Non-mass-like enhancement on contrast-enhanced breast MR imaging: Lesion characterization using combination of dynamic contrast-enhanced and diffusion-weighted MR images

Hidetake Yabuuchi, Yoshio Matsuo, Takeshi Kamitani, Taro Setoguchi, Takashi Okafuji, Hiroyasu Soeda, Shuji Sakai, Masamitsu Hatakenaka, Makoto Kubo, Eriko Tokunaga, Hidetaka Yamamoto, Hiroshi Honda

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Abstract

Purpose: 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 lesions showing non-mass-like enhancement on breast MR imaging and to find the strongest discriminators between carcinoma and benignancy. Materials and methods: We analyzed consecutive MR images in 45 lesions showing non-mass like enhancement in 41 patients. We analyzed lesion size, distribution, internal enhancement, kinetic curve pattern, and apparent diffusion coefficient (ADC) values. We applied univariate and multivariate analyses to find the strongest indicators for malignancy. In a validation study, 22 non-mass-like enhancement lesions in 21 patients were examined. We calculated diagnostic accuracy when we presume category 4b, 4c, and 5 lesions as malignant or high to moderate suspicion for malignancy, and category 4a and 3 as low suspicion for malignancy or benign. Results: Segmental distribution (P = 0.018), clumped internal enhancement (P = 0.005), and ADC less than 1.3 × 10-3 mm2/s (P = 0.047) were the strongest MR indicators of malignancy. In a validation study, sensitivity, specificity, positive predictive value, negative predictive value and accuracy were 87% (13/15), 86% (6/7), 93% (13/14), 75% (6/8) and 86% (19/22), respectively. Conclusion: The combination of DCE-MRI and DWI showed high diagnostic accuracy in characterization of non-mass-like enhancement lesions on breast MR images.

Original languageEnglish
Pages (from-to)e126-e132
JournalEuropean Journal of Radiology
Volume75
Issue number1
DOIs
Publication statusPublished - Jul 1 2010

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Breast
Validation Studies
Neoplasms
Multivariate Analysis
Carcinoma
Sensitivity and Specificity

All Science Journal Classification (ASJC) codes

  • Radiology Nuclear Medicine and imaging

Cite this

Non-mass-like enhancement on contrast-enhanced breast MR imaging : Lesion characterization using combination of dynamic contrast-enhanced and diffusion-weighted MR images. / Yabuuchi, Hidetake; Matsuo, Yoshio; Kamitani, Takeshi; Setoguchi, Taro; Okafuji, Takashi; Soeda, Hiroyasu; Sakai, Shuji; Hatakenaka, Masamitsu; Kubo, Makoto; Tokunaga, Eriko; Yamamoto, Hidetaka; Honda, Hiroshi.

In: European Journal of Radiology, Vol. 75, No. 1, 01.07.2010, p. e126-e132.

Research output: Contribution to journalArticle

Yabuuchi, Hidetake ; Matsuo, Yoshio ; Kamitani, Takeshi ; Setoguchi, Taro ; Okafuji, Takashi ; Soeda, Hiroyasu ; Sakai, Shuji ; Hatakenaka, Masamitsu ; Kubo, Makoto ; Tokunaga, Eriko ; Yamamoto, Hidetaka ; Honda, Hiroshi. / Non-mass-like enhancement on contrast-enhanced breast MR imaging : Lesion characterization using combination of dynamic contrast-enhanced and diffusion-weighted MR images. In: European Journal of Radiology. 2010 ; Vol. 75, No. 1. pp. e126-e132.
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abstract = "Purpose: 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 lesions showing non-mass-like enhancement on breast MR imaging and to find the strongest discriminators between carcinoma and benignancy. Materials and methods: We analyzed consecutive MR images in 45 lesions showing non-mass like enhancement in 41 patients. We analyzed lesion size, distribution, internal enhancement, kinetic curve pattern, and apparent diffusion coefficient (ADC) values. We applied univariate and multivariate analyses to find the strongest indicators for malignancy. In a validation study, 22 non-mass-like enhancement lesions in 21 patients were examined. We calculated diagnostic accuracy when we presume category 4b, 4c, and 5 lesions as malignant or high to moderate suspicion for malignancy, and category 4a and 3 as low suspicion for malignancy or benign. Results: Segmental distribution (P = 0.018), clumped internal enhancement (P = 0.005), and ADC less than 1.3 × 10-3 mm2/s (P = 0.047) were the strongest MR indicators of malignancy. In a validation study, sensitivity, specificity, positive predictive value, negative predictive value and accuracy were 87{\%} (13/15), 86{\%} (6/7), 93{\%} (13/14), 75{\%} (6/8) and 86{\%} (19/22), respectively. Conclusion: The combination of DCE-MRI and DWI showed high diagnostic accuracy in characterization of non-mass-like enhancement lesions on breast MR images.",
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T2 - Lesion characterization using combination of dynamic contrast-enhanced and diffusion-weighted MR images

