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

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

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  • Objective To develop and validate a radiomics-based nomogram for preoperatively predicting grade 1 and grade 2/3 tumors in patients with pancreatic neuroendocrine tumors (PNETs).
    Results The fusion radiomic signature has significant association with histologic grade (p < 0.001). The nomogram integrating independent clinical risk factor tumor margin and fusion radiomic signature showed strong discrimination with an area under the curve (AUC) of 0.974 (95% CI 0.950–0.998) in the training cohort and 0.902 (95% CI 0.798–1.000) in the validation cohort with good calibration. Decision curve analysis verified the clinical usefulness of the predictive nomogram.
    Conclusion We proposed a comprehensive nomogram consisting of tumor margin and fusion radiomic signature as a powerful tool to predict grade 1 and grade 2/3 PNET preoperatively and assist the clinical decision-making for PNET patients.
    CT radiomics may predict the grade of pancreatic neuroendocrine tumors: a multicenter study
    Gu D et al.
    European Radiology June 2019: https://doi.org/10.1007/s00330-019-06176-x
  • Key Points
    • Radiomic signature has strong discriminatory ability for the histologic grade of PNETs.
    • Arterial and portal venous phase CT imaging are complementary for the prediction of PNET grading.
    • The comprehensive nomogram outperformed clinical factors in assisting therapy strategy in PNET patients.
    CT radiomics may predict the grade of pancreatic neuroendocrine tumors: a multicenter study
    Gu D et al.
    European Radiology June 2019: https://doi.org/10.1007/s00330-019-06176-x
  • ”Thus, in this multicenter study, we build a radiomic-based predictive model to noninvasively and operatively achieve PNET grading using CT images. Meanwhile, we would also explore the predictive value of clinical and radiological variables, as comparisons with the radiomic signature. A final combined model integrating both radiomic and clinical factors is expected to accurately classify PNET grading.”
    Can Artificial Intelligence Fix the Reproducibility Problem of Radiomics?
    Park CM
    Radiology 2019; 00:1–2 • https://doi.org/10.1148/radiol.2019191154

