Biomarkers and Imaging of Breast Cancer

Sonographic Glandular Tissue Component: A Potential Imaging Marker for Upgrading BI-RADS 4A Breast Masses

PURPOSE

To investigate whether sonographic glandular tissue component (GTC) can optimize the management of breast imaging reporting and data system (BI-RADS) 4A breast masses.

MATERIALS AND METHODS

We reviewed the patients with BI-RADS 4A breast masses confirmed by ultrasound and pathology reports from January to December 2020. Based on conventional breast ultrasound images, GTC was categorized into GTC-Low and GTC-High. The consistency of the GTC classification between two radiologists was evaluated using a kappa test. Propensity score matching (PSM) was applied to adjust for unbalanced characteristics between the two groups. Logistic regression was used to analyze the relationship between sonographic GTC and the likelihood of BI-RADS 4A masses being benign or malignant.

RESULTS

Of the 319 patients included finally in the study, the agreement between the two radiologists regarding the GTC classification was good (weighted kappa: 0.736/0.716). The malignancy rate in the GTC-High group (32.7%, 16/49) was significantly higher than that in the overall cohort (14.1%, 45/319; P=0.001). After PSM adjustment to balance relevant covariates between the GTC-High and GTC-Low groups, 45 GTC-High patients were matched with 45 GTC-Low patients. After matching, univariate and multivariate logistic regression analyses identified sonographic GTC as an independent variable associated with malignancy in BI-RADS 4A masses (P=0.012). After matching, the malignancy rate in the GTC-High group (35.6%,16/45) was significantly higher (P=0.014) than that in the GTC-Low group (13.3%, 6/45).

CONCLUSION

Sonographic GTC is an independent predictor of malignancy in BI-RADS 4A breast masses. Masses initially classified as BI-RADS 4A may warrant reclassification to BI-RADS 4B when identified as GTC-High.

Usage of the National Cancer Institute Cancer Research Data Commons by Researchers: A Scoping Review of the Literature

PURPOSE

Over the past decade, significant surges in cancer data of all types have happened. To promote sharing and use of these rich data, the National Cancer Institute's Cancer Research Data Commons (CRDC) was developed as a cloud-based infrastructure that provides a large, comprehensive, and expanding collection of cancer data with tools for analysis. We conducted this scoping review of articles to provide an overview of how CRDC resources are being used by cancer researchers.

METHODS

A thorough literature search was conducted to identify all relevant publications. We included publications that directly cited CRDC resources to specifically examine the impact and contributions of CRDC by itself. We summarized the distributions and trends of how CRDC components were used by the research community and discussed current research gaps and future opportunities.

RESULTS

In terms of CRDC resources used by the research community, encouraging trends in utilization were observed, suggesting that CRDC has become an important building block for fostering a wide range of cancer research. We also noted a few areas where current applications are rather lacking and provided insights on how improvements can be made by CRDC and research community.

CONCLUSION

CRDC, as the foundation of a National Cancer Data Ecosystem, will continue empowering the research community to effectively leverage cancer-related data, uncover novel strategies, and address the needs of patients with cancer, ultimately combatting this disease more effectively.

Investigation of α-Klotho Concentrations in Serum of Cats Affected by Hypertrophic Cardiomyopathy

Being involved in various physiological and pathophysiological mechanisms (ageing, kidney damage, cardiovascular diseases, etc.), Klotho is a parameter of increasing interest. Studies in veterinary medicine are still rare, but it is exciting to find out whether the findings obtained can be transferred to animals. The aim of this study was therefore to investigate Klotho in cats. This study addressed α-Klotho concentrations in the serum of two groups of cats: one diseased group affected by hypertrophic cardiomyopathy (n = 27) and one healthy control group (n = 35). α-Klotho concentrations in serum were measured using an ELISA. The results were evaluated in the context of several echocardiographic measurement parameters in the diseased group. No significant difference between α-Klotho concentrations in the two groups was found. A slight negative correlation was found between α-Klotho concentrations and the relation of left atrium/aorta (La/Ao) in the diseased group. Gaining initial information on α-Klotho in cats, it was not possible to draw definite conclusions concerning cardiomyopathies in this species. The assessment of Klotho should be considered in terms of its broad implications in disease processes, but it is also recommended to focus on specific disease features. Both approaches might be promising as possible applications of Klotho in veterinary medicine.

Cabergoline as a Novel Strategy for Post-Pregnancy Breast Cancer Prevention in Mice and Human

Post-pregnancy breast cancer often carries a poor prognosis, posing a major clinical challenge. The increasing trend of later-life pregnancies exacerbates this risk, highlighting the need for effective chemoprevention strategies. Current options, limited to selective estrogen receptor modulators, aromatase inhibitors, or surgical procedures, offer limited efficacy and considerable side effects. Here, we report that cabergoline, a dopaminergic agonist, reduces the risk of breast cancer post-pregnancy in a Brca1/P53-deficient mouse model, with implications for human breast cancer prevention. We show that a single dose of cabergoline administered post-pregnancy significantly delayed the onset and reduced the incidence of breast cancer in Brca1/P53-deficient mice. Histological analysis revealed a notable acceleration in post-lactational involution over the short term, characterized by increased apoptosis and altered gene expression related to ion transport. Over the long term, histological changes in the mammary gland included a reduction in the ductal component, decreased epithelial proliferation, and a lower presence of recombinant Brca1/P53 target cells, which are precursors of tumors. These changes serve as indicators of reduced breast cancer susceptibility. Additionally, RNA sequencing identified gene expression alterations associated with decreased proliferation and mammary gland branching. Our findings highlight a mechanism wherein cabergoline enhances the protective effect of pregnancy against breast cancer by potentiating postlactational involution. Notably, a retrospective cohort study in women demonstrated a markedly lower incidence of post-pregnancy breast cancer in those treated with cabergoline compared to a control group. Our work underscores the importance of enhancing postlactational involution as a strategy for breast cancer prevention, and identifies cabergoline as a promising, low-risk option in breast cancer chemoprevention. This strategy has the potential to revolutionize breast cancer prevention approaches, particularly for women at increased risk due to genetic factors or delayed childbirth, and has wider implications beyond hereditary breast cancer cases.

An Evolutionary Concept Analysis of Precision Medicine, and Its Contribution to a Precision Health Model for Nursing Practice

Precision medicine is a new concept that has been routinely encountered in the literature for little more than a decade. With increasing use, it becomes crucial to understand the meaning of this concept as it is applied in various settings. An evolutionary concept analysis was conducted to develop an understanding of the essential features of precision medicine and its use. The analysis led to a comprehensive list of the antecedents, attributes, and consequences of precision medicine in multiple settings. With this understanding, precision medicine becomes part of the broader practice of precision health, an important process proposed by nursing scholars to provide complete, holistic care to our patients. A model for precision health is presented as a framework for care.