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AU - Matsuo, Yoshio

AU - Kamitani, Takeshi

AU - Setoguchi, Taro

AU - Okafuji, Takashi

AU - Soeda, Hiroyasu

AU - Sakai, Shuji

AU - Hatakenaka, Masamitsu

AU - Kubo, Makoto

AU - Tokunaga, Eriko

AU - Yamamoto, Hidetaka

AU - Honda, Hiroshi

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N2 - Purpose: 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 lesions showing non-mass-like enhancement on breast MR imaging and to find the strongest discriminators between carcinoma and benignancy. Materials and methods: We analyzed consecutive MR images in 45 lesions showing non-mass like enhancement in 41 patients. We analyzed lesion size, distribution, internal enhancement, kinetic curve pattern, and apparent diffusion coefficient (ADC) values. We applied univariate and multivariate analyses to find the strongest indicators for malignancy. In a validation study, 22 non-mass-like enhancement lesions in 21 patients were examined. We calculated diagnostic accuracy when we presume category 4b, 4c, and 5 lesions as malignant or high to moderate suspicion for malignancy, and category 4a and 3 as low suspicion for malignancy or benign. Results: Segmental distribution (P = 0.018), clumped internal enhancement (P = 0.005), and ADC less than 1.3 × 10-3 mm2/s (P = 0.047) were the strongest MR indicators of malignancy. In a validation study, sensitivity, specificity, positive predictive value, negative predictive value and accuracy were 87% (13/15), 86% (6/7), 93% (13/14), 75% (6/8) and 86% (19/22), respectively. Conclusion: The combination of DCE-MRI and DWI showed high diagnostic accuracy in characterization of non-mass-like enhancement lesions on breast MR images.

AB - Purpose: 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 lesions showing non-mass-like enhancement on breast MR imaging and to find the strongest discriminators between carcinoma and benignancy. Materials and methods: We analyzed consecutive MR images in 45 lesions showing non-mass like enhancement in 41 patients. We analyzed lesion size, distribution, internal enhancement, kinetic curve pattern, and apparent diffusion coefficient (ADC) values. We applied univariate and multivariate analyses to find the strongest indicators for malignancy. In a validation study, 22 non-mass-like enhancement lesions in 21 patients were examined. We calculated diagnostic accuracy when we presume category 4b, 4c, and 5 lesions as malignant or high to moderate suspicion for malignancy, and category 4a and 3 as low suspicion for malignancy or benign. Results: Segmental distribution (P = 0.018), clumped internal enhancement (P = 0.005), and ADC less than 1.3 × 10-3 mm2/s (P = 0.047) were the strongest MR indicators of malignancy. In a validation study, sensitivity, specificity, positive predictive value, negative predictive value and accuracy were 87% (13/15), 86% (6/7), 93% (13/14), 75% (6/8) and 86% (19/22), respectively. Conclusion: The combination of DCE-MRI and DWI showed high diagnostic accuracy in characterization of non-mass-like enhancement lesions on breast MR images.

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