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

Predicting efficacy of neoadjuvant chemotherapy in breast cancer patients with synthetic magnetic resonance imaging method MAGiC: An observational cohort study

OBJECTIVE

MAGnetic resonance Imaging Compilation (MAGiC) is typical method of synthetic magnetic resonance imaging (MRI). The present aimed to investigate the role of MAGiC parameters of relaxation time (T1), transverse relaxation time (T2) and proton density (PD) to predict the treatment efficacy of breast cancer patients after neoadjuvant chemotherapy (NAC).

METHODS

The present prospective cohort study enrolled 120 breast cancer patients who received NAC during 2021-2023. Demographic data and clinical characteristics including tumor node metastasis (TNM) stage, pathological type, molecular classification and lymph node metastasis were collected. The levels of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER-2) were measured. Patients were divided by treatment efficacy using the Miller-Payne grading as partial pathological response (pPR) group and pathological complete response (pCR). The values of MAGiC parameters of longitudinal T1, T2, and PD values were recorded.

RESULTS

In all 120 patients, 73 (60.83%) cases were with pPR and 47 (39.17%) cases were with pCR after treatment. T2 values were markedly lower in pPR patients compared with pCR patients. However, no significant difference was found for T1 and PD values. No significant correlation was observed between any of MAGiC parameters and HER-2, ER or PR. ROC curve showed T2 could be used for prediction of pPR with AUC 0.780. Lymph node metastasis and low levels of T2 were found as independent risk factors for pPR after treatment.

CONCLUSION

The T2 value parameter from MAGiC is an independent risk factor for pPR following NAC in breast cancer patients, suggesting its potential as a biomarker for predicting treatment efficacy.

A BRILLIANT-BRCA study: residual breast tissue after mastectomy and reconstruction

INTRODUCTION

Different types of mastectomies leave different amounts of residual breast tissue. The significance of the residual breast volume (RBV) is not clear. Therefore, we developed an MRI tool that allows to easily assess the RBV. In this study we evaluated factors associated with RBV after skin or nipple sparing mastectomy (SSM/NSM) in breast cancer BRCA pathogenic variant (PV) carriers who underwent both therapeutic and risk reducing SSM/NSM and its relation to breast cancer outcomes using an innovative MRI-based tool.

METHODS

Data of breast cancer BRCA PV who were treated between 2006 and 2020 were retrieved from of the oncogenetics unit databases. Only patients who underwent SSM/NSM and had a postoperative breast MRI available for analysis were included. Data collected included demographics, clinicopathological features, and outcomes. The MRI tool was developed by a breast cancer imaging laboratory. A logistic regression test and 95% confidence interval (CI) were used to assess the associated risk of increased RBV. A forward stepwise linear regression was used to correlate tumour-patient specific factors and RBV, and a Kaplan-Meier curve to show the probability of locoregional relapse.

RESULTS

A total of 84 patients undergoing 89 mastectomies were included. At a median follow-up of 98 months, 5 local, 2 regional, and 4 distant recurrences were observed. RBV was not significantly related with breast cancer outcomes (p value = NS). A higher body mass index (BMI) was associated with a higher RBV (p < 0.0001). A larger number of involved axillary nodes was associated with a smaller RBV (p = 0.025). The RBV on the risk-reducing mastectomy side was significantly higher compared to the breast cancer side (p value = 0.007). Local recurrences occurred in the vicinity of the primary tumour.

Clinical advances in analytical profiling of signature lipids: implications for severe non-communicable and neurodegenerative diseases

BACKGROUND

Lipids play key roles in numerous biological processes, including energy storage, cell membrane structure, signaling, immune responses, and homeostasis, making lipidomics a vital branch of metabolomics that analyzes and characterizes a wide range of lipid classes. Addressing the complex etiology, age-related risk, progression, inflammation, and research overlap in conditions like Alzheimer's Disease, Parkinson's Disease, Cardiovascular Diseases, and Cancer poses significant challenges in the quest for effective therapeutic targets, improved diagnostic markers, and advanced treatments. Mass spectrometry is an indispensable tool in clinical lipidomics, delivering quantitative and structural lipid data, and its integration with technologies like Liquid Chromatography (LC), Magnetic Resonance Imaging (MRI), and few emerging Matrix-Assisted Laser Desorption Ionization- Imaging Mass Spectrometry (MALDI-IMS) along with its incorporation into Tissue Microarray (TMA) represents current advances. These innovations enhance lipidomics assessment, bolster accuracy, and offer insights into lipid subcellular localization, dynamics, and functional roles in disease contexts.

