Existing and Emerging Breast Cancer Detection Technologies and Its Challenges: A Review

Barriers and facilitators of artificial intelligence conception and implementation for breast imaging diagnosis in clinical practice: a scoping review

OBJECTIVE

Although artificial intelligence (AI) has demonstrated promise in enhancing breast cancer diagnosis, the implementation of AI algorithms in clinical practice encounters various barriers. This scoping review aims to identify these barriers and facilitators to highlight key considerations for developing and implementing AI solutions in breast cancer imaging.

METHOD

A literature search was conducted from 2012 to 2022 in six databases (PubMed, Web of Science, CINHAL, Embase, IEEE, and ArXiv). The articles were included if some barriers and/or facilitators in the conception or implementation of AI in breast clinical imaging were described. We excluded research only focusing on performance, or with data not acquired in a clinical radiology setup and not involving real patients.

RESULTS

A total of 107 articles were included. We identified six major barriers related to data (B1), black box and trust (B2), algorithms and conception (B3), evaluation and validation (B4), legal, ethical, and economic issues (B5), and education (B6), and five major facilitators covering data (F1), clinical impact (F2), algorithms and conception (F3), evaluation and validation (F4), and education (F5).

CONCLUSION

This scoping review highlighted the need to carefully design, deploy, and evaluate AI solutions in clinical practice, involving all stakeholders to yield improvement in healthcare.

CLINICAL RELEVANCE STATEMENT

The identification of barriers and facilitators with suggested solutions can guide and inform future research, and stakeholders to improve the design and implementation of AI for breast cancer detection in clinical practice.

KEY POINTS

• Six major identified barriers were related to data; black-box and trust; algorithms and conception; evaluation and validation; legal, ethical, and economic issues; and education. • Five major identified facilitators were related to data, clinical impact, algorithms and conception, evaluation and validation, and education. • Coordinated implication of all stakeholders is required to improve breast cancer diagnosis with AI.

miR-125 in Breast Cancer Etiopathogenesis: An Emerging Role as a Biomarker in Differential Diagnosis, Regenerative Medicine, and the Challenges of Personalized Medicine

Breast Cancer (BC) is one of the most common cancer types worldwide, and it is characterized by a complex etiopathogenesis, resulting in an equally complex classification of subtypes. MicroRNA (miRNA or miR) are small non-coding RNA molecules that have an essential role in gene expression and are significantly linked to tumor development and angiogenesis in different types of cancer. Recently, complex interactions among coding and non-coding RNA have been elucidated, further shedding light on the complexity of the roles these molecules fulfill in cancer formation. In this context, knowledge about the role of miR in BC has significantly improved, highlighting the deregulation of these molecules as additional factors influencing BC occurrence, development and classification. A considerable number of papers has been published over the past few years regarding the role of miR-125 in human pathology in general and in several types of cancer formation in particular. Interestingly, miR-125 family members have been recently linked to BC formation as well, and complex interactions (competing endogenous RNA networks, or ceRNET) between this molecule and target mRNA have been described. In this review, we summarize the state-of-the-art about research on this topic.

Mammography using low-frequency electromagnetic fields with deep learning

In this paper, a novel technique for detecting female breast anomalous tissues is presented and validated through numerical simulations. The technique, to a high degree, resembles X-ray mammography; however, instead of using X-rays for obtaining images of the breast, low-frequency electromagnetic fields are leveraged. To capture breast impressions, a metasurface, which can be thought of as analogous to X-rays film, has been employed. To achieve deep and sufficient penetration within the breast tissues, the source of excitation is a simple narrow-band dipole antenna operating at 200 MHz. The metasurface is designed to operate at the same frequency. The detection mechanism is based on comparing the impressions obtained from the breast under examination to the reference case (healthy breasts) using machine learning techniques. Using this system, not only would it be possible to detect tumors (benign or malignant), but one can also determine the location and size of the tumors. Remarkably, deep learning models were found to achieve very high classification accuracy.

Correlation Between Circulating Cell-Free DNA Levels and Breast Cancer Subtypes: A Prospective Observational Study

INTRODUCTION

Breast cancer (BC), a heterogeneous disease, is one of the leading causes of cancer-related deaths among women worldwide. Circulating cell-free DNA (cfDNA) levels have been persistently reported to be elevated in BC patients. In the current study, we evaluated the correlation between the cfDNA levels in patients with BC and its subtypes.

METHODS

We recruited newly diagnosed, histopathologically confirmed BC patients aged >18 years (N=39), who did not have any previous malignancy, from the Department of Surgical Oncology, Indira Gandhi Institute of Medical Sciences (IGIMS), Patna, Bihar, India. A total of 6 ml of venous blood was collected from each subject; of this, 1 ml was subjected to complete blood count (CBC), and 4 ml was transferred to a clot-activated collection vial for plasma separation and the cfDNA isolation thereof. In addition to the basic demographic history of each patient, the information on the cancer subtype was as also recorded from the medical records of each patient. All the data were analysed by GraphPad Prism Version 8 (Insightful Science, LLC, San Diego, California, United States). One-way ANOVA was used to test the difference between more than two groups. Pearson correlation was also estimated between cfDNA levels and various CBC indices. A two-tailed p-value<0.05 was considered statistically significant.

