Diagnostic value of gadopentetate dimeglumine for 1.5-T MR imaging of musculoskeletal masses: comparison with unenhanced T1- and T2-weighted imaging

Usefulness of diffusion-weighted imaging for differentiating between desmoid tumors and malignant soft tissue tumors

PURPOSE

To evaluate the usefulness of diffusion-weighted imaging (DWI) for differentiating between desmoid tumors and malignant soft tissue tumors.

MATERIALS AND METHODS

Conventional MRI and DWI were performed for 8 desmoid tumors and 74 malignant soft tissue tumors. DWI was obtained with a single-shot echo-planar imaging sequence using a 1.5 Tesla (T) MR imager. DW images were acquired with motion-probing gradient pulses applied along three directions (x, y, and z axes) with three b-factors (0, 500, and 1000 s/mm²). Two observers blinded to clinical information measured three regions of interest within the solid tumor and selected a minimum apparent diffusion coefficient () in each lesion. The mean ADC of desmoid tumors was calculated and compared with that of malignant soft tissue tumors using the Mann-Whitney U test.

RESULTS

The mean ADC of desmoid tumors and malignant soft tissue tumors was 1.36 ± 0.48 × 10⁻³ mm² /s and 0.88 ± 0.20 × 10⁻³ mm² /s (mean ± SD), respectively. The mean ADC of the desmoid tumors was significantly higher than that of malignant soft tissue tumors (P < 0.01).

CONCLUSION

DWI is considered to be useful for differentiating between desmoid tumors and malignant soft tissue tumors. In the future, further investigation in a large series is necessary.

Differentiating benign and malignant soft tissue masses by magnetic resonance imaging: role of tissue component analysis

BACKGROUND

There is a variable degree of accuracy in discriminating benign from malignant soft tissue masses based on signal intensity and morphologic characteristics by magnetic resonance imaging (MRI). The aim of this study was to determine the utility of detailed component pattern assessment, in addition to morphologic study, for differentiating benign from malignant soft tissue masses by MRI.

METHODS

The imaging features of 118 histologically proven soft tissue masses were analyzed according to: (1) signal characteristics: (a) high T1 matrix; (b) low T2 matrix; (c) fibrous tissue signal; (d) calcification; (e) myxoid signal tissue; (f) fatty signal tissue; (g) cystic signal; (h) necrotic signal; (i) septations; (j) vascular signal void signal; (k) fat rim; and (l) hemorrhage; and according to (2) morphologic assessment: (a) lesion size (maximal diameter) in centimeters (cm); (b) lesion depth in cm; (c) margins; (d) peritumoral edema; (e) bone involvement; (f) marginal capsule or pseudocapsule; and (g) neurovascular bundle involvement. Univariate and multivariate analyses followed by stepwise logistic regression of combination of imaging features were performed. The predictive value of each imaging feature and various combinations of imaging features were determined.

RESULTS

In univariate analysis, T2 low signal matrix, fibrous tissue, calcification, necrosis, septum, fat rim sign, peritumoral edema, and hemorrhage showed statistically significant differences between benign and malignant masses (p < 0.05). The positive predictive value of necrosis for malignancy was 84.8%, and its specificity was 90.9%. In multivariate analysis, the best model for predicting malignant masses was the combination of necrosis, maximal mass diameter, peritumoral edema, and absent fibrosis, absent calcification, and lack of fat rim. The combination of these parameters resulted in the most correct diagnoses of malignancy, with a sensitivity of 84.2%, specificity of 64.0%, and accuracy of 74.8%, whereas the accuracy of models consisting of component character and morphologic feature were 74.3% and 70.9%, respectively.

CONCLUSION

MRI is useful in determining whether a soft tissue mass is malignant or not. Traditional morphologic assessment was reinforced by detailed component characterization analysis. The parameters favoring malignancy were large lesion size, peritumoral edema, necrosis, and absent calcification, absent fibrosis, and lack of fat rim.

Soft-Tissue Tumors: Value of Static and Dynamic Gadopentetate Dimeglumine–enhanced MR Imaging in Prediction of Malignancy

PURPOSE

To prospectively evaluate static and dynamic gadopentetate dimeglumine-enhanced magnetic resonance (MR) imaging relative to nonenhanced MR imaging in differentiation of benign from malignant soft-tissue lesions and to evaluate which MR imaging parameters are most predictive of malignancy, with associated interobserver variability.

MATERIALS AND METHODS

One hundred forty consecutive patients (78 male patients [median age, 51 years], 62 female patients [median age, 53 years]) with a soft-tissue mass underwent nonenhanced static and dynamic contrast material-enhanced MR imaging. Diagnosis was based on histologic findings in surgical specimens (86 of 140), findings at core-needle biopsy (43 of 140), or results of all imaging procedures with clinical follow-up (11 of 140). Multivariate logistic regression analysis was used to identify the best combination of MR imaging parameters that might be predictive of malignancy. Subjective overall performance of two observers was evaluated with receiver operating characteristic analysis.

RESULTS

For subjective overall diagnosis, area under the receiver operating characteristic curve, a measure for diagnostic accuracy, was significantly larger for combined nonenhanced and contrast-enhanced MR imaging than it was for nonenhanced MR imaging alone, with no significant difference between observers. Multivariate analysis of all lesions revealed that combined nonenhanced static and dynamic contrast-enhanced MR imaging parameters were significantly superior to nonenhanced MR imaging parameters alone and to nonenhanced MR imaging parameters combined with static contrast-enhanced MR imaging parameters in prediction of malignancy. The most discriminating parameters were presence of liquefaction, start of dynamic enhancement (time interval between start of arterial and tumor enhancement), and lesion size (diameter). Results for extremity lesions were the same, with one exception: With dynamic contrast-enhanced MR imaging parameters, diagnostic performance of one observer did not improve.

CONCLUSION

Static and dynamic contrast-enhanced MR imaging, when added to nonenhanced MR imaging, improved differentiation between benign and malignant soft-tissue lesions.