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

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

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  • Objectives: To assess whether radiomics signature can identify aggressive behavior and predict recurrence of hepatocellular carcinoma (HCC) after liver transplantation.
    Results: The stable radiomics features associated with the recurrence of HCC were simply found in arterial phase and portal phase. The prediction model based on the radiomics features extracted from the arterial phase showed better prediction performance than the portal vein phase or the fusion signature combining both of arterial and portal vein phase. A radiomics nomogram based on combined model consisting of the radiomics signature and clinical risk factors showed good predictive performance for RFS with a C-index of 0.785 (95% confidence interval [CI]: 0.674-0.895) in the training dataset and 0.789 (95% CI: 0.620-0.957) in the validation dataset. The calibration curves showed agreement in both training (p = 0.121) and validation cohorts (p = 0.164).
    Conclusions: Radiomics signature extracted from CT images may be a potential imaging biomarker for liver cancer invasion and enable accurate prediction of HCC recurrence after liver transplantation.
    Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation
    Donghui Guo et al.
    European Journal of Radiology 117 (2019) 33–40
  • “Microvascular invasion (MVI) and consequently, early tumor recurrence, is noted in 20% of patients undergoing liver transplantation, reducing the 5-year survival from 80% to 40% after liver transplantation . Recurrence has been the main factor that affects the curative effect of HCC after liver transplantation. Prospective identification of recurrence in patients with HCC thus has direct implications for organ allocation, surgical techniques, prognosis, and public policy.”
    Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation
    Donghui Guo et al.
    European Journal of Radiology 117 (2019) 33–40
  • “Previous reports have indicated that some radiomic features extracted from tumor regions of CT images, especially characteristics of arterial and portal phases, can surpass traditional indicators such as Barcelona Clinic Liver Cancer (BCLC) for assessing the efficacy of HCC hepatectomy. These high-throughput characteristics related to MVI (Microvascular invasion) or prognosis of HCC may be potential independent predictors of HCC recurrence after liver transplantation.”
    Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation
    Donghui Guo et al.
    European Journal of Radiology 117 (2019) 33–40
  • Radiomics signature was built with the rad-score of arterial phase. A combined predictive model was built by incorporating the clinical risk factors and radiomics signature with multivariable Cox regression model. Patients were finally stratified into high-risk and low-risk groups based on the combined model with cut-off values at the median of the training dataset in arterial phase. The Log- rank test was computed to compare the two separated KM survival curves.”
    Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation
    Donghui Guo et al.
    European Journal of Radiology 117 (2019) 33–40
  • “A multi-feature-based radiomics signature was identified to be an effective biomarker for the prediction of HCC recurrence after liver transplantation in this study, with potential prognosis value for individualized RFS. The radiomics signature successfully stratified patients with HCC into high-risk and low-risk groups, with significant differences in RFS. Furthermore, the radiomics nomogram based on the combined model which integrates effective clinical characteristics and radiomics signature showed good discrimination and prominent predictive performance for RFS in HCC patients after liver transplantation.”
    Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation
    Donghui Guo et al.
    European Journal of Radiology 117 (2019) 33–40
  • “For patients with high recurrence, liver transplantation is not recommended, so that the scarce donor liver resources can be allocated to patients who urgently need liver transplantation and have a good prognosis.”
    Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation
    Donghui Guo et al.
    European Journal of Radiology 117 (2019) 33–40
  • “ It is conducive to fully exploit potential images using artificial intelligence and big data technology to overcome limitations of traditional evaluation methods, improve the predictive performance of HCC recurrence after liver transplantation, facilitate the rational distribution of donors in clinical practice, and conduct neces- sary intraoperative or postoperative prophylactic treatment for patients with high recurrence.”
    Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation
    Donghui Guo et al.
    European Journal of Radiology 117 (2019) 33–40
  • Although the usefulness of the proposed nomogram lacks external validation, calibration curve analysis demonstrated that the radiomics nomogram was consistent with actual observations for the probability of tumor recurrence at 1, 2, or 3 years. This indicates that radiomics signature has the potential to be used as a biomarker for recurrence in HCC patients after liver transplantation. Further, radiomics can be used to predict MVI and histopathological differentiation closely related to prognosis of HCC. Radiomics technique is expected to become an important cornerstone of accurate diagnosis and treatment of HCC.
    Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation
    Donghui Guo et al.
    European Journal of Radiology 117 (2019) 33–40
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