Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features.

@article{Park2020DifferentiatingAP,
  title={Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features.},
  author={S Park and Lei Chu and Ralph H. Hruban and Bert Vogelstein and K W Kinzler and Alan Loddon Yuille and Damoun Fouladi and Shahab Shayesteh and Saeed Ghandili and Cristopher L. Wolfgang and Richard A. Burkhart and J. He and Elliot K. Fishman and Satomi Kawamoto},
  journal={Diagnostic and interventional imaging},
  year={2020}
}
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35 Citations
Highly Influential Citations
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Background Citations
6
Methods Citations
2
Results Citations
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35 Citations

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Citation Type

CT Radiomics Features in Differentiation of Focal-Type Autoimmune Pancreatitis from Pancreatic Ductal Adenocarcinoma: A Propensity Score Analysis.
Liver spontaneous hypoattenuation on CT is an imaging biomarker of the severity of acute pancreatitis.
Quantitative Radiomic Features from Computed Tomography Can Predict Pancreatic Cancer up to 36 Months Before Diagnosis
TLDR
QIF can accurately predict PDAC on CT imaging and represent promising biomarkers for early detection of pancreatic cancer in adults diagnosed with PDAC.
Development of CT-Based Imaging Signature for Preoperative Prediction of Invasive Behavior in Pancreatic Solid Pseudopapillary Neoplasm
TLDR
The arterial radiomics model constructed by 3D-ROI feature is potential to predict the invasiveness of pancreatic solid pseudopapillary neoplasm preoperatively.
Characterization of Benign and Malignant Pancreatic Lesions with DECT Quantitative Metrics and Radiomics.
CT Simplified Radiomic Approach to Assess the Metastatic Ductal Adenocarcinoma of the Pancreas
TLDR
A simplified radiomic analysis of pancreatic ductal adenocarcinoma based on qualitative and quantitative tumor features and to compare the results between metastatic and non-metastatic patients showed substantial differences.
Computed tomography-based radiomics approach in pancreatic tumors characterization
TLDR
The use of the CT radiomics approach provides a higher diagnostic performance of CT imaging in pancreatic tumors differentiation and prognosis.
The effect of CT texture-based analysis using machine learning approaches on radiologists' performance in differentiating focal-type autoimmune pancreatitis and pancreatic duct carcinoma
To develop a support vector machine (SVM) classifier using CT texture-based analysis in differentiating focal-type autoimmune pancreatitis (AIP) and pancreatic duct carcinoma (PD), and to assess the…
The impact of radiomics in diagnosis and staging of pancreatic cancer
TLDR
Radiomics seems to be a promising approach to evaluate PC from diagnosis to treatment response prediction, and further and larger studies are required to confirm the role and be allowed to include radiomics parameter in a comprehensive decision support system.
Deep Convolutional Neural Network-Assisted Feature Extraction for Diagnostic Discrimination and Feature Visualization in Pancreatic Ductal Adenocarcinoma (PDAC) versus Autoimmune Pancreatitis (AIP)
TLDR
A machine learning model is successfully trained using deep feature extraction from CT-images to differentiate between AIP and PDAC and, in comparison to traditional radiomic features, deep features achieved a higher sensitivity, specificity, and ROC-AUC.
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References

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TLDR
Feature on dual-phase computed tomography that help differentiate autoimmune pancreatitis (AIP) from pancreatic adenocarcinoma (PA) are studied to demonstrate moderate agreement in distinguishing AIP from PA on the basis of CT imaging.
Utility of CT Radiomics Features in Differentiation of Pancreatic Ductal Adenocarcinoma From Normal Pancreatic Tissue.
TLDR
Radiomics features extracted from whole pancreas can be used to differentiate between CT cases from patients with PDAC and healthy control subjects with normal Pancreas.
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TLDR
Analysis of a combination of CT and MRI findings could improve the diagnostic accuracy of differentiating f-AIP from PC.
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