AIM OF THE REVIEW

The review article summarizes recent advancements in lipidomic methodologies from 2019 to 2023 for diagnosing major neurodegenerative diseases, Alzheimer's and Parkinson's, serious non-communicable cardiovascular diseases and cancer, emphasizing the role of lipid level variations, and highlighting the potential of lipidomics data integration with genomics and proteomics to improve disease understanding and innovative prognostic, diagnostic and therapeutic strategies.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Clinical lipidomic studies are a promising approach to track and analyze lipid profiles, revealing their crucial roles in various diseases. This lipid-focused research provides insights into disease mechanisms, biomarker identification, and potential therapeutic targets, advancing our understanding and management of conditions such as Alzheimer's Disease, Parkinson's Disease, Cardiovascular Diseases, and specific cancers.

In Vitro MRS of Cells Treated with Trastuzumab at 1.5 Tesla

The aim of the study was to investigate the effect of Trastuzumab on the MCF-7 and CRL-2314 breast cancer cell lines. Additionally, an attempt was made to optimize magnetic resonance spectroscopy (MRS) for cell culture studies, with particular emphasis on the impact of treatment with Trastuzumab. The research materials included MCF-7 and CRL-2314 breast cancer cell lines. The study examined the response of these cell lines to treatment with Trastuzumab. The clinical magnetic resonance imaging (MRI) system, OPTIMA MR360 manufactured by GEMS, with a magnetic field induction of 1.5 T, was used. Due to the nature of the tested objects, their size and shape, it was necessary to design and manufacture additional receiving coils. They were used to image the tested cell cultures and record the spectroscopic signal. The spectra obtained by MRS were confirmed by NMR using a 300 MHz NMR Fourier 300 with the TopSpin 3.1 system from Bruker. The designed receiving coils allowed for conducting experiments with the cell lines in a satisfactory manner. These tests would not be possible using factory-delivered coils due to their parameters and the size of the test objects, whose volume did not exceed 1 mL. MRS studies revealed an increase in the metabolite at 1.9 ppm, which indicates the induction of histone acetylation. Changes in histone acetylation play a very important role in both cell development and differentiation processes. The use of Trastuzumab therapy in breast cancer cells increases the levels of acetylated histones. MRS studies and spectra obtained from the 300 MHz NMR system are consistent with the specificity inherent in both systems.

Exploring the Intersection of Geophysics and Diagnostic Imaging in the Health Sciences

To develop diagnostic imaging approaches, this paper emphasizes the transformational potential of merging geophysics with health sciences. Diagnostic imaging technology improvements have transformed the health sciences by enabling earlier and more precise disease identification, individualized therapy, and improved patient care. This review article examines the connection between geophysics and diagnostic imaging in the field of health sciences. Geophysics, which is typically used to explore Earth's subsurface, has provided new uses of its methodology in the medical field, providing innovative solutions to pressing medical problems. The article examines the different geophysical techniques like electrical imaging, seismic imaging, and geophysics and their corresponding imaging techniques used in health sciences like tomography, magnetic resonance imaging, ultrasound imaging, etc. The examination includes the description, similarities, differences, and challenges associated with these techniques and how modified geophysical techniques can be used in imaging methods in health sciences. Examining the progression of each method from geophysics to medical imaging and its contributions to illness diagnosis, treatment planning, and monitoring are highlighted. Also, the utilization of geophysical data analysis techniques like signal processing and inversion techniques in image processing in health sciences has been briefly explained, along with different mathematical and computational tools in geophysics and how they can be implemented for image processing in health sciences. The key findings include the development of machine learning and artificial intelligence in geophysics-driven medical imaging, demonstrating the revolutionary effects of data-driven methods on precision, speed, and predictive modeling.

Breast cancer in young Indian women: factors, challenges in screening, and upcoming diagnostics

Breast cancer management for young Indian women are full of challenges. The National Cancer Registry Programme (NCRP) has predicted that nearly 2,30,000 cases of breast cancer will be reported annually by 2025; with a steady increase in cases of young women (< 45 years of age) with breast cancer. In this review, the available literature is evaluated to understand the various risk factors contributing to the rise in cases of breast cancer in young women in India. Further, the challenges that are faced by the technicians in early diagnosis (e.g., physiology of young breasts, limited trained professionals, and awareness among patients, and cost of the treatment) of breast cancer. This review also focuses on the upcoming diagnostics like serum biomarkers and nanosensors for the early identification of the disease. For better prognosis and to reduce the chances of disease reoccurrence and metastasis, it is important that the disease has to be identified at an early stage.