Breast cancer risk is associated with the HULC rs7763881, MTMR3 rs12537 polymorphisms, and serum levels of HULC and MTMR3 in Egyptian patients

BACKGROUND

Highly upregulated in liver cancer (HULC) is one of the LncRNAs that was documented to enhance cancer progression, and its downregulation is associated with cell cycle arrest and apoptosis. Myotubularin-related protein 3 (MTMR3) is required for autophagy, and many studies consider MTMR3 to be a negative regulator of autophagy processes. However, nothing is understood about how they regulate breast cancer.

MATERIAL AND METHODS

This case-control study included 245 patients (Group A: 85 early BC Group B: 40 metastatic BC cases, Group C: 40 fibroadenoma cases; and Group D: 80 age matched healthy control subjects. TaqMan Real-time PCR was used to analyse rs7158663 and rs12537. MTMR3 and HULC gene expression levels were measured using RT-PCR.

RESULT

Breast cancer patients exhibited elevated serum MTMR3 and HULC compared to fibroadenomas and control cases. The MTMR3 rs12537 "T/T" genotype was highly expressed in cases of breast cancer (early and metastatic) compared to controls (risk genotype). On the other hand, the HULC rs7158663 genotypes were not statistically associated with breast cancer. However, when compared to the control, the C/C genotype of the HULC gene is higher in the case.MTMR3 gene expression was higher in the T/T genotype compared to both the C/C and C/T genotypes, while HULC gene expression was lower in the A/C genotype compared to both the A/A and C/C genotypes. Positive correlation between MTMR3 and HULC. MTMR3 and ALT, as well as HULC and alkaline phosphatase, both showed a statistically significant positive correlation.

CONCLUSION

Our findings reveal that MTMR3 and HULC serum expression and their SNPs (HULC rs7763881, MTMR3 rs12537) are associated with a higher risk for the development of breast cancer in the Egyptian population.

Impact of Imaging Biomarkers and AI on Breast Cancer Management: A Brief Review

Breast cancer stands out as the most frequently identified malignancy, ranking as the fifth leading cause of global cancer-related deaths. The American College of Radiology (ACR) introduced the Breast Imaging Reporting and Data System (BI-RADS) as a standard terminology facilitating communication between radiologists and clinicians; however, an update is now imperative to encompass the latest imaging modalities developed subsequent to the 5th edition of BI-RADS. Within this review article, we provide a concise history of BI-RADS, delve into advanced mammography techniques, ultrasonography (US), magnetic resonance imaging (MRI), PET/CT images, and microwave breast imaging, and subsequently furnish comprehensive, updated insights into Molecular Breast Imaging (MBI), diagnostic imaging biomarkers, and the assessment of treatment responses. This endeavor aims to enhance radiologists' proficiency in catering to the personalized needs of breast cancer patients. Lastly, we explore the augmented benefits of artificial intelligence (AI), machine learning (ML), and deep learning (DL) applications in segmenting, detecting, and diagnosing breast cancer, as well as the early prediction of the response of tumors to neoadjuvant chemotherapy (NAC). By assimilating state-of-the-art computer algorithms capable of deciphering intricate imaging data and aiding radiologists in rendering precise and effective diagnoses, AI has profoundly revolutionized the landscape of breast cancer radiology. Its vast potential holds the promise of bolstering radiologists' capabilities and ameliorating patient outcomes in the realm of breast cancer management.

The outstanding diagnostic value of DKI in multimodal magnetic resonance imaging for benign and malignant breast tumors: A diagnostic accuracy study

To explore the value of applying different magnetic resonance imaging MRI sequences in the differential diagnosis of benign and malignant breast tumors. Routine breast magnetic resonance scans (T1-weighted image, T1WI; T2-weighted image, T2WI), dynamically enhanced scans, diffusion-weighted Imaging, and diffusion kurtosis imaging (DKI) scans were performed on 63 female patients with breast-occupying lesions. The benign and malignant lesions were confirmed by biopsy, excision-histopathology reports. There are 70 lesions, of which 46 are benign and 24 are malignant. Analyze the primary conditions, such as the shape, size, and boundary of the lesion, and determine the apparent diffusion coefficient (ADC), mean kurtosis (MK), and mean diffusion (MD) values. The receiver operating characteristic curve was used to evaluate the value and difference in differentiating benign and malignant lesions. In this study, the results of the 2 testers both showed that the MK of malignant lesions was significantly higher than that of benign lesions (P < .001), and the MD of benign lesions was higher than that of malignant lesions (P < .05). The ADC of benign lesions was higher than that of malignant lesions (P < .05). For MK, the area under the curve of the 2 testers was 0.855/0.869, respectively. When the best cutoff value of MK for tester 1 was 0.515, the sensitivity and specificity of MK for diagnosing malignant tumors were 83.3%/87.0%, respectively. For the 2 testers MD, and ADC, the area under the curve was < 0.5, and the diagnostic value was low. The MK value obtained by DKI has a specific value in the differential diagnosis of benign and malignant breast lesions. DKI is helpful in the identification of benign and malignant breast tumors. The diagnostic value is outstanding, and its importance to the changes in the microstructure of the organization needs to be further explored.

The role and application of small extracellular vesicles in breast cancer

Breast cancer (BC) is the most common malignancy and the leading cause of cancer-related deaths in women worldwide. Currently, patients’ survival remains a challenge in BC due to the lack of effective targeted therapies and the difficult condition of patients with higher aggressiveness, metastasis and drug resistance. Small extracellular vesicles (sEVs), which are nanoscale vesicles with lipid bilayer envelopes released by various cell types in physiological and pathological conditions, play an important role in biological information transfer between cells. There is growing evidence that BC cell-derived sEVs may contribute to the establishment of a favorable microenvironment that supports cancer cells proliferation, invasion and metastasis. Moreover, sEVs provide a versatile platform not only for the diagnosis but also as a delivery vehicle for drugs. This review provides an overview of current new developments regarding the involvement of sEVs in BC pathogenesis, including tumor proliferation, invasion, metastasis, immune evasion, and drug resistance. In addition, sEVs act as messenger carriers carrying a variety of biomolecules such as proteins, nucleic acids, lipids and metabolites, making them as potential liquid biopsy biomarkers for BC diagnosis and prognosis. We also described the clinical applications of BC derived sEVs associated MiRs in the diagnosis and treatment of BC along with ongoing clinical trials which will assist future scientific endeavors in a more organized direction.

