OBJECTIVES: The objective of this study was to evaluate whether apparent diffusion coefficient (ADC) obtained through diffusion-weighted imaging magnetic resonance imaging at 3 T can be used as an imaging biomarker to differentiate invasive breast cancer from noninvasive ductal carcinoma in situ (DCIS). MATERIALS AND METHODS: One hundred seventy-six histopathologically verified primary malignant breast tumors were retrospectively evaluated in 170 patients. All patients had undergone a standardized 3-T magnetic resonance imaging protocol, containing a diffusion-weighted sequence with 2 b values and a series of dynamic contrast-enhanced T1-weighted sequences. Apparent diffusion coefficient was measured manually by a reader blinded to the histopathological results. The ADC values were correlated with histopathological results. Mean ADC values were compared between invasive cancers and DCIS as well as between different tumor grades. Receiver operating characteristics curves were used to calculate diagnostic performance. RESULTS: There were 155 invasive cancers and 21 noninvasive DCIS. Mean (SD) values differed significantly between the invasive cancers (0.9 [0.15] ×10 mm/s) and the DCIS (1.24 [0.23] ×10 mm/s, P < 0.001). Area under the receiver operating characteristics curve was 0.895 (95% confidence interval [CI], 0.840-0.936). A threshold of 1.01 ×10 mm/s or less allowed an identification of invasive cancers with a sensitivity of 78.06% (95% CI, 70.7%-84.3%) and a specificity of 90.5% (95% CI, 69.6%-98.8%). No significant ADC differences were found among different tumor grades (P > 0.05). CONCLUSIONS: Apparent diffusion coefficient could be used as an imaging biomarker for the diagnosis of breast cancer. It seems to be a valuable noninvasive quantitative biomarker to assess breast cancer invasiveness. Thus, ADC measurements provide the potential to reduce overdiagnosis and subsequent overtreatment.
OBJECTIVES: The objective of this study was to evaluate whether apparent diffusion coefficient (ADC) obtained through diffusion-weighted imaging magnetic resonance imaging at 3 T can be used as an imaging biomarker to differentiate invasive breast cancer from noninvasive ductal carcinoma in situ (DCIS). MATERIALS AND METHODS: One hundred seventy-six histopathologically verified primary malignant breast tumors were retrospectively evaluated in 170 patients. All patients had undergone a standardized 3-T magnetic resonance imaging protocol, containing a diffusion-weighted sequence with 2 b values and a series of dynamic contrast-enhanced T1-weighted sequences. Apparent diffusion coefficient was measured manually by a reader blinded to the histopathological results. The ADC values were correlated with histopathological results. Mean ADC values were compared between invasive cancers and DCIS as well as between different tumor grades. Receiver operating characteristics curves were used to calculate diagnostic performance. RESULTS: There were 155 invasive cancers and 21 noninvasive DCIS. Mean (SD) values differed significantly between the invasive cancers (0.9 [0.15] ×10 mm/s) and the DCIS (1.24 [0.23] ×10 mm/s, P < 0.001). Area under the receiver operating characteristics curve was 0.895 (95% confidence interval [CI], 0.840-0.936). A threshold of 1.01 ×10 mm/s or less allowed an identification of invasive cancers with a sensitivity of 78.06% (95% CI, 70.7%-84.3%) and a specificity of 90.5% (95% CI, 69.6%-98.8%). No significant ADC differences were found among different tumor grades (P > 0.05). CONCLUSIONS: Apparent diffusion coefficient could be used as an imaging biomarker for the diagnosis of breast cancer. It seems to be a valuable noninvasive quantitative biomarker to assess breast cancer invasiveness. Thus, ADC measurements provide the potential to reduce overdiagnosis and subsequent overtreatment.
Authors: D Leithner; G J Wengert; T H Helbich; S Thakur; R E Ochoa-Albiztegui; E A Morris; K Pinker Journal: Clin Radiol Date: 2017-12-09 Impact factor: 2.350
Authors: Hubert Bickel; Katja Pinker; Stephan Polanec; Heinrich Magometschnigg; Georg Wengert; Claudio Spick; Wolfgang Bogner; Zsuzsanna Bago-Horvath; Thomas H Helbich; Pascal Baltzer Journal: Eur Radiol Date: 2016-08-30 Impact factor: 5.315
Authors: Manuela Durando; Lucas Gennaro; Gene Y Cho; Dilip D Giri; Merlin M Gnanasigamani; Sujata Patil; Elizabeth J Sutton; Joseph O Deasy; Elizabeth A Morris; Sunitha B Thakur Journal: Eur J Radiol Date: 2016-06-28 Impact factor: 3.528