Future of Breast Radiology

The landscape of breast imaging has transformed significantly since mammography's introduction in the 1960s, accelerated by ultrasound and imageguided biopsies in the 1990s. The emergence of magnetic resonance imaging (MRI) in the 2000s added a valuable dimension to advanced imaging. Multimodality and multiparametric imaging have firmly established breast radiology's pivotal role in managing breast disorders. A shift from conventional to digital radiology emerged in the late 20th and early 21st centuries, enabling advanced techniques like digital breast tomosynthesis, contrast-enhanced mammography, and artificial intelligence (AI) integration. AI's impending integration into breast radiology may enhance diagnostics and workflows. It involves computer-aided diagnosis (CAD) algorithms, workflow support algorithms, and data processing algorithms. CAD systems, developed since the 1980s, optimize cancer detection rates by addressing false positives and negatives. Radiologists' roles will evolve into specialized clinicians collaborating with AI for efficient patient care and utilizing advanced techniques with multiparametric imaging and radiomics. Wearable technologies, non-contrast MRI, and innovative modalities like photoacoustic imaging show potential to enhance diagnostics. Imaging-guided therapy, notably cryotherapy, and theranostics, gains traction. Theranostics, integrating therapy and diagnostics, holds potential for precise treatment. Advanced imaging, AI, and novel therapies will revolutionize breast radiology, offering refined diagnostics and personalized treatments. Personalized screening, AI's role, and imaging-guided therapies will shape the future of breast radiology.

Imaging and Spectroscopic-Based Methods to Understand Cancer Metabolism and Biology

The results of publications in PubMed with the MeSH terms "cancer", "biology", "imaging and cancer", "metabolism" and "spectroscopy" are shown in Figure 1 in the form of a Venn diagram [...].

Calcium-Sensing Receptor Expression in Breast Cancer

The calcium-sensing receptor (CaSR) plays a crucial role in maintaining the balance of calcium in the body. Altered signaling through the CaSR has been linked to the development of various tumors, such as colorectal and breast tumors. This retrospective study enrolled 79 patients who underwent surgical removal of invasive breast carcinoma of no special type (NST) to explore the expression of the CaSR in breast cancer. The patients were categorized based on age, tumor size, hormone receptor status, HER2 status, Ki-67 proliferation index, tumor grade, and TNM staging. Immunohistochemistry was conducted on core needle biopsy samples to assess CaSR expression. The results revealed a positive correlation between CaSR expression and tumor size, regardless of the tumor surrogate subtype (p = 0.001). The expression of ER exhibited a negative correlation with CaSR expression (p = 0.033). In contrast, a positive correlation was observed between CaSR expression and the presence of HER2 receptors (p = 0.002). Increased CaSR expression was significantly associated with lymph node involvement and the presence of distant metastasis (p = 0.001 and p = 0.038, respectively). CaSR values were significantly higher in the patients with increased Ki-67 (p = 0.042). Collectively, higher CaSR expression in breast cancer could suggest a poor prognosis and treatment outcome regardless of the breast cancer subtype.

Potential Applications of Nanoparticles in Improving the Outcome of Lung Cancer Treatment

Lung cancer is managed using conventional therapies, including chemotherapy, radiation therapy, or a combination of both. Each of these therapies has its own limitations, such as the indiscriminate killing of normal as well as cancer cells, the solubility of the chemotherapeutic drugs, rapid clearance of the drugs from circulation before reaching the tumor site, the resistance of cancer cells to radiation, and over-sensitization of normal cells to radiation. Other treatment modalities include gene therapy, immunological checkpoint inhibitors, drug repurposing, and in situ cryo-immune engineering (ICIE) strategy. Nanotechnology has come to the rescue to overcome many shortfalls of conventional therapies. Some of the nano-formulated chemotherapeutic drugs, as well as nanoparticles and nanostructures with surface modifications, have been used for effective cancer cell killing and radio sensitization, respectively. Nano-enabled drug delivery systems act as cargo to deliver the sensitizer molecules specifically to the tumor cells, thereby enabling the radiation therapy to be more effective. In this review, we have discussed the different conventional chemotherapies and radiation therapies used for inhibiting lung cancer. We have also discussed the improvement in chemotherapy and radiation sensitization using nanoparticles.