Predictive biomarkers for personalized medicine in breast cancer

Breast cancer is one of the most frequent malignancies among women worldwide. Based on clinical and molecular features of breast tumors, patients are treated with chemotherapy, hormonal therapy and/or radiotherapy and more recently with immunotherapy or targeted therapy. These different therapeutic options have markedly improved patient outcomes. However, further improvement is needed to fight against resistance to treatment. In the rapidly growing area of research for personalized medicine, predictive biomarkers - which predict patient response to therapy - are essential tools to select the patients who are most likely to benefit from the treatment, with the aim to give the right therapy to the right patient and avoid unnecessary overtreatment. The search for predictive biomarkers is an active field of research that includes genomic, proteomic and/or machine learning approaches. In this review, we describe current strategies and innovative tools to identify, evaluate and validate new biomarkers. We also summarize current predictive biomarkers in breast cancer and discuss companion biomarkers of targeted therapy in the context of precision medicine.

Cysteine conjugate beta-lyase 2 (CCBL2) expression as a prognostic marker of survival in breast cancer patients

OBJECTIVE

Cysteine conjugate beta-lyase 2 (CCBL2), also known as kynurenine aminotransferase 3 (KAT3) or glutamine transaminase L (GTL), plays an essential role in transamination and cytochrome P450. Its correlation with some other cancers has been explored, but breast cancer (BC) not yet.

METHODS

The mRNA and protein expression of CCBL2 in BC cell lines and patient samples were detected by RT-qPCR and immunohistochemistry (IHC). BC patients' clinical information and RNA-Seq expression were acquired via The Cancer Genome Atlas (TCGA) database. Patients were categorized into high/low CCBL2 expression groups based on the optimal cutoff value (8.973) determined by receiver operating characteristic (ROC) curve. We investigated CCBL2 and clinicopathological characteristics' relationship using Chi-square tests, estimated diagnostic capacity using ROC curves and drew survival curves using Kaplan-Meier estimate. We compared survival differences using Cox regression and externally validated using Gene Expression Omnibus (GEO) database. We evaluated enriched signaling pathways using gene set enrichment analysis (GSEA), explored CCBL2 and relevant genes' relationship using tumor immunoassay resource (TIMER) databases and used the human protein atlas (HPA) for pan-cancer analysis and IHC.

RESULTS

CCBL2 was overexpressed in normal human cell lines and tissues. CCBL2 expression was lower in BC tissues (n = 1104) than in normal tissues (n = 114), validated by GEO database. Several clinicopathologic features were related to CCBL2, especially estrogen receptor (ER), progesterone receptor (PR) and clinical stages. The low expression group exhibited poor survival. CCBL2's area under curve (AUC) analysis showed finite diagnostic capacity. Multivariate cox-regression analysis indicated CCBL2 independently predicted BC survival. GSEA showed enriched pathways: early estrogen response, MYC and so on. CCBL2 positively correlated with estrogen, progesterone and androgen receptors. CCBL2 was downregulated in most cancers and was associated with their survival, including renal and ovarian cancers.

CONCLUSIONS

Low CCBL2 expression is a promising poor BC survival independent prognostic marker.

MRI as a biomarker for breast cancer diagnosis and prognosis

Breast cancer (BC) is the most frequently diagnosed female invasive cancer in Western countries and the leading cause of cancer-related death worldwide. Nowadays, tumor heterogeneity is a well-known characteristic of BC, since it includes several nosological entities characterized by different morphologic features, clinical course and response to treatment. Thus, with the spread of molecular biology technologies and the growing knowledge of the biological processes underlying the development of BC, the importance of imaging biomarkers as non-invasive information about tissue hallmarks has progressively grown. To date, breast magnetic resonance imaging (MRI) is considered indispensable in breast imaging practice, with widely recognized indications such as BC screening in females at increased risk, locoregional staging and neoadjuvant therapy (NAT) monitoring. Moreover, breast MRI is increasingly used to assess not only the morphologic features of the pathological process but also to characterize individual phenotypes for targeted therapies, building on developments in genomics and molecular biology features. The aim of this review is to explore the role of breast multiparametric MRI in providing imaging biomarkers, leading to an improved differentiation of benign and malignant breast lesions and to a customized management of BC patients in monitoring and predicting response to treatment. Finally, we discuss how breast MRI biomarkers offer one of the most fertile ground for artificial intelligence (AI) applications. In the era of personalized medicine, with the development of omics-technologies, machine learning and big data, the role of imaging biomarkers is embracing new opportunities for BC diagnosis and treatment.

Dynamic contrast-enhanced MRI radiomics nomogram for predicting axillary lymph node metastasis in breast cancer

Purpose

The goal of this study is to develop and validate a radiomics nomogram integrating the radiomics features from DCE-MRI and clinical factors for the preoperative diagnosis of axillary lymph node (ALN) metastasis in breast cancer patients.

Procedures

A total of 432 patients with breast cancer were enrolled in this retrospective study and divided into a training cohort (n = 296) and a validation cohort (n = 136). Radiomics features were extracted from the second phase of dynamic contrast enhanced (DCE) MRI images. The least absolute shrinkage and selection operator (LASSO) regression method was used to screen optimal features and construct a radiomics signature in the training cohort. Multivariable logistic regression analysis was used to establish a radiomics nomogram model based on the radiomics signature and clinical factors. The predictive performance of the nomogram was quantified with respect to discrimination and calibration, which was further evaluated in the independent validation cohort.

Results

Fourteen ALN metastasis-related features were selected to construct the radiomics signature, with an area under the curve (AUC) of 0.847 and 0.805 in the training and validation cohorts, respectively. The nomogram was established by incorporating the histological grade, multifocality, MRI report lymph node status and radiomics signature and showed good calibration and excellent performance for ALN detection (AUC of 0.907 and 0.874 in the training and validation cohorts, respectively). The decision curve, which demonstrated the radiomics nomogram, displayed promising clinical utility.

Conclusions

The radiomics nomogram can be used as a noninvasive and reliable tool to assist clinicians in accurately predicting ALN metastasis in breast cancer preoperatively.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40644-022-00450-w.

Multi-Task Fusion for Improving Mammography Screening Data Classification

Machine learning and deep learning methods have become essential for computer-assisted prediction in medicine, with a growing number of applications also in the field of mammography. Typically these algorithms are trained for a specific task, e.g., the classification of lesions or the prediction of a mammogram's pathology status. To obtain a comprehensive view of a patient, models which were all trained for the same task(s) are subsequently ensembled or combined. In this work, we propose a pipeline approach, where we first train a set of individual, task-specific models and subsequently investigate the fusion thereof, which is in contrast to the standard model ensembling strategy. We fuse model predictions and high-level features from deep learning models with hybrid patient models to build stronger predictors on patient level. To this end, we propose a multi-branch deep learning model which efficiently fuses features across different tasks and mammograms to obtain a comprehensive patient-level prediction. We train and evaluate our full pipeline on public mammography data, i.e., DDSM and its curated version CBIS-DDSM, and report an AUC score of 0.962 for predicting the presence of any lesion and 0.791 for predicting the presence of malignant lesions on patient level. Overall, our fusion approaches improve AUC scores significantly by up to 0.04 compared to standard model ensembling. Moreover, by providing not only global patient-level predictions but also task-specific model results that are related to radiological features, our pipeline aims to closely support the reading workflow of radiologists.

