Differentiating pancreatic mucinous cystic neoplasms form serous oligocystic adenomas in spectral CT images using machine learning algorithms: A preliminary study

Chao Li, Xiao Zhu Lin, Rui Wang, Chun Hui, Kin Man Lam, Su Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


Pancreatic cancer is one of the most fatal cancers. Distinguishing mucinous cystic neoplasm from serous oligocystic adenoma by using cross-sectional imaging system is very important for patients' prognosis. Gemstone spectral computed tomography (CT) can provide more information as compared with the conventional CT. Machine-learning algorithms have been employed in a great variety of applications. This preliminary study aims to verify the effectiveness of the additional information provided by spectral CT with the use of the state-of-the-art classification algorithm combined with feature-selection methods. Results show that SVM+MI achieves the highest classification accuracy (71.43%). The second highest classification accuracy is obtained by using SVM+LO (63.83%). Features selected by these algorithms are consistent with clinical observations. Top-ranking features include lower viewing energy (around 50 keV) CT values, Iodine-Water concentrations, and Effective-Z.
Original languageEnglish
Title of host publicationProceedings - International Conference on Machine Learning and Cybernetics
PublisherIEEE Computer Society
Number of pages6
ISBN (Electronic)9781479902576
Publication statusPublished - 1 Jan 2014
Event12th International Conference on Machine Learning and Cybernetics, ICMLC 2013 - Tianjin, China
Duration: 14 Jul 201317 Jul 2013


Conference12th International Conference on Machine Learning and Cybernetics, ICMLC 2013


  • Mucinous Cystic Oligocystic Adenoma
  • Spectral CT
  • Support feature-selection algorithm

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Human-Computer Interaction

Cite this