  • Can Artificial Intelligence Fix the Reproducibility Problem of Radiomics?
    Park CM
    Radiology 2019; 00:1–2 • https://doi.org/10.1148/radiol.2019191154
  • ”For the fusion radiomic signature, we build a multivariable logistic regression model using the two single-phase radiomic signatures. The fusion radiomic signature outperformed either of the single-phase radiomic signatures. Potential reasons for this finding may be that the combination of the two phases could show the vascularity of PNETs more accurately than only one phase. The fusion signature could also provide more textural information in the tumor microenvironments since the most effective features from the two phases in this study were texture features.”
    Can Artificial Intelligence Fix the Reproducibility Problem of Radiomics?
    Park CM
    Radiology 2019; 00:1–2 • https://doi.org/10.1148/radiol.2019191154
  • Objectives: The primary aim of this study was to determine if computed tomographic (CT) texture analysis measurements of the tumor are independently associated with progression-free survival (PFS) and overall survival (OS) in patients with unresectable pancreatic ductal adenocarcinoma (PDAC), including both unresectable locally advanced and metastatic PDAC, who were treated with chemotherapy.
    Conclusions: Pretreatment CT quantitative imaging biomarkers from texture analysis are associated with PFS and OS in patients with unresectable PDAC who were treated with chemotherapy, and the combination of pre- treatment texture parameters and tumor size have the potential to perform better in survival models than imaging biomarker alone.
    Unresectable pancreatic ductal adenocarcinoma: Role of CT quantitative T imaging biomarkers for predicting outcomes of patients treated with chemotherapy
    Cheng, Si-Hang et al.
    European Journal of Radiology, Volume 113, 188 - 197
  • “This may reveal subtle differences imperceptible to the naked eye, thereby compensating for the limitations of conventional CT . CT texture analysis relies on objective computer-aided evaluation of gray-level patterns within lesions to assess tumor heterogeneity quantitatively in terms of numerous relevant parameters, which has been used in the prediction of various cancer prognosis.”
    Unresectable pancreatic ductal adenocarcinoma: Role of CT quantitative T imaging biomarkers for predicting outcomes of patients treated with chemotherapy
    Cheng, Si-Hang et al.
    European Journal of Radiology, Volume 113, 188 - 197
  • In conclusion, instead of post-chemotherapy texture parameters or Δ value, pre-chemotherapy could provide more information about tumor biology. Therefore, using pre-chemotherapy texture of unresectable PDAC to predict survival is more accurate and reliable. Furthermore, texture analysis as a noninvasive image-processing tool has the potential to select patients with good prognosis before therapy, indicating a promising prospect of clinical application in the future.
    Unresectable pancreatic ductal adenocarcinoma: Role of CT quantitative T imaging biomarkers for predicting outcomes of patients treated with chemotherapy
    Cheng, Si-Hang et al.
    European Journal of Radiology, Volume 113, 188 - 197
  • “In our study, SD has been demonstrated to be closely associated with both PFS and OS, and higher SD, which indicated higher intratumoral heterogeneity, predicted better survival outcome in patients with unresectable PDAC. However, in many cancers, increased tumor heterogeneity is associated with worse outcomes. Hypoxia and necrosis, correlated with impaired response to chemotherapy and radiotherapy, are likely to occur in tumors with low levels of angiogenesis, which were closely associated with SD value. In addition, tumor necrosis, which can reflect the presence of hypoxia, was in- vestigated by previous study to verify its significant value in predicting outcome in patients with PDAC, and multivariate survival analysis showed that necrosis was an independent predictor of poor outcome in terms of both disease-free survival (DFS) and disease-specific survival.”
    Unresectable pancreatic ductal adenocarcinoma: Role of CT quantitative T imaging biomarkers for predicting outcomes of patients treated with chemotherapy
    Cheng, Si-Hang et al.
    European Journal of Radiology, Volume 113, 188 - 197
  • ”The CT texture parameter measured in this study included (1) mean gray-level intensity (Mean, brightness); (2) standard deviation (SD, spread of distribution); (3) entropy (irregularity or complexity of pixel intensity in space); (4) mean of positive pixels (MPP); (5) skewness (symmetry of the pixel intensity distribution); (6) kurtosis (sharpness or pointedness of the pixel intensity distribution).”
    Unresectable pancreatic ductal adenocarcinoma: Role of CT quantitative T imaging biomarkers for predicting outcomes of patients treated with chemotherapy
    Cheng, Si-Hang et al.
    European Journal of Radiology, Volume 113, 188 - 197
  • Purpose: To develop and validate an effective model to differentiate NF-pNET from PDAC.
    Conclusion: The integrated model outperformed the model based on clinicoradiological features alone and was comparable to the model based on the radiomic signature alone with respect to the differential diagnosis of atypical NF-pNET and PDAC. The nomogram achieved an optimal preoperative, noninvasive differential diagnosis between atypical pNET and PDAC, which can better inform therapeutic choice in clinical practice.
    Differentiation of atypical non-functional pancreatic neuroendocrine tumor T and pancreatic ductal adenocarcinoma using CT based radiomics
    Ming He et al.
    European Journal of Radiology 117 (2019) 102–111
  • “The therapeutic strategies and prognoses differ significantly between these two major pancreatic solid lesion subtypes, which make the correct differentiation of PDAC from pNET a major issue in clinical practice, especially for atypical cases. For pNET, enucleation is possible, and patients with liver metastasis and with preoperative vascular abutment or invasion can still benefit from surgical resection. For PDAC, more radical surgery is needed, which entails higher post-operative complications and risks; surgery is contraindicated for pa- tients with liver metastasis or vascular invasion.”
    Differentiation of atypical non-functional pancreatic neuroendocrine tumor T and pancreatic ductal adenocarcinoma using CT based radiomics
    Ming He et al.
    European Journal of Radiology 117 (2019) 102–111
  • In the present study, our hypothesis was that a radiomics-based model represented with a nomogram that integrated clinicoradiological features and the radiomic signature would improve the differential diagnostic performance between atypical NF-pNET and PDAC, which is difficult to achieve in clinical practice. Therefore, we aimed to develop and validate an effective model and represent it with a nomogram to differentiate NF-pNET from PDAC.
    Differentiation of atypical non-functional pancreatic neuroendocrine tumor T and pancreatic ductal adenocarcinoma using CT based radiomics
    Ming He et al.
    European Journal of Radiology 117 (2019) 102–111

  • Differentiation of atypical non-functional pancreatic neuroendocrine tumor T and pancreatic ductal adenocarcinoma using CT based radiomics
    Ming He et al.
    European Journal of Radiology 117 (2019) 102–111
  • In conclusion, the integrated model outperformed the model based on the clinicoradiological features alone and performed comparably to the model based on the radiomic signature alone in the differential diagnosis of atypical NF-pNET versus PDAC. The nomogram achieved an optimal preoperative, noninvasive differential diagnosis between atypical pNET and PDAC, which facilitates informed therapeutic choices in clinical practice.
    Differentiation of atypical non-functional pancreatic neuroendocrine tumor T and pancreatic ductal adenocarcinoma using CT based radiomics
    Ming He et al.
    European Journal of Radiology 117 (2019) 102–111
© 1999-2019 Elliot K. Fishman, MD, FACR. All rights reserved.