Background parenchymal enhancement and uptake as breast cancer imaging biomarkers: A state-of-the-art review

Within the past decade, background parenchymal enhancement (BPE) and background parenchymal uptake (BPU) have emerged as novel imaging-derived biomarkers in the diagnosis and treatment monitoring of breast cancer. Growing evidence supports the role of breast parenchyma vascularity and metabolic activity as probable risk factors for breast cancer development. Furthermore, in the presence of a newly-diagnosed breast cancer, added clinically-relevant data was surprisingly found in the respective imaging properties of the non-affected contralateral breast. Evaluation of the contralateral BPE and BPU have been found to be especially instrumental in predicting the prognosis of a patient with breast cancer and even anticipating their response to neoadjuvant chemotherapy. Simultaneously, further research has found a link between these two biomarkers, even though they represent different physical properties. The aim of this review is to provide an up to date summary of the current clinical applications of BPE and BPU as breast cancer imaging biomarkers with the hope that it propels their further usage in clinical practice.

Breast imaging: Beyond the detection

Breast cancer is a heterogeneous disease nowadays, including different biological subtypes with a variety of possible treatments, which aim to achieve the best outcome in terms of response to therapy and overall survival. In recent years breast imaging has evolved considerably, and the ultimate goal is to predict these strong phenotypic differences noninvasively. Indeed, breast cancer multiparametric studies can highlight not only qualitative imaging parameters, as the presence/absence of a likely malignant finding, but also quantitative parameters, suggesting clinical-pathological features through the evaluation of imaging biomarkers. A further step has been the introduction of artificial intelligence and in particular radiogenomics, that investigates the relationship between breast cancer imaging characteristics and tumor molecular, genomic and proliferation features. In this review, we discuss the main techniques currently in use for breast imaging, their respective fields of use and their technological and diagnostic innovations.

DNAJC3-AS1 Is Associated with Proliferation, Metastasis, and Poor Prognosis of Breast Cancer

Objective

Long noncoding RNA DNAJC3-AS1 (DNAJC3-AS1) was a newly identified tumor-related lncRNA. The aim of the present study was to explore the prognostic value and diagnostic of DNAJC3-AS1 (DNAJC3-AS1) expression in breast cancer (BC) patients. Patients and Methods. The expression of DNAJC3-AS1 was detected in 170 BC tissues and matched normal breast samples by qRT-PCR. The diagnostic value of DNAJC3-AS1 was examined by receiver-operating characteristic (ROC) assays. The correlation of DNAJC3-AS1 with clinicopathological features and prognosis was also statistically analyzed. CCK-8 assays, colony formation assays, and Transwell assays were applied to examine the potential function of DNAJC3-AS1 on tumor progression. Western blot was used to examine the expression of EMT-related proteins.

Results

The expression of DNAJC3-AS1 in BC specimens was higher than that in the adjacent nontumor tissues (p < 0.01). Diagnostic assays revealed that DNAJC3-AS1 has considerable diagnostic accuracy, with an area under the ROC curve (AUC) of 0.7457 (p < 0.001). High DNAJC3-AS1 expression was positively associated with lymph node metastasis (p = 0.010) and clinical stage (p = 0.023). A survival study revealed that patients with high DNAJC3-AS1 expression had shorter overall survival (p = 0.0067) and disease-free survival (p < 0.0001) than those with low DNAJC3-AS1 expression. More importantly, multivariate assays indicated that DNAJC3-AS1 was an independent prognostic factor in BC patients. Functional assays confirmed that silence of DNAJC3-AS1 distinctly suppressed the proliferation, metastasis, and EMT progress of BC cells.

Conclusions

DNAJC3-AS1 may be a prognostic and diagnostic biomarker for BC patients.

The potential role of abnormal angiotensin-converting enzyme 2 expression correlated with immune infiltration after SARS-CoV-2 infection in the prognosis of breast cancer

The potential role of abnormal ACE2 expression after SARS-CoV-2 infection in the prognosis of breast cancer is still ambiguous. In this study, we analyzed ACE2 changes in breast cancer and studied the correlation between ACE2 and the prognosis and further analyzed the relationship between immune infiltration and the prognosis of different breast cancer subtypes. Finally, we inferred the prognosis of breast cancer patients after SARS-CoV-2 infection. We found that ACE2 expression decreased significantly in breast cancer, except for basal-like subtype. Decreased ACE2 expression level was correlated with abnormal immune infiltration and poorer prognosis of luminal B breast cancer (RFS: HR 0.76, 95%CI=0.63-0.92, p=0.005; DMFS: HR 0.70, 95%CI=0.49-1.00, p=0.046). The expression of ACE2 was strongly positively correlated with the immune infiltration level of CD8⁺ T cell (r=0.184, p<0.001), CD4⁺ T cell (r=0.104, p=0.02) and neutrophils (r=0.101, p=0.02). ACE2 expression level in the luminal subtype was positively correlated with CD8A and CD8B markers in CD8+ T cells, and CEACAM3, S100A12 in neutrophils. In conclusion, breast tumor tissues might undergo a further decrease in the expression level of ACE2 after SARS-CoV-2 infection, which could contribute to further deterioration of immune infiltration and worsen the prognosis of luminal B breast cancer after SARS-CoV-2 infection.

Screening Breast MRI and the Science of Premenopausal Background Parenchymal Enhancement

The significance of background parenchymal enhancement (BPE) on screening and diagnostic breast MRI continues to be elucidated. Background parenchymal enhancement was initially deemed probably benign and followed or thought of as an artifact degrading the accuracy of breast cancer detection on breast MRI examinations. Subsequent research has focused on understanding the role of BPE regarding screening breast MRI. Today, there is growing evidence that a myriad of factors affect BPE, which in turn may influence patient outcomes. Additionally, BPE could represent an important risk factor for the future development of breast cancer. This article aims to describe the most up-to-date research on BPE as it relates to screening breast MRI in premenopausal women.

Diagnostics and Therapeutics in Targeting HER2 Breast Cancer: A Novel Approach

Breast cancer is one of the most commonly occurring cancers in women globally and is the primary cause of cancer mortality in females. BC is highly heterogeneous with various phenotypic expressions. The overexpression of HER2 is responsible for 15-30% of all invasive BC and is strongly associated with malignant behaviours, poor prognosis and decline in overall survival. Molecular imaging offers advantages over conventional imaging modalities, as it provides more sensitive and specific detection of tumours, as these techniques measure the biological and physiological processes at the cellular level to visualise the disease. Early detection and diagnosis of BC is crucial to improving clinical outcomes and prognosis. While HER2-specific antibodies and nanobodies may improve the sensitivity and specificity of molecular imaging, the radioisotope conjugation process may interfere with and may compromise their binding functionalities. Aptamers are single-stranded oligonucleotides capable of targeting biomarkers with remarkable binding specificity and affinity. Aptamers can be functionalised with radioisotopes without compromising target specificity. The attachment of different radioisotopes can determine the aptamer's functionality in the treatment of HER2(+) BC. Several HER2 aptamers and investigations of them have been described and evaluated in this paper. We also provide recommendations for future studies with HER2 aptamers to target HER2(+) BC.

