Joy Ajin, Saucedo Andres, Joines Melissa, Lee-Felker Stephanie, Kumar Sumit, Sarma Manoj K, Sayre James, DiNome Maggie, Thomas M Albert
Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States.
Surgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States.
BJR Open. 2022 Oct 12;4(1):20220009. doi: 10.1259/bjro.20220009. eCollection 2022.
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.
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.
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.
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.
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.
本研究的主要目的是通过使用加速5D EP - COSI技术在多个空间位置将磁共振波谱扩展到二维来检测乳腺癌中的新型生物标志物。
对5D EP - COSI数据进行非均匀欠采样，加速因子为8，并使用基于组稀疏性的压缩感知重建进行重建。然后对不同的代谢物和脂质比率进行量化并进行统计学显著性分析。基于量化的代谢物和脂质比率生成线性判别模型。还重建了量化的代谢物和脂质比率的光谱图像。
使用5D EP - COSI技术生成的二维COSY波谱显示，健康、良性和恶性组织在代谢物和脂质比率的平均值方面存在差异，特别是基于不饱和脂肪酸、肌醇和甘氨酸的潜在新型生物标志物的比率。进一步表明，从乳腺多个位置的量化COSY信号生成的胆碱和不饱和脂质比率图有潜力作为恶性肿瘤的补充标志物，可添加到多参数磁共振检查方案中。发现使用代谢物和脂质比率的判别模型在区分健康组织中的良性和恶性肿瘤方面具有统计学显著性。
加速5D EP - COSI技术除了能检测乳腺癌中常见的胆碱外，还显示出检测新型生物标志物如甘氨酸、肌醇和不饱和脂肪酸的潜力，并有助于生成代谢物和脂质比率图，这些图在乳腺癌检测中可能发挥重要作用。
本研究首次评估了一种多维磁共振波谱成像技术，用于检测除常见的胆碱外，基于甘氨酸、肌醇和不饱和脂肪酸的潜在新型生物标志物。还展示了恶性和良性乳腺肿块中胆碱和不饱和脂肪酸比率相对于水的空间映射。这些代谢特征可作为额外的生物标志物，用于改善乳腺癌的诊断和治疗评估。
