Enhanced mass on contrast-enhanced breast MR imaging: Lesion characterization using combination of dynamic contrast-enhanced and diffusion-weighted MR images.
ABSTRACT To evaluate the diagnostic accuracy of a combination of dynamic contrast-enhanced MR imaging (DCE-MRI) and diffusion-weighted MR imaging (DWI) in characterization of enhanced mass on breast MR imaging and to find the strongest discriminators between carcinoma and benignancy.
We analyzed consecutive breast MR images in 270 patients; however, 13 lesions in 93 patients were excluded based on our criteria. We analyzed tumor size, shape, margin, internal mass enhancement, kinetic curve pattern, and apparent diffusion coefficient (ADC) values. We applied univariate and multivariate analyses to find the strongest indicators of malignancy and calculate a predictive probability for malignancy. We added the corresponding categories to these prediction probabilities for malignancy and calculated diagnostic accuracy when we consider category 4b, 4c, and 5 lesions as malignant and category 4a, 3, and 2 lesions as benign. In a validation study, 75 enhancing lesions in 71 patients were examined consecutively.
Irregular margin, heterogeneous internal enhancement, rim enhancement, plateau time-intensity curve (TIC) pattern, and washout TIC pattern were the strongest indicators of malignancy as well as past studies, and ADC values less than 1.1x10(-3) mm2/s were also the strongest indicators of malignancy. In a validation study, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 92% (56/61), 86% (12/14), 97% (56/58), 71% (12/17), and 91% (68/75), respectively.
The combination of DWI and DCE-MRI could produce high diagnostic accuracy in the characterization of enhanced mass on breast MR imaging.
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ABSTRACT: To develop an interpretation model based on architectural features of suspicious breast findings on magnetic resonance (MR) images. One hundred ninety-two patients with mammographically visible or palpable findings underwent T1- and fat-saturated T2-weighted spin-echo and contrast agent-enhanced fat-saturated gradient-echo MR imaging. Patients underwent subsequent excisional biopsy for histopathologic confirmation. An interpretation model was constructed by using 98 cases and was tested prospectively and expanded by using 94 different cases. Sensitivity, specificity, predictive values, and receiver operating characteristic curves were computed for all models. Individual features with high predictive values were MR visibility, enhancement degree and pattern, focal mass border characteristics, and focal mass internal septations. Feature combinations with high negative predictive values for malignancy were absence of an MR-visible abnormality, focal masses with smooth borders, lobulated or irregular masses with nonenhancing internal septations, and focal masses with no (or minimal) enhancement. The validated- and revised-model performance characteristics were, respectively, as follows: sensitivity, 100% and 96%; specificity, 69% and 79%; positive predictive value, 75% and 76%; negative predictive value, 100% and 97%; and overall accuracy, 83% and 86%. An interpretation model that incorporates breast MR architectural features can achieve high sensitivity and improve specificity for diagnosing breast cancer.Radiology 04/1997; 202(3):833-41. · 6.34 Impact Factor
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ABSTRACT: Breast magnetic resonance imaging (MRI) has been shown to have high sensitivity for cancer detection and is increasingly used following mammography to evaluate suspicious breast lesions. To determine the accuracy of breast MRI in conjunction with mammography for the detection of breast cancer in patients with suspicious mammographic or clinical findings. Prospective multicenter investigation of the International Breast MR Consortium conducted at 14 university hospitals in North America and Europe from June 2, 1998, through October 31, 2001, of 821 patients referred for breast biopsy for American College of Radiology category 4 or 5 mammographic assessment or suspicious clinical or ultrasound finding. MRI examinations performed prior to breast biopsy; MRI results were interpreted at each site, which were blinded to pathological results. Area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of breast MRI. Among the 821 patients, there were 404 malignant index lesions, of which 63 were ductal carcinoma in situ (DCIS) and 341 were invasive carcinoma. Of the 417 nonmalignant index lesions, 366 were benign, 47 showed atypical histology, and 4 were lobular carcinoma in situ. The AUC pooled over all institutions was 0.88 (95% confidence interval [CI], 0.86-0.91). MRI correctly detected cancer in 356 of 404 cancer cases (DCIS or invasive cancer), resulting in a sensitivity of 88.1% (95% CI, 84.6%-91.1%), and correctly identified as negative for cancer 281 of 417 cases without cancer, resulting in a specificity of 67.7% (95% CI, 62.7%-71.9%). MRI performance was not significantly affected by mammographic breast density, tumor histology, or menopausal status. The positive predictive values for 356 of 492 patients was 72.4% (95% CI, 68.2%-76.3%) and of mammography for 367 of 695 patients was 52.8% (95% CI, 49.0%-56.6%) (P<.005). Dynamic MRI did not improve the AUC compared with 3-dimensional MRI alone, but the specificity of a washout pattern for 123 of 136 patients without cancer was 90.4% (95% CI, 84%-95%). Breast MRI has high sensitivity but only moderate specificity independent of breast density, tumor type, and menopausal status. Although the positive predictive value of MRI is greater than mammography, MRI does not obviate the need for subsequent tissue sampling in this setting.JAMA The Journal of the American Medical Association 01/2005; 292(22):2735-42. · 29.98 Impact Factor
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ABSTRACT: To evaluate short inversion time inversion-recovery (STIR) turbo spin-echo (TSE) magnetic resonance (MR) imaging for detection of metastases in lymph nodes by using quantitative and qualitative analyses. One hundred ten patients (68 men and 42 women) with non-small cell lung cancer who ranged in age from 36 to 82 years (mean age, 64 years) were examined with respiratory-triggered STIR TSE MR imaging. Ratios of signal intensity in a lymph node to that in a 0.9% saline phantom (lymph node-saline ratios [LSRs]) for all lymph nodes were classified into three groups according to nodal short-axis diameter. LSRs of each group were compared by using pathologic diagnosis as the standard of reference. For quantitative analysis, the LSR threshold value for a positive test was determined on a per-node basis and tested for ability to enable a correct diagnosis on a per-patient basis. For qualitative analysis, signal intensities of lymph nodes were assessed by using a five-point visual scoring system. Results of quantitative and qualitative analyses were compared on a per-patient basis with McNemar testing. In 110 patients, 92 of 802 lymph nodes were pathologically diagnosed as containing metastases, while 710 lymph nodes did not contain metastases. Mean LSR in the lymph node group with metastasis was higher than that in the group without metastasis (P <.05). When an LSR of 0.6 was used as the positive-test threshold at quantitative analysis, sensitivity was 93% (37 of 40 patients) and specificity was 87% (61 of 70 patients) on a per-patient basis. With a score of 4 as the positive-test threshold at qualitative analysis, sensitivity was 88% (35 of 40 patients) and specificity was 86% (60 of 70 patients) on a per-patient basis. There was no significant difference (P >.05) between results of quantitative and those of qualitative analysis. Quantitative and qualitative analyses of STIR TSE MR images enable differentiation of lymph nodes with metastasis from those without. Qualitative analysis can substitute for quantitative analysis of STIR TSE MR imaging data.Radiology 06/2004; 231(3):872-9. · 6.34 Impact Factor