Diffusion-weighted MRI in Multicenter Trials of Breast Cancer: A Useful Measure of Tumor Response?

Correlated MR spectroscopic imaging of breast cancer to investigate metabolites and lipids: acceleration and compressed sensing reconstruction

Objectives

The main objective of this work was to detect novel biomarkers in breast cancer by spreading the MR spectra over two dimensions in multiple spatial locations using an accelerated 5D EP-COSI technology.

Methods

The 5D EP-COSI data were non-uniformly undersampled with an acceleration factor of 8 and reconstructed using group sparsity-based compressed sensing reconstruction. Different metabolite and lipid ratios were then quantified and statistically analyzed for significance. Linear discriminant models based on the quantified metabolite and lipid ratios were generated. Spectroscopic images of the quantified metabolite and lipid ratios were also reconstructed.

Results

The 2D COSY spectra generated using the 5D EP-COSI technique showed differences among healthy, benign, and malignant tissues in terms of their mean values of metabolite and lipid ratios, especially the ratios of potential novel biomarkers based on unsaturated fatty acids, myo-inositol, and glycine. It is further shown the potential of choline and unsaturated lipid ratio maps, generated from the quantified COSY signals across multiple locations in the breast, to serve as complementary markers of malignancy that can be added to the multiparametric MR protocol. Discriminant models using metabolite and lipid ratios were found to be statistically significant for classifying benign and malignant tumor from healthy tissues.

Conclusions

Accelerated 5D EP-COSI technique demonstrates the potential to detect novel biomarkers such as glycine, myo-inositol, and unsaturated fatty acids in addition to commonly reported choline in breast cancer, and facilitates metabolite and lipid ratio maps which have the potential to play a significant role in breast cancer detection.

Advances in knowledge

This study presents the first evaluation of a multidimensional MR spectroscopic imaging technique for the detection of potentially novel biomarkers based on glycine, myo-inositol, and unsaturated fatty acids, in addition to commonly reported choline. Spatial mapping of choline and unsaturated fatty acid ratios with respect to water in malignant and benign breast masses are also shown. These metabolic characteristics may serve as additional biomarkers for improving the diagnostic and therapeutic evaluation of breast cancer.

Preoperative Prediction of Pathologic Response to Neoadjuvant Chemoradiotherapy in Patients With Esophageal Cancer Using 18F-FDG PET/CT and DW-MRI: A Prospective Multicenter Study

PURPOSE

Accurate preoperative prediction of pathologic response to neoadjuvant chemoradiotherapy (nCRT) in patients with esophageal cancer could enable omission of esophagectomy in patients with a pathologic complete response (pCR). This study aimed to evaluate the individual and combined value of ¹⁸F-fluorodeoxyglucose positron emission tomography with integrated computed tomography (¹⁸F-FDG PET/CT) and diffusion-weighted magnetic resonance imaging (DW-MRI) during and after nCRT to predict pathologic response in patients with esophageal cancer.

METHODS AND MATERIALS

In this multicenter prospective study, patients scheduled to receive nCRT followed by esophagectomy for esophageal cancer underwent ¹⁸F-FDG PET/CT and DW-MRI scanning before the start of nCRT, during nCRT, and before esophagectomy. Response to nCRT was based on histopathologic evaluation of the resection specimen. Relative changes in ¹⁸F-FDG PET/CT and DW-MRI parameters were compared between patients with pCR and non-pCR groups. Multivariable ridge regression analyses with bootstrapped c-indices were performed to evaluate the individual and combined value of ¹⁸F-FDG PET/CT and DW-MRI.

RESULTS

pCR was found in 26.1% of 69 patients. Relative changes in ¹⁸F-FDG PET/CT parameters after nCRT (Δ standardized uptake value [SUV]_mean,postP = .016, and Δ total lesion glycolysis _postP = .024), as well as changes in DW-MRI parameters during nCRT (Δ apparent diffusion coefficient [ADC]_duringP = .008) were significantly different between pCR and non-pCR. A c-statistic of 0.84 was obtained for a model with ΔADC_during, ΔSUV_mean,post, and histology in classifying patients as pCR (versus 0.82 for ΔADC_during and 0.79 for ΔSUV_mean,post alone).

CONCLUSIONS

Changes on ¹⁸F-FDG PET/CT after nCRT and early changes on DW-MRI during nCRT can help identify pCR to nCRT in esophageal cancer. Moreover, ¹⁸F-FDG PET/CT and DW-MRI might be of complementary value in the assessment of pCR.

Diffusion-weighted imaging of the breast: current status as an imaging biomarker and future role

Diffusion-weighted imaging (DWI) of the breast is a MRI sequence that shows several advantages when compared to the dynamic contrast-enhanced sequence: it does not need intravenous contrast, it is relatively quick and easy to implement (artifacts notwithstanding). In this review, the current applications of DWI for lesion characterization and prognosis as well as for response evaluation are analyzed from the point of view of the necessary steps to become a useful surrogate of underlying biological processes (tissue architecture and cellularity): from the proof of concept, to the proof of mechanism, the proof of principle and finally the proof of effectiveness. Future applications of DWI in screening, DWI modeling and radiomics are also discussed.

Radiomics of Multiparametric MRI for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer: A Multicenter Study

PURPOSE

We evaluated the performance of the newly proposed radiomics of multiparametric MRI (RMM), developed and validated based on a multicenter dataset adopting a radiomic strategy, for pretreatment prediction of pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer.

EXPERIMENTAL DESIGN

A total of 586 potentially eligible patients were retrospectively enrolled from four hospitals (primary cohort and external validation cohort 1-3). Quantitative imaging features were extracted from T2-weighted imaging, diffusion-weighted imaging, and contrast-enhanced T1-weighted imaging before NAC for each patient. With features selected using a coarse to fine feature selection strategy, four radiomic signatures were constructed based on each of the three MRI sequences and their combination. RMM was developed based on the best radiomic signature incorporating with independent clinicopathologic risk factors. The performance of RMM was assessed with respect to its discrimination and clinical usefulness, and compared with that of clinical information-based prediction model.

RESULTS

Radiomic signature combining multiparametric MRI achieved an AUC of 0.79 (the highest among the four radiomic signatures). The signature further achieved good performances in hormone receptor-positive and HER2-negative group and triple-negative group. RMM yielded an AUC of 0.86, which was significantly higher than that of clinical model in two of the three external validation cohorts.

CONCLUSIONS

The study suggested a possibility that RMM provided a potential tool to develop a model for predicting pCR to NAC in breast cancer.