Magnetic Resonance Imaging (MRI) and MR Spectroscopic Methods in Understanding Breast Cancer Biology and Metabolism

Abstract

A common malignancy that affects women is breast cancer. It is the second leading cause of cancer-related death among women. Metabolic reprogramming occurs during cancer growth, invasion, and metastases. Functional magnetic resonance (MR) methods comprising an array of techniques have shown potential for illustrating physiological and molecular processes changes before anatomical manifestations on conventional MR imaging. Among these, in vivo proton (1H) MR spectroscopy (MRS) is widely used for differentiating breast malignancy from benign diseases by measuring elevated choline-containing compounds. Further, the use of hyperpolarized 13C and 31P MRS enhanced the understanding of glucose and phospholipid metabolism. The metabolic profiling of an array of biological specimens (intact tissues, tissue extracts, and various biofluids such as blood, urine, nipple aspirates, and fine needle aspirates) can also be investigated through in vitro high-resolution NMR spectroscopy and high-resolution magic angle spectroscopy (HRMAS). Such studies can provide information on more metabolites than what is seen by in vivo MRS, thus providing a deeper insight into cancer biology and metabolism. The analysis of a large number of NMR spectral data sets through multivariate statistical methods classified the tumor sub-types. It showed enormous potential in the development of new therapeutic approaches. Recently, multiparametric MRI approaches were found to be helpful in elucidating the pathophysiology of cancer by quantifying structural, vasculature, diffusion, perfusion, and metabolic abnormalities in vivo. This review focuses on the applications of NMR, MRS, and MRI methods in understanding breast cancer biology and in the diagnosis and therapeutic monitoring of breast cancer.

False-positive incidental lesions detected on contrast-enhanced breast MRI: clinical and imaging features

“…Nevertheless, such applications are still in the technical development phase and are far from being incorporated into clinical practice [22-24, and 38 ].Third, MRI technology has evolved and proton MR spectroscopy and diffusion weighting can also affect false-positive results. However, these techniques require a longer acquisition time and have a substantial number of technical aws, such as voxel shift, incomplete acquisition, or incorrect shimming, and therefore, their performance was not evaluated in our study [39,40]. Last but not least, false positives may be affected depending on the indication of the MRI study, and therefore, our study focused primarily on high-risk patients may not be transferable to other settings.…”

Evaluating Diagnostic Value of Magnetic Resonance Spectroscopy (Mrs) for Diagnosis of Malignant Breast Masses

“…In another study, sensitivity and specificity of MRS for distinguishing benign and malignant lesions was found to be 89.6% and 91.2% respectively (Thakur and Bitencourt, 2022). A study investigated use of MRS along with MRI for diagnosis of breast lesions (Sharma and Jagannathan, 2022). In this study, women with suspicious breast mass of 1 cm diameter were examined.…”

Machine Learning Algorithms for Breast Cancer Diagnosis: Challenges, Prospects and Future Research Directions

“…Although mammography is cost effective, it has some limitations such as discomfort breast compression and radiation exposure. On the other hand, a study by Sharma et al [25] showed that MRI achieves 96.2% sensitivity and 83.3% for clinical response and pathological respectively. However, MRI is not effective for breast cancer detection as specificity to lesion characteristics remains low, rendering distinction between cancer and benign pathology a challenge [26] .…”