Exosomes in Breast Cancer - Mechanisms of Action and Clinical Potential

Extracellular vesicles (EV) are a heterogeneous group of cell-derived membrane vesicles comprising apoptotic bodies, microvesicles, and small EVs also called as exosomes. Exosomes when initially identified were considered as a waste product but the advancement in research techniques have provided insight into the important roles of exosomes in cell-cell communication, various biological processes and diseases, including cancer. As an important component of EVs, exosomes contain various biomolecules such as miRNAs, lipids, and proteins that largely reflect the characteristics of their parent cells. Notably, cancer cells generate and secrete many more exosomes than normal cells. A growing body of evidence suggests that exosomes, as mediators of intercellular cross-talk, play a role in tumorigenesis, cancer cell invasion, angiogenesis, tumor microenvironment (TME) formation, and cancer metastasis. As we gain more insights into the association between exosomes and cancer, the potential of exosomes for clinical use is becoming more intriguing. This review is focused on the role of exosomes in breast cancer, in terms of breast cancer biology, mechanism of action, potential as biomarkers, and therapeutic opportunities.

Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML)

The identification of reliable and non-invasive oncology biomarkers remains a main priority in healthcare. There are only a few biomarkers that have been approved as diagnostic for cancer. The most frequently used cancer biomarkers are derived from either biological materials or imaging data. Most cancer biomarkers suffer from a lack of high specificity. However, the latest advancements in machine learning (ML) and artificial intelligence (AI) have enabled the identification of highly predictive, disease-specific biomarkers. Such biomarkers can be used to diagnose cancer patients, to predict cancer prognosis, or even to predict treatment efficacy. Herein, we provide a summary of the current status of developing and applying Magnetic resonance imaging (MRI) biomarkers in cancer care. We focus on all aspects of MRI biomarkers, starting from MRI data collection, preprocessing and machine learning methods, and ending with summarizing the types of existing biomarkers and their clinical applications in different cancer types.

Images Are Data: A Breast Imaging Perspective on a Contemporary Paradigm

BACKGROUND

 Considering radiological examinations not as mere images, but as a source of data, has become the key paradigm in the diagnostic imaging field. This change of perspective is particularly popular in breast imaging. It allows breast radiologists to apply algorithms derived from computer science, to realize innovative clinical applications, and to refine already established methods. In this context, the terminology "imaging biomarker", "radiomics", and "artificial intelligence" are of pivotal importance. These methods promise noninvasive, low-cost (e. g., in comparison to multigene arrays), and workflow-friendly (automated, only one examination, instantaneous results, etc.) delivery of clinically relevant information.

METHODS AND RESULTS

 This paper is designed as a narrative review on the previously mentioned paradigm. The focus is on key concepts in breast imaging and important buzzwords are explained. For all areas of breast imaging, exemplary studies and potential clinical use cases are discussed.

CONCLUSION

 Considering radiological examination as a source of data may optimize patient management by guiding individualized breast cancer diagnosis and oncologic treatment in the age of precision medicine.

KEY POINTS

  · In conventional breast imaging, examinations are interpreted based on patterns perceivable by visual inspection.. · The radiomics paradigm treats breast images as a source of data, containing information beyond what is visible to our eyes.. · This results in radiomic signatures that may be considered as imaging biomarkers, as they provide diagnostic, predictive, and prognostic information.. · Radiomics derived imaging biomarkers may be used to individualize breast cancer treatment in the era of precision medicine.. · The concept and key research of radiomics in the field of breast imaging will be discussed in this narrative review..

CITATION FORMAT

· Dietzel M, Clauser P, Kapetas P et al. Images Are Data: A Breast Imaging Perspective on a Contemporary Paradigm. Fortschr Röntgenstr 2021; 193: 898 - 908.

Multiparametric ultrasound examination for response assessment in breast cancer patients undergoing neoadjuvant therapy

To investigate the performance of multiparametric ultrasound for the evaluation of treatment response in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). The IRB approved this prospective study. Breast cancer patients who were scheduled to undergo NAC were invited to participate in this study. Changes in tumour echogenicity, stiffness, maximum diameter, vascularity and integrated backscatter coefficient (IBC) were assessed prior to treatment and 7 days after four consecutive NAC cycles. Residual malignant cell (RMC) measurement at surgery was considered as standard of reference. RMC < 30% was considered a good response and > 70% a poor response. The correlation coefficients of these parameters were compared with RMC from post-operative histology. Linear Discriminant Analysis (LDA), cross-validation and Receiver Operating Characteristic curve (ROC) analysis were performed. Thirty patients (mean age 56.4 year) with 42 lesions were included. There was a significant correlation between RMC and echogenicity and tumour diameter after the 3rd course of NAC and average stiffness after the 2nd course. The correlation coefficient for IBC and echogenicity calculated after the first four doses of NAC were 0.27, 0.35, 0.41 and 0.30, respectively. Multivariate analysis of the echogenicity and stiffness after the third NAC revealed a sensitivity of 82%, specificity of 90%, PPV = 75%, NPV = 93%, accuracy = 88% and AUC of 0.88 for non-responding tumours (RMC > 70%). High tumour stiffness and persistent hypoechogenicity after the third NAC course allowed to accurately predict a group of non-responding tumours. A correlation between echogenicity and IBC was demonstrated as well.

Whole-Volume Tumor MRI Radiomics for Prognostic Modeling in Endometrial Cancer

Background

In endometrial cancer (EC), preoperative pelvic MRI is recommended for local staging, while final tumor stage and grade are established by surgery and pathology. MRI‐based radiomic tumor profiling may aid in preoperative risk‐stratification and support clinical treatment decisions in EC.

Purpose

To develop MRI‐based whole‐volume tumor radiomic signatures for prediction of aggressive EC disease.

Study Type

Retrospective.

Population

A total of 138 women with histologically confirmed EC, divided into training (n_T = 108) and validation cohorts (n_V = 30).

Field Strength/Sequence

Axial oblique T₁‐weighted gradient echo volumetric interpolated breath‐hold examination (VIBE) at 1.5T (71/138 patients) and DIXON VIBE at 3T (67/138 patients) at 2 minutes postcontrast injection.

Assessment

Primary tumors were manually segmented by two radiologists with 4 and 8 years' of experience. Radiomic tumor features were computed and used for prediction of surgicopathologically‐verified deep (≥50%) myometrial invasion (DMI), lymph node metastases (LNM), advanced stage (FIGO III + IV), nonendometrioid (NE) histology, and high‐grade endometrioid tumors (E3). Corresponding analyses were also conducted using radiomics extracted from the axial oblique image slice depicting the largest tumor area.