RESULTS

The mean age of included patients was 48.6±8.20 years. The mean levels of cfDNA were 2.81±2.39 ng/µL. The mean counts of various blood cell types and other indices of CBC were in the normal range. Compared to BC patients with estrogen receptors (ER+), the cfDNA levels were significantly higher in patients with human epidermal growth factor receptor 2 (HER2+) and triple-negative BC (TNBC) (p<0.05).  Conclusion: The elevated levels of cfDNA in patients with BC can be a prognostic marker for the disease subtype. However, more replicative studies are warranted to substantiate our findings.

Current Situation of Diagnosis and Treatment of HER2-Positive Metastatic Breast Cancer Patients in China: A Nationwide Cross-Sectional Survey of Doctors

BACKGROUND

The Advanced Breast Cancer Alliance conducted a nationwide investigation to understand the current situation of the diagnosis and treatment of human epidermal growth factor receptor 2 (HER2)-positive metastatic breast cancer (MBC) patients.

METHODS

In 2019, electronic questionnaires including basic information about respondents, characteristics of patients, and the present status of diagnosis and treatment were sent to 495 doctors from 203 medical centers covering 28 provinces.

RESULTS

The factors that influenced treatment plans included the disease process, the performance status, and the economic status of patients. Regimens and response to neoadjuvant/adjuvant chemotherapy were important factors in the decision of the first-line treatment. Overall, 54% of doctors retained trastuzumab and replaced chemotherapy drugs in second-line treatment regimens for patients with progression-free survival (PFS) ≥ 6 months in the first-line setting, while 52% of participants chose pyrotinib plus capecitabine for patients with PFS < 6 months. Economic factors played an important role in doctors' decision-making and the varying treatment options for respondents in first-tier, second-tier, and other cities.

CONCLUSIONS

This large-scale survey regarding the diagnosis and treatment of HER2-positive MBC patients revealed that clinical decisions made by Chinese doctors followed the guidelines, but their choices were constrained by economic factors.

Optimized Intelligent Classifier for Early Breast Cancer Detection Using Ultra-Wide Band Transceiver

Breast cancer is the most common cancer diagnosed in women and the leading cause of cancer-related deaths among women worldwide. The death rate is high because of the lack of early signs. Due to the absence of a cure, immediate treatment is necessary to remove the cancerous cells and prolong life. For early breast cancer detection, it is crucial to propose a robust intelligent classifier with statistical feature analysis that considers parameter existence, size, and location. This paper proposes a novel Multi-Stage Feature Selection with Binary Particle Swarm Optimization (MSFS-BPSO) using Ultra-Wideband (UWB). A collection of 39,000 data samples from non-tumor and with tumor sizes ranging from 2 to 7 mm was created using realistic tissue-like dielectric materials. Subsequently, the tumor models were inserted into the heterogeneous breast phantom. The breast phantom with tumors was imaged and represented in both time and frequency domains using the UWB signal. Consequently, the dataset was fed into the MSFS-BPSO framework and started with feature normalization before it was reduced using feature dimension reduction. Then, the feature selection (based on time/frequency domain) using seven different classifiers selected the frequency domain compared to the time domain and continued to perform feature extraction. Feature selection using Analysis of Variance (ANOVA) is able to distinguish between class-correlated data. Finally, the optimum feature subset was selected using a Probabilistic Neural Network (PNN) classifier with the Binary Particle Swarm Optimization (BPSO) method. The research findings found that the MSFS-BPSO method has increased classification accuracy up to 96.3% and given good dependability even when employing an enormous data sample.

AI and Clinical Decision Making: The Limitations and Risks of Computational Reductionism in Bowel Cancer Screening

Advances in artificial intelligence in healthcare are frequently promoted as ‘solutions’ to improve the accuracy, safety, and quality of clinical decisions, treatments, and care. Despite some diagnostic success, however, AI systems rely on forms of reductive reasoning and computational determinism that embed problematic assumptions about clinical decision-making and clinical practice. Clinician autonomy, experience, and judgement are reduced to inputs and outputs framed as binary or multi-class classification problems benchmarked against a clinician’s capacity to identify or predict disease states. This paper examines this reductive reasoning in AI systems for colorectal cancer (CRC) to highlight their limitations and risks: (1) in AI systems themselves due to inherent biases in (a) retrospective training datasets and (b) embedded assumptions in underlying AI architectures and algorithms; (2) in the problematic and limited evaluations being conducted on AI systems prior to system integration in clinical practice; and (3) in marginalising socio-technical factors in the context-dependent interactions between clinicians, their patients, and the broader health system. The paper argues that to optimise benefits from AI systems and to avoid negative unintended consequences for clinical decision-making and patient care, there is a need for more nuanced and balanced approaches to AI system deployment and evaluation in CRC.