To prospectively evaluate the diagnostic performance of magnetic resonance (MR) spectroscopy in patients with suspicious lesions or biopsy-proved cancers at MR imaging by using histologic findings as the reference standard. After institutional review board approval and informed consent were obtained for this HIPAA-compliant study, breast MR spectroscopy was performed in patients with suspicious or biopsy-proved malignant lesions measuring 1 cm or larger at MR imaging. Single-voxel MR spectroscopy data were collected from a single rectangular volume of interest that encompassed the lesion. MR spectroscopy findings were defined as positive if the signal-to-noise ratio of the choline resonance peak was greater than or equal to 2 and as negative in all other cases. MR spectroscopy findings were then compared with histologic findings. A total of 56 patients (age range, 20-77 years) with 57 lesions were imaged. The median lesion size at MR imaging was 2.3 cm (range, 1-15 cm). Histologically, 31 (54%) of 57 lesions were malignant, and 26 (46%) were benign. A choline peak was present in 34 of 57 lesions (including all cancers) and in three of 26 benign lesions, giving MR spectroscopy a sensitivity of 100% and a specificity of 88%. In 40 lesions of unknown histologic type, the use of MR spectroscopy as an adjunct to MR imaging would have significantly (P<.01) increased the positive predictive value of biopsy from 35% to 82%. If biopsy had been performed only on those lesions with a choline peak at MR spectroscopy, biopsy may have been spared in 23 (58%) of 40 lesions, and none of the cancers would have been missed. Proton MR spectroscopy was successfully incorporated into breast MR imaging studies for lesions measuring 1 cm or larger. This technique may be useful in reducing the number of lesions detected at MR imaging that require biopsy.

The majority of WHO grades II and III gliomas harbor a missense mutation in the metabolic gene isocitrate dehydrogenase (IDH) and accumulate the metabolite R-2-hydroxyglutarate (R-2HG). Prior studies showed that this metabolite can be detected in vivo using proton magnetic-resonance spectroscopy (MRS), but the sensitivity of this methodology and its clinical implications are unknown. We developed an MR imaging protocol to integrate 2HG-MRS into routine clinical glioma imaging and examined its performance in 89 consecutive glioma patients. Detection of 2-hydroxyglutarate (2HG) in IDH-mutant gliomas was closely linked to tumor volume, with sensitivity ranging from 8% for small tumors (8 mL). In patients undergoing 2HG-MRS prior to surgery, tumor levels of 2HG corresponded with tumor cellularity but not with tumor grade or mitotic index. Cytoreductive therapy resulted in a gradual decrease in 2HG levels with kinetics that closely mirrored changes in tumor volume. Our study demonstrates that 2HG-MRS can be linked with routine MR imaging to provide quantitative measurements of 2HG in glioma and may be useful as an imaging biomarker to monitor the abundance of IDH-mutant tumor cells noninvasively during glioma therapy and disease monitoring.

To use clinical and MRI radiomic features coupled with machine learning to assess HER2 expression level and predict pathologic response (pCR) in HER2 overexpressing breast cancer patients receiving neoadjuvant chemotherapy (NAC). This retrospective study included 311 patients. pCR was defined as no residual invasive carcinoma in the breast or axillary lymph nodes (ypT0/isN0). Radiomics/statistical analysis was performed using MATLAB and CERR software. After ROC and correlation analysis, selected radiomics parameters were advanced to machine learning modelling alongside clinical MRI-based parameters (lesion type, multifocality, size, nodal status). For predicting pCR, the data was split into a training and test set (80:20). The overall pCR rate was 60.5% (188/311). The final model to predict HER2 heterogeneity utilised three MRI parameters (two clinical, one radiomic) for a sensitivity of 99.3% (277/279), specificity of 81.3% (26/32), and diagnostic accuracy of 97.4% (303/311). The final model to predict pCR included six MRI parameters (two clinical, four radiomic) for a sensitivity of 86.5% (32/37), specificity of 80.0% (20/25), and diagnostic accuracy of 83.9% (52/62) (test set); these results were independent of age and ER status, and outperformed the best model developed using clinical parameters only (p=0.029, comparison of proportion Chi-squared test). The machine learning models, including both clinical and radiomics MRI features, can be used to assess HER2 expression level and can predict pCR after NAC in HER2 overexpressing breast cancer patients. NIH/NCI (P30CA008748), Susan G. Komen Foundation, Breast Cancer Research Foundation, Spanish Foundation Alfonso Martin Escudero, European School of Radiology.

Purpose: The aims of this study were to compare dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) with diffusion-weighted imaging (DWI) with apparent diffusion coefficient mapping as a stand-alone parameter without any other supportive sequence for breast cancer detection and to assess its combination as multiparametric MRI (mpMRI) of the breast. Materials and Methods: In this institutional review board–approved single-center study, prospectively acquired data of 106 patients who underwent breast MRI from 12/2010 to 09/2014 for an imaging abnormality (Breast Imaging Reporting and Data System 0, 4/5) were retrospectively analyzed. Four readers independently assessed DWI and DCE as well as combined as mpMRI. Breast Imaging Reporting and Data System categories, lesion size, and mean apparent diffusion coefficient values were recorded. Histopathology was used as the gold standard. Appropriate statistical tests were used to compare diagnostic values. Results: There were 69 malignant and 41 benign tumors in 106 patients. Four patients presented with bilateral lesions. Dynamic contrast-enhanced MRI was the most sensitive test for breast cancer detection, with an average sensitivity of 100%. Diffusion-weighted imaging alone was less sensitive (82%; P < 0.001) but more specific than DCE-MRI (86.8% vs 76.6%; P = 0.002). Diagnostic accuracy was 83.7% for DWI and 90.6% for DCE-MRI. Multiparametric MRI achieved a sensitivity of 96.8%, not statistically different from DCE-MRI (P = 0.12) and with a similar specificity as DWI (83.8%; P = 0.195), maximizing diagnostic accuracy to 91.9%. There was almost perfect interreader agreement for DWI (κ = 0.864) and DCE-MRI (κ = 0.875) for differentiation of benign and malignant lesions. Conclusion: Dynamic contrast-enhanced MRI is most sensitive for breast cancer detection and thus still indispensable. Multiparametric MRI using DCE-MRI and DWI maintains a high sensitivity, increases specificity, and maximizes diagnostic accuracy, often preventing unnecessary breast biopsies. Diffusion-weighted imaging should not be used as a stand-alone parameter because it detects significantly fewer cancers in comparison with DCE-MRI and mpMRI.