Statistical Tests

Logistic least absolute shrinkage and selection operator (LASSO) was applied for radiomic modeling in the training cohort. The diagnostic performances of the radiomic signatures were evaluated by area under the receiver operating characteristic curve in the training (AUC_T) and validation (AUC_V) cohorts. Progression‐free survival was assessed using the Kaplan–Meier and Cox proportional hazard model.

Results

The whole‐tumor radiomic signatures yielded AUC_T/AUC_V of 0.84/0.76 for predicting DMI, 0.73/0.72 for LNM, 0.71/0.68 for FIGO III + IV, 0.68/0.74 for NE histology, and 0.79/0.63 for high‐grade (E3) tumor. Single‐slice radiomics yielded comparable AUC_T but significantly lower AUC_V for LNM and FIGO III + IV (both P < 0.05). Tumor volume yielded comparable AUC_T to the whole‐tumor radiomic signatures for prediction of DMI, LNM, FIGO III + IV, and NE, but significantly lower AUC_T for E3 tumors (P < 0.05). All of the whole‐tumor radiomic signatures significantly predicted poor progression‐free survival with hazard ratios of 4.6–9.8 (P < 0.05 for all).

Data Conclusion

MRI‐based whole‐tumor radiomic signatures yield medium‐to‐high diagnostic performance for predicting aggressive EC disease. The signatures may aid in preoperative risk assessment and hence guide personalized treatment strategies in EC.

Level of Evidence

4

Technical Efficacy Stage

2

Imaging of Breast Cancers With Predilection for Nonmass Pattern of Growth: Invasive Lobular Carcinoma and DCIS-Does Imaging Capture It All?

OBJECTIVE. Invasive lobular carcinoma (ILC) and ductal carcinoma in situ (DCIS) are distinct histopathologic entities with several commonalities: both have subtle clinical and imaging presentation, have been linked with controversy regarding optimal imaging techniques and management, and exemplify the codependence of adequate imaging evaluation and optimal treatment strategies in breast care. CONCLUSION. We review molecular mechanisms and histopathologic patterns that define the biologic behavior of both ILC and DCIS and discuss how these mechanisms translate into distinct clinical and imaging presentations that affect the staging workup and patient management algorithm.

Advanced Quantitative Spine Imaging

Although advanced quantitative imaging may not be currently used to any degree in the routine reporting of spinal examinations, this situation will change in the not too distant future. Advanced quantitative imaging has already allowed us to understand a great deal more regarding spinal development, marrow physiology, and disease pathogenesis. Radiologists are ideally suited to drive this research forward. To speed up this process and optimize the impact of studies reporting spine quantitative data, we should work toward universal standards on the acquisition of spine data that will allow quantitative studies to be more easily compared, contrasted, and amalgamated.

Discovery and Validation of a CT-Based Radiomic Signature for Preoperative Prediction of Early Recurrence in Hypopharyngeal Carcinoma

Purpose

In the clinical management of hypopharyngeal squamous cell carcinoma (HSCC), preoperative identification of early recurrence (≤2 years) after curative resection is essential. Thus, we aimed to develop a CT-based radiomic signature to predict early recurrence in HSCC patients preoperatively.

Methods

In total, 167 HSCC patients who underwent partial surgery were enrolled in this retrospective study and divided into two groups, i.e., the training cohort (n = 133) and the validation cohort (n = 34). Each individual was followed up for at least for 2 years. Radiomic features were extracted from CT images, and the radiomic signature was built with the least absolute shrinkage and selection operator (LASSO) logistic regression (LR) model. The associations of preoperative clinical factors with early recurrence were evaluated. A radiomic signature-combined model was built, and the area under the curve (AUC) was used to explore their performance in discriminating early recurrence.

Results

Among the 1415 features, 335 of them were selected using the variance threshold method. Then, the SelectKBest method was further used for the selection of 31 candidate features. Finally, 11 out of 31 optimal features were identified with the LASSO algorithm. In the LR classifier, the AUCs of the training and validation sets in discriminating early recurrence were 0.83 (95% CI: 0.76-0.90) (sensitivity 0.8 and specificity 0.83) and 0.83 (95% CI: 0.67-0.99) (sensitivity 0.69 and specificity 0.71), respectively.

Conclusions

Using the radiomic signature, we developed a radiomic signature to preoperatively predict early recurrence in patients with HSCC, which may serve as a potential noninvasive tool to guide personalized treatment.

ACLY: A biomarker of recurrence in breast cancer

OBJECTIVE

ACLY is a cytoplasmic metabolic enzyme involved in lipid synthesis. It also affects proliferation and metastasis of breast cancer. However, the correlation of ACLY expression with breast cancer recurrence is unclear.

METHODS

The Oncomine and TCGA databases were used to investigate the mRNA expression of ACLY in breast cancer. Immunohistochemistry (IHC) was used to evaluate ACLY expression level in tumor tissues and normal tissues from 127 breast cancer patients. Next, the prognostic role of ACLY was explored by analyzing the clinicopathological features and prognosis during follow-up. The role of ACLY in breast cancer cells drug resistance was further detected by CCK-8 assays and quantitative real-time polymerase chain reaction (qRT-PCR).

RESULTS

ACLY mRNA and protein expression was significantly increased in the breast cancer tissues compared to normal tissues. Clinically, high ACLY levels were associated with ER status, PR status, tumor size, TNM stage, and lymph node invasion. Upregulated ACLY predicted worse tumor relapse-free survival (RFS) of breast cancer patients in univariate analyses and in multivariate models. In subgroup analysis, patients with high ACLY expression showed worse RFS in the TNM III or ER positive subgroups. Moreover, ACLY over-expression induced the resistance of breast cancer cells to docetaxel and promoted the expression of multi-drug resistant protein ABCB1/ABCG2.

CONCLUSIONS

Our study highlights the possibility of ACLY as a potential and independent biomarker for the recurrence prediction in breast cancer patients. It may be related to ACLY promoting drug resistance in breast cancer cells.

A review on methods for diagnosis of breast cancer cells and tissues

Breast cancer has seriously been threatening physical and mental health of women in the world, and its morbidity and mortality also show clearly upward trend in China over time. Through inquiry, we find that survival rate of patients with early‐stage breast cancer is significantly higher than those with middle‐ and late‐stage breast cancer, hence, it is essential to conduct research to quickly diagnose breast cancer. Until now, many methods for diagnosing breast cancer have been developed, mainly based on imaging and molecular biotechnology examination. These methods have great contributions in screening and confirmation of breast cancer. In this review article, we introduce and elaborate the advances of these methods, and then conclude some gold standard diagnostic methods for certain breast cancer patients. We lastly discuss how to choose the most suitable diagnostic methods for breast cancer patients. In general, this article not only summarizes application and development of these diagnostic methods, but also provides the guidance for researchers who work on diagnosis of breast cancer.

