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Discrimination between neoplastic and nonneoplastic brain lesions by use of proton MR spectroscopy: the limits of accuracy with a logistic regression model. AJNR Am J Neuroradiol 2000 Aug;21(7):1213-9

Date

08/23/2000

Pubmed ID

10954271

Scopus ID

2-s2.0-0033845593   48 Citations

Abstract

BACKGROUND AND PURPOSE: The most accurate method of clinical MR spectroscopy (MRS) interpretation remains an open question. We sought to construct a logistic regression (LR) pattern recognition model for the discrimination of neoplastic from nonneoplastic brain lesions with MR imaging-guided single-voxel proton MRS data. We compared the LR sensitivity, specificity, and receiver operator characteristic (ROC) curve area (Az) with the sensitivity and specificity of blinded and unblinded qualitative MRS interpretations and a choline (Cho)/N-acetylaspartate (NAA) amplitude ratio criterion.

METHODS: Consecutive patients with suspected brain neoplasms or recurrent neoplasia referred for MRS were enrolled once final diagnoses were established by histopathologic examination or serial neurologic examinations, laboratory data, and imaging studies. Control spectra from healthy adult volunteers were included. An LR model was constructed with 10 input variables, including seven metabolite resonance amplitudes, unsuppressed brain water content, water line width, and the final diagnosis (neoplasm versus nonneoplasm). The LR model output was the probability of tumor, for which a cutoff value was chosen to obtain comparable sensitivity and specificity. The LR sensitivity and specificity were compared with those of qualitative blinded interpretations from two readers (designated A and B), qualitative unblinded interpretations (in aggregate) from a group of five staff neuroradiologists and a spectroscopist, and a quantitative Cho/NAA amplitude ratio > 1 threshold for tumor. Sensitivities and specificities for each method were compared with McNemar's chi square analysis for binary tests and matched data with a significance level of 5%. ROC analyses were performed where possible, and Az values were compared with Metz's method (CORROC2) with a 5% significance level.

RESULTS: Of the 99 cases enrolled, 86 had neoplasms and 13 had nonneoplastic diagnoses. The discrimination of neoplastic from control spectra was trivial with the LR, reflecting high homogeneity among the control spectra. An LR cutoff probability for tumor of 0.8 yielded a specificity of 87%, a comparable sensitivity of 85%, and an area under the ROC curve of 0.96. Sensitivities, specificities, and ROC areas (where available) for the other methods were, on average, 82%, 74%, and 0.82, respectively, for readers A and B, 89% (sensitivity) and 92% (specificity) for the group of unblinded readers, and 79% (sensitivity), 77% (specificity), and 0.84 (Az) for the Cho/NAA > 1 criterion. McNemar's analysis yielded significant differences in sensitivity (n approximately 86 neoplasms) between the LR and reader A, and between the LR and the Cho/NAA > 1 criterion. The differences in specificity between the LR and all other methods were not significant (n approximately 13 nonneoplasms). Metz's analysis revealed a significant difference in Az between the LR and the Cho/NAA ratio criterion.

Author List

Butzen J, Prost R, Chetty V, Donahue K, Neppl R, Bowen W, Li SJ, Haughton V, Mark L, Kim T, Mueller W, Meyer G, Krouwer H, Rand S

Authors

Shi-Jiang Li PhD Professor in the Biophysics department at Medical College of Wisconsin
Leighton P. Mark MD Professor in the Radiology department at Medical College of Wisconsin
Wade M. Mueller MD Professor in the Neurosurgery department at Medical College of Wisconsin
Kathleen M. Schmainda PhD Professor in the Biophysics department at Medical College of Wisconsin




MESH terms used to index this publication - Major topics in bold

Adolescent
Adult
Aged
Aged, 80 and over
Aspartic Acid
Brain
Brain Neoplasms
Choline
Diagnosis, Differential
Female
Humans
Logistic Models
Magnetic Resonance Spectroscopy
Male
Middle Aged
Neoplasm Recurrence, Local
Observer Variation
ROC Curve
Sensitivity and Specificity