RATIONALE AND OBJECTIVES: To evaluate the value of adding Diffusion Weighted Imaging (DWI) with Apparent Diffusion Coefficient (ADC) mapping to dynamic contrast enhanced (DCE-MRI) to distinguish benign from malignant pathology subtypes and tumor recurrence. METHOD AND MATERIALS: In this retrospective IRB approved study, 956 consecutive patients underwent bilateral breast MRI between 1/2015 and 12/2015, with 156 BIRADS 4, 5, or 6 lesions detected in 111 patients. DWI imaging at B0, B100, B600, B1000 was performed with DCE-MRI. Values for diffusion and ADC images were recorded by two fellowship-trained breast radiologists. Mean ADC and signal intensity (SI) values were correlated with histology, tumor grade, hormone receptors (ER, PR, and HER-2)and Oncotype DX scores, when available. p ≤ 0.05 was considered significant. RESULTS: Of 156 lesions, there were 59 (38%) benign lesions, 24 (15%) Ductal Carcinoma In-Situ, 47 (30%) Invasive Ductal Carcinoma (IDC), 15 (10%) Invasive Lobular Carcinoma (ILC) and 2 (2%) Mucinous carcinoma (MC), five (5%) mixed IDC and ILC, and four (4%) other, including tubular and rare types of malignancy. Mean ADC values for malignancy were significantly lower than for benign lesions (1085 ± 343 × 10-6 vs 1481 ± 276 × 10-6 mm2/s), which is highly predictive (area under curve = 0.82). In addition, tumors with PR negativity and Oncotype score ≥18 (intermediate to high risk for recurrence) demonstrated significantly lower ADC values. SI at B100 and B600 was helpful in distinguishing benign versus IDC. There was no significant correlation between ADC values and tumor grade or ER/HER2 status. CONCLUSION: ADC value is important factor in distinguishing malignancy, differentiating tumors with higher Oncotype score, and PR negativity. Therefore, it can be used as an important tool to assist appropriate treatment selection.
RATIONALE AND OBJECTIVES: To evaluate the value of adding Diffusion Weighted Imaging (DWI) with Apparent Diffusion Coefficient (ADC) mapping to dynamic contrast enhanced (DCE-MRI) to distinguish benign from malignant pathology subtypes and tumor recurrence. METHOD AND MATERIALS: In this retrospective IRB approved study, 956 consecutive patients underwent bilateral breast MRI between 1/2015 and 12/2015, with 156 BIRADS 4, 5, or 6 lesions detected in 111 patients. DWI imaging at B0, B100, B600, B1000 was performed with DCE-MRI. Values for diffusion and ADC images were recorded by two fellowship-trained breast radiologists. Mean ADC and signal intensity (SI) values were correlated with histology, tumor grade, hormone receptors (ER, PR, and HER-2)and Oncotype DX scores, when available. p ≤ 0.05 was considered significant. RESULTS: Of 156 lesions, there were 59 (38%) benign lesions, 24 (15%) Ductal Carcinoma In-Situ, 47 (30%) Invasive Ductal Carcinoma (IDC), 15 (10%) Invasive Lobular Carcinoma (ILC) and 2 (2%) Mucinous carcinoma (MC), five (5%) mixed IDC and ILC, and four (4%) other, including tubular and rare types of malignancy. Mean ADC values for malignancy were significantly lower than for benign lesions (1085 ± 343 × 10-6 vs 1481 ± 276 × 10-6 mm2/s), which is highly predictive (area under curve = 0.82). In addition, tumors with PR negativity and Oncotype score ≥18 (intermediate to high risk for recurrence) demonstrated significantly lower ADC values. SI at B100 and B600 was helpful in distinguishing benign versus IDC. There was no significant correlation between ADC values and tumor grade or ER/HER2 status. CONCLUSION: ADC value is important factor in distinguishing malignancy, differentiating tumors with higher Oncotype score, and PR negativity. Therefore, it can be used as an important tool to assist appropriate treatment selection.
Authors: Alexey Surov; Yun-Woo Chang; Lihua Li; Laura Martincich; Savannah C Partridge; Jin You Kim; Andreas Wienke Journal: BMC Cancer Date: 2019-11-05 Impact factor: 4.430
Authors: Lamiss Mohamed Abd El Aziz Sad; Naglaa Lofty Dabees; Dareen Abd El-Aziz Mohamed; Amr Tageldin; Samar Galal Younis Journal: Int J Breast Cancer Date: 2022-02-18
Authors: Corrado Tagliati; Paola Piccinni; Paola Ercolani; Elisabetta Marconi; Barbara Franca Simonetti; Gian Marco Giuseppetti; Andrea Giovagnoni Journal: Pol J Radiol Date: 2021-04-30