Relationship Between Breast Ultrasound Background Echotexture and Magnetic Resonance Imaging Background Parenchymal Enhancement and the Effect of Hormonal Status Thereon

We studied the relationship between breast ultrasound background echotexture (BET) and magnetic resonance imaging (MRI) background parenchymal enhancement (BPE) and whether this relationship varied with hormonal status and amount of fibroglandular tissue (FGT) on MRI. Two hundred eighty-three Korean women (52.1 years; range = 27-79 years) with newly diagnosed primary breast cancer who underwent preoperative breast ultrasound and MRI were retrospectively studied. Background echotexture, BPE, and FGT were classified into 4 categories, and age, menopausal status, menstrual cycle regularity, and menstrual cycle stage at MRI were recorded. Background echotexture and BPE relationship was assessed overall, and in menopausal, FGT, menstrual cycle regularity, and menstrual cycle stage subgroups. Background echotexture and BPE correlated in women overall, and menopausal, FGT, and menstrual cycle subgroups and those in the first half of the cycle (all P < 0.001). Background echotexture reflects BPE, regardless of menopausal status, menstrual cycle regularity, and FGT and may be a biomarker of breast cancer risk.

Quantitative Multiparametric Breast Ultrasound: Application of Contrast-Enhanced Ultrasound and Elastography Leads to an Improved Differentiation of Benign and Malignant Lesions

OBJECTIVES

The aim of this study was to evaluate breast multiparametric ultrasound (mpUS) and its potential to reduce unnecessary breast biopsies with 1, 2, or 3 additional quantitative parameters (Doppler, elastography, and contrast-enhanced ultrasound [CEUS]) to B-mode and investigate possible variations with different reader experience.

MATERIALS AND METHODS

This prospective study included 124 women (age range, 18-82 years; mean, 52 years), each with 1 new breast lesion, scheduled for ultrasound-guided biopsy between October 2015 and September 2016. Each lesion was examined with B-mode, elastography (Virtual Touch IQ [VTIQ]), Doppler, and CEUS, and different quantitative parameters were recorded for each modality. Four readers (2 experienced breast radiologists and 2 in-training) independently evaluated B-mode images of each lesion and assigned a BI-RADS (Breast Imaging Reporting and Data System) score. Using the area under the receiver operating characteristic curve (AUC), the most accurate quantitative parameter for each modality was chosen. These were then combined with the BI-RADS scores of all readers. Descriptive statistics and AUC were used to evaluate the diagnostic performance of mpUS.

RESULTS

Sixty-five lesions were malignant. MpUS with B-mode and 2 additional quantitative parameters (VTIQ and CEUS or Doppler) showed the highest diagnostic performance for all readers (averaged AUCs, 0.812-0.789 respectively vs 0.683 for B-mode, P = 0.0001). Both combinations significantly reduced the number of false-positive findings up to 46.9% (P < 0.0001).

CONCLUSIONS

Quantitative mpUS with 2 different triple assessment modalities (B-mode, VTIQ elastography, CEUS, or Doppler) shows the best diagnostic performance for breast cancer diagnosis and leads to a significant reduction of false-positive biopsy recommendations, for both experienced and inexperienced readers.

Injectable Biodegradable Multimodal Mammography Marker

Introducing temporary markers for imaging studies is an idea, which in the proper clinical settings can be advantageous for patient compliance and in selected cases where a permanent marker is nondesirable. Hence, we developed injectable marker formulation using a biodegradable "pasty polymer" of poly(ricinoleic acid-co-sebacic acid) (PSA:RA) containing iodixanol and iron oxide as contrast agents that can serve as a visual marker for the region suspected to have tumor growth. The goal of this work is to noninvasively evaluate the visibility, shape, and degradation of the injectable PSA:RA formulation using magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US). Prescreening of the marker formulation was performed under MRI and CT scanning using agar gel phantom models with poly(l-lactide-co-ε-caprolactone) (PCL:LA) solid inserts (clips) that contained varying combinations of the contrast agents. The contrast agent combination with the PCL:LA clip that had the best visibility in both MRI and CT was selected and additionally tested as in PSA:RA formulation. Further, we evaluated the PSA:RA marker placement in bovine liver and poultry muscles. The PSA:RA formulation is predictable with good MRI, CT, and US visibility and shows no in vivo systemic toxicity symptoms when implanted subcutaneously in mice. Further, the advantage of PSA:RA formulation is its undefined shape and ease of injecting through a small gauge needle, making it possible to reach into the regions of the body.

Nanotechnology based therapeutic application in cancer diagnosis and therapy

Due to the lack of early diagnosis, cancer remains as one of the leading cause of human mortality. Inability to translate research into clinical trials and also inability of chemotherapeutics delivery to targeted tumor sites are major drawbacks in cancer therapeutics. With the emergence of nanomedicine, several nanoprobes (conjugated with targeting ligands and chemotherapeutic drugs) are developed. It can interact with biological system and thus sense and monitor the biological events with high efficiency and accuracy along with therapy application. Nanoparticles like gold and iron oxide are frequently used in the computed tomography and magnetic resonance imaging applications, respectively. Moreover, enzymatic activity of gold and iron oxide nanoparticles enables the visible colorimetric diagnostic of cancer cells, whereas, fluorescence property of quantum dots and upconversion nanoparticles helps in in vivo imaging application. Other than this, drug conjugation with nanoparticles also reduces the systemic toxic effect of chemotherapeutic drugs. Due to their several unique intrinsic properties, nanoparticles itself can also be employed as therapeutics in cancer treatment by photothermal therapy (PTT) and photodynamic therapy (PDT). Thus, the main focus of this review is to emphasize on current progress in diagnostic and therapeutic application of nanoprobes in cancer.

Impact of Molybdenum Compounds as Anticancer Agents

The aim of this mini review was to report the molybdenum compound intervention to control cancer disease. The intervention explains its roles and progress from inorganic molybdenum compounds via organomolybdenum complexes to its nanoparticles to control oesophageal cancer and breast cancer as case studies. Main contributions of molybdenum compounds as anticancer agents could be observed in their nanofibrous support with suitable physicochemical properties, combination therapy, and biosensors (biomarkers). Recent areas in anticancer drug design, which entail the uses of selected targets, were also surveyed and proposed.

