Quantitative CT based radiomics as predictor of resectability of pancreatic adenocarcinoma

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In current clinical practice, the resectability of pancreatic ductal adenocarcinoma (PDA) is determined subjec-tively by a physician, which is an error-prone procedure. In this paper, we present a method for automated determination of resectability of PDA from a routine abdominal CT, to reduce such decision errors. The tumor features are extracted from a group of patients with both hypo- A nd iso-attenuating tumors, of which 29 were resectable and 21 were not. The tumor contours are supplied by a medical expert. We present an approach that uses intensity, shape, and texture features to determine tumor resectability. The best classification results are obtained with fine Gaussian SVM and the L0 Feature Selection algorithms. Compared to expert predictions made on the same dataset, our method achieves better classification results. We obtain significantly better results on correctly predicting non-resectability (+17%) compared to a expert, which is essential for patient treatment (negative prediction value). Moreover, our predictions of resectability exceed expert predictions by approximately 3% (positive prediction value).

Original languageEnglish
Title of host publicationSPIE.Medical Imaging: Computer-Aided Diagnosis, 10-15 February 2018, Houston, Texas
Subtitle of host publicationComputer-Aided Diagnosis
Place of PublicationBellingham
ISBN (Electronic)9781510616394
Publication statusPublished - 1 Jan 2018
EventSPIE Medical Imaging 2018 - Houston, United States
Duration: 10 Feb 201815 Feb 2018

Publication series

NameProceedings of SPIE


ConferenceSPIE Medical Imaging 2018
Country/TerritoryUnited States

Bibliographical note

session PS9


  • Classification
  • Computer-Aided Diagnosis
  • CT
  • Pancreatic ductal adenocarcinoma
  • Radiomics
  • Resectability Prediction


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