Rationale and Objectives
The aim of this study was to evaluate the added value of histogram analysis of apparent
diffusion coefficient (ADC) maps in differentiating indeterminate orbital malignant
tumors from benign tumors, compared to using magnetic resonance (MR) morphological
features alone.
Materials and Methods
We retrospectively evaluated 54 patients with orbital tumors from March 2013 to February
2015. All the patients were assessed by both routine MR and diffusion-weighted imaging,
and divided into benign group and malignant group. Routine MR imaging features and
histogram parameters derived from ADC maps, including mean ADC (ADCmean), median ADC (ADCmedian), standard deviation, skewness, kurtosis, and 10th and 90th percentiles of ADC (ADC10 and ADC90), were compared between two groups. Univariate and multivariate logistic regression
analyses were used to identify the most valuable variables in predicting malignancy.
Receiver operating characteristic (ROC) curve analysis was used to determine the diagnostic
value of significant variables.
Results
Multivariate logistic regression analysis indicated that two or more quadrants involved,
iso-intense on T2-weighted imaging (T2WI), and ADC10 were significant predictors for orbital malignancy. By using model 2 (iso-intense
on T2WI + two or more quadrants involved + ADC10 < 0.990) as the criterion, higher AUC and specificity could be achieved than by using
model 1 (iso-intense on T2WI + two or more quadrants involved) alone, (model 2 vs
model 1; area under curve (AUC), 0.827 vs 0.793; sensitivity, 65.4% vs 69.2%; specificity,
100% vs 89.3%).
Conclusions
Iso-intense on T2WI, two or more quadrants involved, and ADC10 are risk factors for orbital malignancy. Histogram analysis of ADC map might provide
added value in predicting orbital malignancy.
Key Words
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Article info
Publication history
Published online: November 25, 2015
Accepted:
October 13,
2015
Received in revised form:
October 12,
2015
Received:
July 3,
2015
Identification
Copyright
© 2015 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