MTMR3 is upregulated in patients with breast cancer and regulates proliferation, cell cycle progression and autophagy in breast cancer cells

As a member of the myotubularin family, myotubularin related protein 3 (MTMR3) has been demonstrated to participate in tumor development, including oral and colon cancer. However, little is known about its functional roles in breast cancer. In the present study, the expression of MTMR3 in breast cancer was evaluated by immunohistochemical staining of tumor tissues from 172 patients. Online data was then used for survival analysis from the PROGgeneV2 database. In vitro, MTMR3 expression was silenced in MDA-MB-231 cells via lentiviral shRNA transduction. MTT, colony formation and flow cytometry assays were performed in the control and MTMR3-silenced cells to evaluate the cell growth, proliferation and cell cycle phase distribution, respectively. Western blotting was used to evaluate the protein expression levels of autophagy-related markers. The results demonstrated that the expression of MTMR3 in breast cancer tissues was significantly increased compared with adjacent normal tissues. MTMR3 was highly expressed in triple-negative breast cancer and was associated with disease recurrence. MTMR3 knockdown in MDA-MB-231 cells inhibited cell proliferation and induced cell cycle arrest and autophagy. The present results indicated that MTMR3 may have an important role in promoting the progression of breast cancer, and its inhibition may serve as a promising therapeutic target for breast cancer treatment.

Combined Benefit of Quantitative Three-Compartment Breast Image Analysis and Mammography Radiomics in the Classification of Breast Masses in a Clinical Data Set

Purpose To investigate the combination of mammography radiomics and quantitative three-compartment breast (3CB) image analysis of dual-energy mammography to limit unnecessary benign breast biopsies. Materials and Methods For this prospective study, dual-energy craniocaudal and mediolateral oblique mammograms were obtained immediately before biopsy in 109 women (mean age, 51 years; range, 31-85 years) with Breast Imaging Reporting and Data System category 4 or 5 breast masses (35 invasive cancers, 74 benign) from 2013 through 2017. The three quantitative compartments of water, lipid, and protein thickness at each pixel were calculated from the attenuation at high and low energy by using a within-image phantom. Masses were automatically segmented and features were extracted from the low-energy mammograms and the quantitative compartment images. Tenfold cross-validations using a linear discriminant classifier with predefined feature signatures helped differentiate between malignant and benign masses by means of (a) water-lipid-protein composition images alone, (b) mammography radiomics alone, and (c) a combined image analysis of both. Positive predictive value of biopsy performed (PPV₃) at maximum sensitivity was the primary performance metric, and results were compared with those for conventional diagnostic digital mammography. Results The PPV₃ for conventional diagnostic digital mammography in our data set was 32.1% (35 of 109; 95% confidence interval [CI]: 23.9%, 41.3%), with a sensitivity of 100%. In comparison, combined mammography radiomics plus quantitative 3CB image analysis had PPV₃ of 49% (34 of 70; 95% CI: 36.5%, 58.9%; P < .001), with a sensitivity of 97% (34 of 35; 95% CI: 90.3%, 100%; P < .001) and 35.8% (39 of 109) fewer total biopsies (P < .001). Conclusion Quantitative three-compartment breast image analysis of breast masses combined with mammography radiomics has the potential to reduce unnecessary breast biopsies. © RSNA, 2018 Online supplemental material is available for this article.

Predicting the response to neoadjuvant therapy for early-stage breast cancer: tumor-, blood-, and imaging-related biomarkers

Neoadjuvant therapy (NAT) has been used increasingly in patients with locally advanced or early-stage breast cancer. However, the accurate evaluation and prediction of response to NAT remain the great challenge. Biomarkers could prove useful to identify responders or nonresponders, or even to distinguish between early and delayed responses. These biomarkers could include markers from the tumor itself, such as versatile proteins, genes, and ribonucleic acids, various biological factors or peripheral blood cells, and clinical and pathological features. Possible predictive markers could also include multiple features from functional imaging, such as standard uptake values in positron emission tomography, apparent diffusion coefficient in magnetic resonance, or radiomics imaging biomarkers. In addition, cells that indirectly present the immune status of tumor cells and/or their host could also potentially be used as biomarkers, eg, tumor-infiltrating lymphocytes, tumor-associated macrophages, and myeloid-derived suppressor cells. Though numerous biomarkers have been widely investigated, only estrogen and/or progesterone receptors and human epidermal growth factor receptor have been proven to be reliable biomarkers to predict the response to NAT. They are the only biomarkers recommended in several international guidelines. The other aforementioned biomarkers warrant further validation studies. Some multigene profiling assays that are commercially available, eg, Oncotype DX and MammaPrint, should be used with caution when extrapolated to NAT settings. A panel of combined multilevel biomarkers might be able to predict the response to NAT more robustly than individual biomarkers. To establish such a panel and its prediction model, reliable methods and extensive clinical validation are warranted.

Role of DCE-MR in predicting breast cancer subtypes

OBJECTIVE

The purpose of this retrospective study is to find a correlation between dynamic contrast-enhanced MR features with histological, immunohistochemical and loco-regional characteristics of breast cancer.

MATERIALS AND METHODS

A total of 149 patients with histopathologically confirmed invasive breast carcinoma underwent MR imaging. Histological analysis included: histological features (histological type, necrosis, vascular invasion and Mib-1), immunohistochemical characterization (immunophenotype, receptor status, HER2-neu and grading) and loco-regional characteristics (T and N). The kinetic MR features analyzed were: curve type, maximum enhancement, time to peak, wash-in and wash-out rate, brevity of enhancement and area under curve.

RESULTS

MRI kinetic parameters and immunohistological features were compared using chi square test, two-tailed student t test and Anova test, with p = 0.05 level of significance. Vascular invasion was shown to be significantly related to time to peak (p = 0.02). The immunohistotype was shown to be significantly related with maximum enhancement (p = 0.05), time to peak (p = 0.04) and wash-in rate (p = 0.01). ER status correlates with maximum and relative enhancement (p = 0.004 and p = 0.028), wash-in rate (p = 0.0018) and area under curve (p = 0.006). PR status was significantly related to time to peak (p = 0.048) and wash-in rate (p = 0.05).

CONCLUSION

Maximum enhancement absolute and relative, time to peak, wash-in rate and area under the curve significantly correlate with several prognostic factors, like ER status, immune profile and tumoral vascular invasion, and may predict the aggressiveness of the tumor.

Application of Radiomics and Decision Support Systems for Breast MR Differential Diagnosis

Over the years, MR systems have evolved from imaging modalities to advanced computational systems producing a variety of numerical parameters that can be used for the noninvasive preoperative assessment of breast pathology. Furthermore, the combination with state-of-the-art image analysis methods provides a plethora of quantifiable imaging features, termed radiomics that increases diagnostic accuracy towards individualized therapy planning. More importantly, radiomics can now be complemented by the emerging deep learning techniques for further process automation and correlation with other clinical data which facilitate the monitoring of treatment response, as well as the prediction of patient's outcome, by means of unravelling of the complex underlying pathophysiological mechanisms which are reflected in tissue phenotype. The scope of this review is to provide applications and limitations of radiomics towards the development of clinical decision support systems for breast cancer diagnosis and prognosis.