Tumor-Attentive Segmentation-Guided GAN for Synthesizing Breast Contrast-Enhanced MRI Without Contrast Agents

OBJECTIVE

Breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a sensitive imaging technique critical for breast cancer diagnosis. However, the administration of contrast agents poses a potential risk. This can be avoided if contrast-enhanced MRI can be obtained without using contrast agents. Thus, we aimed to generate T1-weighted contrast-enhanced MRI (ceT1) images from pre-contrast T1 weighted MRI (preT1) images in the breast.

METHODS

We proposed a generative adversarial network to synthesize ceT1 from preT1 breast images that adopted a local discriminator and segmentation task network to focus specifically on the tumor region in addition to the whole breast. The segmentation network performed a related task of segmentation of the tumor region, which allowed important tumor-related information to be enhanced. In addition, edge maps were included to provide explicit shape and structural information. Our approach was evaluated and compared with other methods in the local (n = 306) and external validation (n = 140) cohorts. Four evaluation metrics of normalized mean squared error (NRMSE), Pearson cross-correlation coefficients (CC), peak signal-to-noise ratio (PSNR), and structural similarity index map (SSIM) for the whole breast and tumor region were measured. An ablation study was performed to evaluate the incremental benefits of various components in our approach.

RESULTS

Our approach performed the best with an NRMSE 25.65, PSNR 54.80 dB, SSIM 0.91, and CC 0.88 on average, in the local test set.

CONCLUSION

Performance gains were replicated in the validation cohort.

SIGNIFICANCE

We hope that our method will help patients avoid potentially harmful contrast agents. Clinical and Translational Impact Statement-Contrast agents are necessary to obtain DCE-MRI which is essential in breast cancer diagnosis. However, administration of contrast agents may cause side effects such as nephrogenic systemic fibrosis and risk of toxic residue deposits. Our approach can generate DCE-MRI without contrast agents using a generative deep neural network. Thus, our approach could help patients avoid potentially harmful contrast agents resulting in an improved diagnosis and treatment workflow for breast cancer.

Quantification of gadolinium in nephrogenic systemic fibrosis: re-examination of a reported cohort with analysis of clinical factors

BACKGROUND

Nephrogenic systemic fibrosis (NSF) has been associated with exposure to gadolinium-based contrast agent (GdCA) used in medical imaging.

OBJECTIVE

We sought to quantify levels of gadolinium in affected skin of patients with NSF and correlate the levels to clinical and laboratory parameters.

METHODS

Skin biopsy specimens were analyzed using inductively coupled plasma mass spectroscopy (ICP-MS) and the micrograms of gadolinium per gram (microg/g) of dry tissue were determined. Clinical and laboratory data were obtained through retrospective chart review. Pearson correlation coefficients were used to correlate tissue gadolinium levels with various parameters.

RESULTS

Six patients from a prior cohort were analyzed for gadolinium in affected skin. The mean amount of gadolinium in affected skin of NSF patients was 320.1 microg/g. No gadolinium was found in limited control tissue from patients unexposed to GdCA or in patients exposed to such agents, but without disease. Higher levels of gadolinium in skin correlated with younger age, lower body weight, lower corrected serum calcium levels, and lower erythropoietin dosing.

LIMITATIONS

The study was limited by the small number of cases and by the retrospective nature of the data and skin samples.

CONCLUSION

Gadolinium is present in substantial amounts within the skin of patients with NSF. Quantification of gadolinium in tissue using ICP-MS may facilitate our understanding of the disease.