title: MSCT follow-up in malignant lymphoma: comparison of manual linear measurements with semi-automated lymph node analysis for therapy response classification.
abstract: OBJECTIVE
  Assignment of semi-automated lymph node analysis compared to manual measurements for therapy response classification of malignant lymphoma in MSCT.
  
  
  METHODS
  MSCT scans of 63 malignant lymphoma patients before and after 2 cycles of chemotherapy (307 target lymph nodes) were evaluated. The long axis diameter (LAD), short axis diameter (SAD) and bi-dimensional WHO were determined manually and semi-automatically. The time for manual and semi-automatic segmentation was evaluated. The ref. standard response was defined as the mean relative change across all manual and semi-automatic measurements (mean manual/semi-automatic LAD, SAD, semi-automatic volume). Statistical analysis encompassed t-test and McNemar's test for clustered data.
  
  
  RESULTS
  Response classification per lymph node revealed semi-automated volumetry and bi-dimensional WHO to be significantly more accurate than manual linear metric measurements. Response classification per patient based on RECIST revealed more patients to be correctly classified by semi-automatic measurements, e. g. 96.0 %/92.9 % (WHO bi-dimensional/volume) compared to 85.7/84.1 % for manual LAD and SAD, respectively (mean reduction in misclassified patients of 9.95 %). Considering the use of correction tools, the time expenditure for lymph node segmentation (29.7 ± 17.4 sec) was the same as with the manual approach (29.1 ± 14.5 sec).
  
  
  CONCLUSIONS
  Semi-automatically derived "lymph node volume" and "bi-dimensional WHO" significantly reduce the number of misclassified patients in the CT follow-up of malignant lymphoma by at least 10 %. However, lymph node volumetry does not outperform bi-dimensional WHO.
authors.affiliation: Department of Clinical Radiology, University of Münster. weslingj@uni-muenster.de
authors.forename: J
authors.initials: J
authors.lastname: Weßling
authors.forename: M
authors.initials: M
authors.lastname: Puesken
authors.forename: R
authors.initials: R
authors.lastname: Koch
authors.forename: N
authors.initials: N
authors.lastname: Kohlhase
authors.forename: T
authors.initials: T
authors.lastname: Persigehl
authors.forename: R
authors.initials: R
authors.lastname: Mesters
authors.forename: W
authors.initials: W
authors.lastname: Heindel
authors.forename: B
authors.initials: B
authors.lastname: Buerke
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D005260:Female; D006801:Humans; D008198:Lymph Nodes / Q000000981:diagnostic imaging*; D008223:Lymphoma / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D015588:Observer Variation; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*; D055815:Young Adult