Purpose:To evaluate dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) as a tool for early prediction of response to neoadjuvant chemotherapy (NAC) and 5-year survival in patients with locally advanced breast cancer.Materials and Methods: DCE-MRI was performed in patients scheduled for NAC (n ϭ 24) before and after the first treatment cycle. Clinical response was evaluated after completed NAC. Relative signal intensity (RSI) and area under the curve (AUC) were calculated from the DCE-curves and compared to clinical treatment response. Kohonen and probabilistic neural network (KNN and PNN) analysis were used to predict 5-year survival.Results: RSI and AUC were reduced after only one cycle of NAC in patients with clinical treatment response (P ϭ 0.02 and P ϭ 0.08). The mean and 10th percentile RSI values before NAC were significantly lower in patients surviving more than 5 years compared to nonsurvivors (P ϭ 0.05 and 0.02). This relationship was confirmed using KNN, which demonstrated that patients who remained alive clustered in separate regions from those that died. Calibration of contrast enhancement curves by PNN for patient survival at 5 years yielded sensitivity and specificity for training and testing ranging from 80%-92%.Conclusion: DCE-MRI in locally advanced breast cancer has the potential to predict 5-year survival in a small patient cohort. In addition, changes in tumor vascularization after one cycle of NAC can be assessed.

The objectives of this study were to (a) explore the spectral characteristics of brain metastases, focusing on the origin of the primary cancer, and (b) evaluate the correlation with clinical outcome using multivariate analysis. High-resolution magic angle spinning (HR-MAS) MR spectra (n = 26) were obtained from 16 patients with brain metastases using a Bruker Avance DRX600 instrument. Standard pulse-acquired and spin-echo (TE 32 and 285 ms) (1)H spectra were obtained. These were examined using principal component analysis (PCA) and partial least squares regression analysis (PLS) relating spectral data to clinical outcome. The PCA score plot of pulse-acquired HR-MAS spectra showed a trend of clustering due to the origin of the metastases, mainly based on differences in the lipid signals at 1.3 and 0.9 ppm. With PLS, spectra of patients who died less than 5 months after surgery appeared to cluster in the lower left quadrant of the score plot. These preliminary results on brain metastasis classification and prediction of survival must be validated in a larger patient cohort. However, the possibility of differentiating metastases according to origin and predicting survival on the basis of HR-MAS spectra suggests that this method may be useful for diagnosing and planning treatment for brain metastases and also for guiding decisions about terminating further treatment.

BackgroundMetastases to the central nervous system from different primary cancers are an oncologic challenge as the overall prognosis for these patients is generally poor. The incidence of brain metastases varies with type of primary cancer and is probably increasing due to improved therapies of extracranial metastases prolonging patient's overall survival and thereby time for brain metastases to develop. In addition, the greater access to improved neuroimaging techniques can provide earlier diagnosis. The aim of this study was to investigate the feasibility of using proton magnetic resonance spectroscopy (MRS) and multivariate analyses to characterize brain metastases originating from different primary cancers, to assess changes in spectra during radiation treatment and to correlate the spectra to clinical outcome after treatment.MethodsPatients (n = 26) with brain metastases were examined using single voxel MRS at a 3T clinical MR system. Five patients were excluded due to poor spectral quality. The spectra were obtained before start (n = 21 patients), immediately after (n = 6 patients) and two months after end of treatment (n = 4 patients). Principal component analysis (PCA) and partial least square regression analysis (PLS) were applied in order to identify clustering of spectra due to origin of metastases and to relate clinical outcome (survival) of the patients to spectral data from the first MR examination.ResultsThe PCA results indicated that brain metastases from primary lung and breast cancer were separated into two clusters, while the metastases from malignant melanomas showed no uniformity. The PLS analysis showed a significant correlation between MR spectral data and survival five months after MRS before start of treatment.ConclusionMRS determined metabolic profiles analysed by PCA and PLS might give valuable clinical information when planning and evaluating the treatment of brain metastases, and also when deciding to terminate further therapies.

KPS and in particular the extent of BM were the most important prognostic factors. Grouping patients into RPA classes may be important when deciding whether breast cancer patients should be aggressively treated for their BM.