title: Differentiation of lipoma from liposarcoma on MRI using texture and shape analysis.
abstract: OBJECTIVE
  To determine if differentiation of lipoma from liposarcoma on magnetic resonance imaging can be improved using computer-assisted diagnosis (CAD).
  
  
  METHODS
  Forty-four histologically proven lipomatous tumors (24 lipomas and 20 liposarcomas) were studied retrospectively. Studies were performed at 1.5T and included T1-weighted, T2-weighted, T2-fat-suppressed, short inversion time inversion recovery, and contrast-enhanced sequences. Two experienced musculoskeletal radiologists blindly and independently noted their degree of confidence in malignancy using all available images/sequences for each patient. For CAD, tumors were segmented in three dimensions using T1-weighted images. Gray-level co-occurrence and run-length matrix textural features, as well as morphological features, were extracted from each tumor volume. Combinations of shape and textural features were used to train multiple, linear discriminant analysis classifiers. We assessed sensitivity, specificity, and accuracy of each classifier for delineating lipoma from liposarcoma using 10-fold cross-validation. Diagnostic accuracy of the two radiologists was determined using contingency tables. Interreader agreement was evaluated by Cohen kappa.
  
  
  RESULTS
  Using optimum-threshold criteria, CAD produced superior values (sensitivity, specificity, and accuracy are 85%, 96%, and 91%, respectively) compared to radiologist A (75%, 83%, and 80%) and radiologist B (80%, 75%, and 77%). Interreader agreement between radiologists was substantial (kappa [95% confidence interval]=0.69 [0.48-0.90]).
  
  
  CONCLUSIONS
  CAD may help radiologists distinguish lipoma from liposarcoma.
authors.affiliation: The Ottawa Hospital, Ottawa, Ontario, Canada.
authors.forename: Rebecca E
authors.initials: RE
authors.lastname: Thornhill
authors.affiliation: The Ottawa Hospital, Ottawa, Ontario, Canada.
authors.forename: Mohammad
authors.initials: M
authors.lastname: Golfam
authors.affiliation: Department of Medical Imaging, The Ottawa Hospital, General Campus, 501 Smyth Rd, Ottawa, Ontario, K1H 8L Canada. Electronic address: asheikh@ottawahospital.on.ca.
authors.forename: Adnan
authors.initials: A
authors.lastname: Sheikh
authors.affiliation: The Ottawa Hospital, Ottawa, Ontario, Canada.
authors.forename: Greg O
authors.initials: GO
authors.lastname: Cron
authors.affiliation: Keck Medical Center of USC, Los Angeles, California.
authors.forename: Eric A
authors.initials: EA
authors.lastname: White
authors.affiliation: The Ottawa Hospital, Ottawa, Ontario, Canada.
authors.forename: Joel
authors.initials: J
authors.lastname: Werier
authors.affiliation: Stony Brook Medicine, Stony Brook, New York.
authors.forename: Mark E
authors.initials: ME
authors.lastname: Schweitzer
authors.affiliation: The Ottawa Hospital, Ottawa, Ontario, Canada.
authors.forename: Gina
authors.initials: G
authors.lastname: Di Primio
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D003287:Contrast Media; D003937:Diagnosis, Differential; D005260:Female; D005682:Gadolinium; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008067:Lipoma / Q000175:diagnosis*; D008080:Liposarcoma / Q000175:diagnosis*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D015588:Observer Variation; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D012983:Soft Tissue Neoplasms / Q000175:diagnosis*; D055815:Young Adult
chemical_list: D003287:Contrast Media; D005682:Gadolinium
keywords: Computer-assisted diagnosis; computer-aided diagnosis; lipoma; liposarcoma; magnetic resonance imaging; soft tissue tumor