Predictive ability of DWI/ADC and DCE-MRI kinetic parameters in differentiating benign from malignant breast lesions and in building a prediction model.

OBJECTIVES: To analyze DWI/ADC and DCE-MRI kinetic parameters on breast MRI between benign and malignant lesions, to obtain independent predictors of malignancy, and to build a possible future predictive model. METHODS: 121 patients (151 breast lesions) were evaluated by 3.0T breast MRI. Based on their post-operative histopathology, we divided them into two groups, benign and malignant. We processed their DCE-MRI images to obtain size of lesions and several kinetic parameters like Time Intensity Curve (TIC), Time To Peak (TTP), Area Under Curve (AUC), Maximum Enhancement, Wash-In (WI), and Wash-Out (WO). We also processed their DWI/ADC maps to obtain their Signal Intensity (SI) ADC values. These parameters were compared between the two groups. RESULTS: The age of the patients was higher in the malignant group as compared to the benign group (mean=55.9 years and 47.3 years, respectively, P<0.001). Malignant lesions were of greater size than benign ones (mean 2.55 cm and 1.82 cm, respectively, P<0.01). On DCE-MRI, parameters such as TIC, AUC, WI, and WO were statistically significantly different (all P values <0.05). On DWI/ADC, malignant lesions had statistically significant lower ADC values than benign lesions (mean of 1.02x0.001 and 1.72x0.001 mm2/s, respectively, P<0.001). We obtained a cutoff ADC value of 1.18x0.001 mm2/s to differentiate between benign and malignant lesions. Our predictive model results showed ADCmean and TIC as independent predictors of malignancy with an excellent combined model of fit (P=0.000). CONCLUSION: Kinetic DCE-MRI and DWI parameters are significantly different between benign and malignant breast lesions. ADCmean and TIC are reliable independent predictors of malignancy in our prediction model, with excellent goodness of fit to predict cancer with a sensitivity of 94.2%.

Prediction and prognosis of biologically aggressive breast cancers by the combination of DWI/DCE-MRI and immunohistochemical tumor markers.

Breast cancer (BC) research has been evolving tremendously on all fronts, whether it being for imaging, pathology, oncology, pharmacology, or genetics. Regarding medical imaging, dynamic contrast enhanced MRI (DCE-MRI) and diffusion weighted imaging (DWI) are now both universally recognized and widely used modalities in multiparametric MRI (mp-MRI) to diagnose and stage BC, to assess post-chemotherapy response, and to differentiate between scar tissue and recurrent tumor. Meanwhile, pathologists have provided evidence of BC being heterogeneous and having several subtypes, which in turn might affect its prognostic and therapeutic outcomes. Immunohistochemical testing for estrogen receptor (ER), progesterone receptor (PR), human epidermal receptor factor-2 (HER-2), and Ki-67 proliferation index is performed daily to categorize breast tumors into different molecular subtypes. Since then, a number of studies have evaluated whether there is any inter-relationship between them and mp-MRI parameters, the nature of their relationship if any, and the predictive ability of mp-MRI to diagnose biologically aggressive tumors. This review aims to summarize published literature where the data of DCE-MRI/DWI and immunohistochemical markers have been combined for BC studies in order to observe what conclusions have been reached so far, how our understanding of BC has changed because of them, and what are the future implications of these for the diagnosis of breast tumors. We also give our suggestions on what other relevant areas should be investigated.