determined by specialists in urologic pathology and by general pathologists?
    Findings: In a validation dataset of 752 biopsy specimens obtained from 2 independent medical laboratories and a tertiary teaching hospital, this study found that rate of agreement with subspecialists was significantly higher for the deep learning system than it was for a cohort of general pathologists.
    Meaning: The deep learning system warrants evaluation as an assistive tool for improving prostate cancer diagnosis and treatment decisions, especially where subspecialist expertise is unavailable.
    Development and Validation of a Deep Learning Algorithm for Gleason Grading of Prostate Cancer From Biopsy Specimens
    Kunal Nagpal et al.
    JAMA Oncol. doi:10.1001/jamaoncol.2020.2485 Published online July 23, 2020.
  • “To conclude, we have presented a DLS for Gleason grading of prostate biopsy specimens that is highly concordant with sub- specialists and that maintained its performance on an external validation set. Future work will need to assess the diag- nostic and clinical effect of the use of a DLS for increasing the accuracy and consistency of Gleason grading to improve patient care.”
    Development and Validation of a Deep Learning Algorithm for Gleason Grading of Prostate Cancer From Biopsy Specimens
    Kunal Nagpal et al.
    JAMA Oncol. doi:10.1001/jamaoncol.2020.2485 Published online July 23, 2020.

  • Development and Validation of a Deep Learning Algorithm for Gleason Grading of Prostate Cancer From Biopsy Specimens
    Kunal Nagpal et al.
    JAMA Oncol. doi:10.1001/jamaoncol.2020.2485 Published online July 23, 2020.
  • 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
  • Purpose: To compare biparametric contrast-free radiomic machine learning (RML), mean apparent dffusion coeficient (ADC), and radiologist assessment for characterization of prostate lesions detected during prospective MRI interpretation.
    Conclusion: Quantitative measurement of the mean apparent diffusion coeficient (ADC) improved differentiation of benign versus malignant prostate lesions, compared with clinical assessment. Radiomic machine learning had comparable but not better performance than mean ADC assessment.
    Radiomic Machine Learning for Characterization of Prostate Lesions with MRI: Comparison to ADC Values
    Bonekamp D et al.
    Radiology 2018 (in press)
  • “The authors demonstrated that the DL-CNN can overcome the strong boundary between two regions that have large difference in gray levels and provides a seamless mask to guide level set segmentation, which has been a problem for many gradient-based segmentation methods. Compared to our previous CLASS with LCR method, which required two user inputs to initialize the segmentation, DL-CNN with level sets achieved better segmentation performance while using a single user input. Compared to the Haar-feature-based likelihood map, the DL-CNN-based likelihood map could guide the level sets to achieve better segmentation. The results demonstrate the feasibility of our new approach of using DL-CNN in combination with level sets for segmentation of the bladder.”


    Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets.
Cha KH et al.
Med Phys. 2016 Apr;43(4):1882

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