Newly Diagnosed Glioma patients study with MRI/MRSI by GjnL49L

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									                                                               Poster #33


 Integrating in vivo MRI/MRSI data for
 non-invasive classification of gliomas



             Xiaojuan Li, Ying Lu, Andrea Pirzkall, Sarah Nelson

                                ISMRM workshop on
            In Vivo Functional and Molecular Assessment of Cancer
                    Santa Cruz, CA, USA, Oct 19-21, 2002


Graduate Bioengineering Group            Magnetic Resonance Science Center
    University of California                   Department of Radiology
   Berkeley / San Francisco              University of California, San Francisco
                                             Poster #33
                     GOALS

Combining MRI and MR Spectroscopic
imaging (MRSI) to improve non-invasive
diagnosis for newly diagnosed glioma
patients
   To explicitly integrate information from MRI and
    MRSI in classification procedure
   To consider MRSI characteristics of the entire
    lesion instead of a pre-selected region from
    within the anatomic lesion.
                                                            Poster #33
        MATERIALS AND METHODS

   64 untreated glioma patients, classified by histology
       21 grade 2, 22 grade 3 and 21 grade 4
   1.5T GE Signa Echospeed clinical scanner
       T2-weighted images, pre/post-contrast T1-weighted images
       3D MR Spectroscopic imaging
            PRESS, Te=144ms, TR=1s, 1.0 cc voxel size
   Data classified with recursive partitioning analysis
    (RPA)
       Morphologic abnormal volumes, metabolic abnormal volumes,
        median, max levels of metabolic abnormal within ROIs, metabolic
        burdens measured by sum of abnormal index within ROIs
                                                            Poster #33
                       RESULTS
Classification tree with morphologic information
                                                     N NecL     Y
        CEL         NecL
       Y    N      Y        N
 G2    1   20      0       21             N            Y
                                               CEL
 G3   14    8      0       22
                                                                    G4
 G4   21    0     17        4
                                     G2                G3

                CEL: contrast enhancement lesion
                NecL: Macroscopic necrosis


      Cross validation error rate 15/64 = 23.4%
                                                                   Poster #33
Classification tree with MRI/MRSI parameters
V1: burden of Cr/NAA abnormal                     N        NecL    Y
V2:volume of lactate/lipid, volume
ratio of Cho/NAA abnormal/T2, burden
Cho/NAA abnormal



                           N           CEL    Y                         G4



                  V1                                  V2


        G2                 G3                G3               G4

             Cross validation error rate 7/64 = 10.9%
 MRSI helps to distinguish
  Non-enhanced grade 3 lesions from grade 2 lesions
  Grade 4 lesions without macroscopic necrosis from
 enhanced grade 3 lesions
                                     Poster #33
Non-enhanced grade 3 lesion vs. grade 2 lesion



                              CrNI
                                      Grade 2




       Grade 3

CrNI

0       6
                                       Poster #33
Grade 4 lesion without macroscopic necrosis
vs. enhanced grade 3 lesion

                           Lac+Lip


                                     Grade 4




           Grade 3

 Lac+Lip

 0          17
                                                        Poster #33
                                   N        NecL
                                                    Y


              N      CE        Y
                                                             G4

        V1                             V2
  G2          G3          G3                   G4




Full classification tree structure with images of typical
lesions for each branch. The method also provides
clues for possible subgroups within each grade.
                                                  Poster #33
  CONCLUSIONS/DISCUSSIONS
 MRSI provides useful information in addition to
  MRI during non-invasive classification for gliomas.
  It also provides clues for subgroups within each
  grade.
 Recursive partitioning analysis is a powerful tool to
  develop strategies for combining the information
  from MRI and MRSI for classification.
    With natural and useful interpretation
    Easy to integrate more variables
    An exploratory tool and the results in this study can
     be data dependent because of the small patient
     population. Larger cohort of patients will be studied
     in the future.

This research was supported by NIH R01-CA79719
                                                             Poster #33
Recursive Partitioning Analysis (RPA) &
Linear Discriminant Analysis (LDA)
                 c1



                               c2




                  (a)                                (b)
   Both of the algorithms maximize between group differences while
    minimize within group differences. But RPA is a sequential binary
    process and LDA is a one-step linear separation. (a) Classification
    tree; (b) Linear discriminant analysis with discriminant function.
 So our strategy:
  Apply LDA to subgroups, obtain LDA function
  Apply this function to all of the observation and apply this resulted
    vector in addition to other variables to construct the tree
Metabolic index images


                                                            13



                     CNI                ChCrI


                                                             0




                     CrNI                 LLI
  CNI2   CrNI2 Define metabolic abnormalities as regions within
  LLI5   ChCrI2 CNI2, CrNI2, ChCrI2 and LL5 respectively

								
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