Identification of crucial genes involved in thyroid cancer development

BACKGROUND

A malignancy of the endocrine system, one of the most common types, is thyroid cancer. It is proven that children who receive radiation treatment for leukemia or lymphoma are at a heightened risk of thyroid cancer due to low-dose radiation exposure throughout childhood. Several factors can increase the risk of thyroid cancer (ThyCa), such as chromosomal and genetic mutations, iodine intake, TSH levels, autoimmune thyroid disorders, estrogen, obesity, lifestyle changes, and environmental contaminants.

OBJECTIVES

The study aimed to identify a specific gene as an essential candidate for thyroid cancer progression. We might be able to focus on developing a better understanding of how thyroid cancer is inherited.

METHODS

The review article uses electronic databases such as PubMed, Google Scholar, Ovid MEDLINE, Embase, and Cochrane Central. The most frequently associated genes with thyroid cancer found on PubMed were BAX, XRCC1, XRCC3, XPO5, IL-10, BRAF, RET, and K-RAS. To perform an electronic literature search, genes derived from DisGeNET: a database of gene-disease associations, including PRKAR1A, BRAF, RET, NRAS, and KRAS, are used.

CONCLUSION

Examining the genetics of thyroid cancer explicitly emphasizes the primary genes associated with the pathophysiology of young and older people with thyroid cancer. Developing such gene investigations at the beginning of the thyroid cancer development process can identify better outcomes and the most aggressive thyroid cancers.

Label-Free Chemically and Molecularly Selective Magnetic Resonance Imaging

Biomedical imaging, especially molecular imaging, has been a driving force in scientific discovery, technological innovation, and precision medicine in the past two decades. While substantial advances and discoveries in chemical biology have been made to develop molecular imaging probes and tracers, translating these exogenous agents to clinical application in precision medicine is a major challenge. Among the clinically accepted imaging modalities, magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) exemplify the most effective and robust biomedical imaging tools. Both MRI and MRS enable a broad range of chemical, biological and clinical applications from determining molecular structures in biochemical analysis to imaging diagnosis and characterization of many diseases and image-guided interventions. Using chemical, biological, and nuclear magnetic resonance properties of specific endogenous metabolites and native MRI contrast-enhancing biomolecules, label-free molecular and cellular imaging with MRI can be achieved in biomedical research and clinical management of patients with various diseases. This review article outlines the chemical and biological bases of several label-free chemically and molecularly selective MRI and MRS methods that have been applied in imaging biomarker discovery, preclinical investigation, and image-guided clinical management. Examples are provided to demonstrate strategies for using endogenous probes to report the molecular, metabolic, physiological, and functional events and processes in living systems, including patients. Future perspectives on label-free molecular MRI and its challenges as well as potential solutions, including the use of rational design and engineered approaches to develop chemical and biological imaging probes to facilitate or combine with label-free molecular MRI, are discussed.

A classification model for detection of ductal carcinoma in situ by Fourier transform infrared spectroscopy based on deep structured semantic model

At present, deep learning is widely used in spectral data processing. Deep learning requires a large amount of data for training, while the collection of biological serum spectra is limited by sample numbers and labor costs, so it is impractical to obtain a large amount of serum spectral data for disease detection. In this study, we propose a spectral classification model based on the deep structured semantic model (DSSM) and successfully apply it to Fourier Transform Infrared (FT-IR) spectroscopy for ductal carcinoma in situ (DCIS) detection. Compared with the traditional deep learning model, we match the spectral data into positive and negative pairs according to whether the spectra are from the same category. The DSSM structure is constructed by extracting features according to the spectral similarity of spectra pairs. This new construction model increases the data amount used for model training and reduces the dimension of spectral data. Firstly, the FT-IR spectra are paired. The spectra pairs are labeled as positive pairs if they come from the same category, and the spectra pairs are labeled as negative pairs if they come from different categories. Secondly, two spectra in each spectra pair are put into two deep neural networks of the DSSM structure separately. Then the spectral similarity between the output feature maps of two deep neural networks is calculated. The DSSM structure is trained by maximizing the conditional likelihood of the spectra pairs from the same category. Thirdly, after the training of DSSM is done, the training set and testing set are input into two deep neural networks separately. The output feature maps of the training set are put into the reference library. Lastly, the k-nearest neighbor (KNN) model is used for classification according to Euclidean distances between the output feature map of each unknown sample to the reference library. The category of the unknown sample is judged according to the categories of k nearest samples. We also use principal component analysis (PCA) to reduce dimension for comparison. The accuracies of the KNN model, principal component analysis-k nearest neighbor (PCA-KNN) model, and deep structured semantic model-k nearest neighbor (DSSM-KNN) model are 78.8%, 72.7%, and 97.0%, which proves that our proposed model has higher accuracy.

