Quantitative CT based radiomics as predictor of resectability of pancreatic adenocarcinoma

Joost van der Putten; Svitlana Zinger; Fons van der Sommen; Peter H. N. de With; Mathias Prokop; John Hermans

doi:10.1117/12.2291746

27 February 2018 Quantitative CT based radiomics as predictor of resectability of pancreatic adenocarcinoma

Joost van der Putten, Svitlana Zinger, Fons van der Sommen, Peter H. N. de With, Mathias Prokop, John Hermans

Proceedings Volume 10575, Medical Imaging 2018: Computer-Aided Diagnosis; 105753O (2018) https://doi.org/10.1117/12.2291746
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

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- and 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).

Citation Download Citation

Joost van der Putten, Svitlana Zinger, Fons van der Sommen, Peter H. N. de With, Mathias Prokop, and John Hermans "Quantitative CT based radiomics as predictor of resectability of pancreatic adenocarcinoma", Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105753O (27 February 2018); https://doi.org/10.1117/12.2291746

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