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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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)
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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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