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

PURPOSE

To identify demographic and imaging features of MRI-detected enhancing lesions without clinical, ultrasound, and mammographic correlation associated with false-positive outcomes, impacting patient care.

MATERIALS AND METHODS

A retrospective multi-institutional study of imaging studies and patient's chart review of consecutive women with MRI-detected enhancing lesions without clinical, mammogram, or ultrasound correlation between January and December 2018, who underwent MRI-guided biopsy. According to the BI-RADS lexicon, lesions' frequency and imaging features were recorded. The demographic and imaging characteristics variables were correlated with histopathology as the gold standard and an uneventful follow-up of at least one year. Univariate logistic regression analysis was used to explore the correlation between the baseline variables such as age, genetic mutation, family history of breast cancer, personal history of breast cancer, MRI indication, background parenchymal enhancement, and MRI characteristic of the lesion with the false-positive results in main data and subgroup analysis.

RESULTS

Two hundred nineteen women (median age 49 years; range 26-85 years) with 219 MRI-detected enhancing lesions that underwent MRI-guided vacuum-assisted biopsy during the study period fulfilled the study criteria and formed the study cohort. Out of 219, 180 lesions (82.2%) yielded benign pathology results, including 137 benign outcomes (76%) and 43 high-risk lesions (24%). Most demographic and imaging characteristics variables did not help to differentiate malignant from benign lesions. The variables that showed statistically significant association with true-positive results in univariate analyses were age (OR 1.05; 95% CI 1.02-1.08; p = 0.0015), irregular mass-lesion shape when compared with oval/round mass lesion (OR 11.2; 95% CI 1.6-78.4; p = 0.015), and clumped and clustered ring of enhancement when compared with homogeneous (OR 3.22, 95% CI 1.40-7.40; p = 0.0058). For participants with mass breast lesion, the hyperintense signal on the T2-weighted sequence (compared to the normal fibroglandular signal) was significantly related to the false-positive result (OR 0.13; 95% CI 0.02-0.76; p = 0.024).

CONCLUSION

Young patients, oval/round mass-lesion shape, and homogeneous pattern of non-mass enhancement showed the strongest association with false-positive results of enhancing lesions depicted by MRI. For participants with mass breast lesion, T2-bright mass lesion showed significant association with false-positive result. It may impact the patient's management with a suggestion of follow-up rather than interventional procedure when these demographic and imaging parameters are present, consequently decreasing the patient's anxiety and health care costs.

Artificial Intelligence in Breast Cancer Screening and Diagnosis

Cancer is a disease that continues to plague our modern society. Among all types of cancer, breast cancer is now the most common type of cancer occurring in women worldwide. Various factors, including genetics, lifestyle, and the environment, have contributed to the rise in the prevalence of breast cancer among women of all socioeconomic strata. Therefore, proper screening for early diagnosis and treatment becomes a major factor when fighting the disease. Artificial intelligence (AI) continues to revolutionize various spheres of our lives with its numerous applications. Using AI in the existing screening process makes obtaining results even easier and more convenient. Faster, more accurate results are some of the benefits of AI methods in breast cancer screening. Nonetheless, there are many challenges in the process of the integration of AI that needs to be addressed systematically. The following is a review of the application of AI in breast cancer screening.

Multinuclear MRI in Drug Discovery

The continuous development of magnetic resonance imaging broadens the range of applications to newer areas. Using MRI, we can not only visualize, but also track pharmaceutical substances and labeled cells in both in vivo and in vitro tests. ¹H is widely used in the MRI method, which is determined by its high content in the human body. The potential of the MRI method makes it an excellent tool for imaging the morphology of the examined objects, and also enables registration of changes at the level of metabolism. There are several reports in the scientific publications on the use of clinical MRI for in vitro tracking. The use of multinuclear MRI has great potential for scientific research and clinical studies. Tuning MRI scanners to the Larmor frequency of a given nucleus, allows imaging without tissue background. Heavy nuclei are components of both drugs and contrast agents and molecular complexes. The implementation of hyperpolarization techniques allows for better MRI sensitivity. The aim of this review is to present the use of multinuclear MRI for investigations in drug delivery.