학술논문
Differentiation of lipoma from liposarcoma on MRI using texture and shape analysis.
Document Type
Academic Journal
Author
Thornhill RE; The Ottawa Hospital, Ottawa, Ontario, Canada.; Golfam M; The Ottawa Hospital, Ottawa, Ontario, Canada.; Sheikh A; Department of Medical Imaging, The Ottawa Hospital, General Campus, 501 Smyth Rd, Ottawa, Ontario, K1H 8L Canada. Electronic address: asheikh@ottawahospital.on.ca.; Cron GO; The Ottawa Hospital, Ottawa, Ontario, Canada.; White EA; Keck Medical Center of USC, Los Angeles, California.; Werier J; The Ottawa Hospital, Ottawa, Ontario, Canada.; Schweitzer ME; Stony Brook Medicine, Stony Brook, New York.; Di Primio G; The Ottawa Hospital, Ottawa, Ontario, Canada.
Source
Publisher: Association Of University Radiologists Country of Publication: United States NLM ID: 9440159 Publication Model: Print Cited Medium: Internet ISSN: 1878-4046 (Electronic) Linking ISSN: 10766332 NLM ISO Abbreviation: Acad Radiol Subsets: MEDLINE
Subject
Language
English
Abstract
Rationale and Objectives: To determine if differentiation of lipoma from liposarcoma on magnetic resonance imaging can be improved using computer-assisted diagnosis (CAD).
Materials and 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.
(Copyright © 2014 AUR. Published by Elsevier Inc. All rights reserved.)
Materials and 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.
(Copyright © 2014 AUR. Published by Elsevier Inc. All rights reserved.)