Computational Intelligence based analysis of dMRI, for Detection of Spinal Bone Marrow Malignancies

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@InProceedings{oai:CiteSeerPSU:569236,
  title =        "Computational Intelligence based analysis of {dMRI},
                 for Detection of Spinal Bone Marrow Malignancies",
  author =       "Georgia Panagi and Lia A. Moulopoulos and 
                 George Dounias and Thomas Maris and Evangelia Panourgias and 
                 Athanasios Tsakonas and Meletios A. Dimopoulos",
  year =         "2002",
  booktitle =    "{XVII} Symposium Neuroradiologicum",
  address =      "Paris",
  month =        "18-24 " # aug,
  keywords =     "genetic algorithms, genetic programming",
  citeseer-isreferencedby = "oai:CiteSeerPSU:97400",
  annote =       "The Pennsylvania State University CiteSeer Archives",
  language =     "en",
  oai =          "oai:CiteSeerPSU:569236",
  rights =       "unrestricted",
  URL =          "http://www2.ba.aegean.gr/members/tsakonas/Paris2002.pdf",
  URL =          "http://citeseer.ist.psu.edu/569236.html",
  size =         "7 pages",
  abstract =     "This study deals with the problem of detecting spinal
                 bone marrow malignancies with the aid of dynamic
                 contrast enhanced MRI (dMRI). Detection of spinal bone
                 marrow infiltration has improved with the aid of MRI,
                 even though conventional MRI may not be helpful in the
                 presence of red marrow or benign disorders of the
                 vertebral bodies, which often complicate the course of
                 disease in cancer patients. In most of these cases,
                 dMRI may identify underlying malignant infiltration.
                 Modern computational intelligence based methods are
                 applied in order to uncover possible hidden relations
                 among the ROI measurements of dMRI used to describe the
                 problem of spinal bone marrow malignancies, i.e. signal
                 intensity of contrast medium in discrete time intervals
                 and specific measurements (wash-in and wash-out rates,
                 TTPK and TMSP values). The methods used for discovering
                 knowledge, hidden inside the imaging data, are
                 inductive machine learning and genetic programming. A
                 group of 92 patients divided in three sub-groups
                 (normal, abnormal and normal appearing bone marrow)
                 underwent dMRI of the lumbosacral spine. Meaningful
                 sets of diagnostic rules and decision trees are
                 produced by analysing the parameters corresponding to
                 the sequences of dMRI, which not only classify
                 correctly the already proven normal and abnormal group
                 of patients, but also suggest a classification for the
                 group of patients with proven malignant dissemination
                 and apparently normal appearance of the bone marrow on
                 conventional MR images. Furthermore comparisons are
                 given between the results acquired by the computational
                 intelligence based methods and the standard statistical
                 analysis performed on the same data, in order to
                 validate generalised conclusions arising from the
                 proposed analysis.",
  notes =        "not verified",
}

Genetic Programming entries for Georgia Panagi Lia A Moulopoulos Georgios Dounias Thomas Maris Evangelia Panourgias Athanasios D Tsakonas Meletios A Dimopoulos

Citations