A Review of Breast Imaging for Timely Diagnosis of Disease

A Narrative Review of the Use of Artificial Intelligence in Breast, Lung, and Prostate Cancer

Artificial intelligence (AI) has been an important topic within radiology. Currently, AI is used clinically to assist with the detection of lesions through detection systems. However, a number of recent studies have demonstrated the increased value of neural networks in radiology. With an increasing number of screening requirements for cancers, this review aims to study the accuracy of the numerous AI models used in the detection and diagnosis of breast, lung, and prostate cancers. This study summarizes pertinent findings from reviewed articles and provides analysis on the relevancy to clinical radiology. This study found that whereas AI is showing continual improvement in radiology, AI alone does not surpass the effectiveness of a radiologist. Additionally, it was found that there are multiple variations on how AI should be integrated with a radiologist's workflow.

Exosomal miRNAs and breast cancer: a complex theranostics interlink with clinical significance

Breast cancer (BC) remains the most challenging global health crisis of the current decade, impacting a large population of females annually. In the field of cancer research, the discovery of extracellular vesicles (EVs), specifically exosomes (a subpopulation of EVs), has marked a significant milestone. In general, exosomes are released from all active cells but tumour cell-derived exosomes (TDXs) have a great impact (TDXs miRNAs, proteins, lipid molecules) on cancer development and progression. TDXs regulate multiple events in breast cancer such as tumour microenvironment remodelling, immune cell suppression, angiogenesis, metastasis (EMT-epithelial mesenchymal transition, organ-specific metastasis), and therapeutic resistance. In BC, early detection is the most challenging event, exosome-based BC screening solved the problem. Exosome-based BC treatment is a sign of the transforming era of liquid biopsy, it is also a promising therapeutic tool for breast cancer. Exosome research goes to closer precision oncology via a single exosome profiling approach. Our hope is that this review will serve as motivation for researchers to explore the field of exosomes and develop an efficient, and affordable theranostics approach for breast cancer.

3D Harmonic and Subharmonic Imaging for Characterizing Breast Lesions: A Multi-Center Clinical Trial

OBJECTIVE

Breast cancer is the most frequent type of cancer among women. This multi-center study assessed the ability of 3D contrast-enhanced ultrasound to characterize suspicious breast lesions using clinical assessments and quantitative parameters.

METHODS

Women with suspicious breast lesions scheduled for biopsy were enrolled in this prospective, study. Following 2D grayscale ultrasound and power Doppler imaging (PDI), a contrast agent (Definity; Lantheus) was administrated. Contrast-enhanced 3D harmonic imaging (HI; transmitting/receiving at 5.0/10.0 MHz), as well as 3D subharmonic imaging (SHI; transmitting/receiving at 5.8/2.9 MHz), were performed using a modified Logiq 9 scanner (GE Healthcare). Five radiologists independently scored the imaging modes (including standard-of-care imaging) using a 7-point BIRADS scale as well as lesion vascularity and diagnostic confidence. Parametric volumes were constructed from time-intensity curves for vascular heterogeneity, perfusion, and area under the curve. Diagnostic accuracy was determined relative to pathology using receiver operating characteristic (ROC) and reverse, step-wise logistical regression analyses. The κ-statistic was calculated for inter-reader agreement.

RESULTS

Data were successfully acquired in 219 cases and biopsies indicated 164 (75%) benign and 55 (25%) malignant lesions. SHI depicted more anastomoses and vascularity than HI (P < .021), but there were no differences by pathology (P > .27). Ultrasound achieved accuracies of 82 to 85%, which was significantly better than standard-of-care imaging (72%; P < .03). SHI increased diagnostic confidence by 3 to 6% (P < .05), but inter-reader agreements were medium to low (κ < 0.52). The best regression model achieved 97% accuracy by combining clinical reads and parametric SHI.

CONCLUSIONS

Combining quantitative 3D SHI parameters and clinical assessments improves the characterization of suspicious breast lesions.

Special Issue "New Advances in Breast Imaging"

Breast cancer (BC) is the most common cancer in women of all ages, with more than 2 million diagnoses every year and a high economic and psychological impact on both the health care system and the population [...].

Comparative study of breast core needle biopsy (CNB) findings with ultrasound BI-RADS subtyping

&lt;b&gt; Introduction:&lt;/b&gt; Given the high prevalence of breast cancer, developing quick and accessible diagnostics solutions is critical. The BIRADS classification is a reliable method for assessing and estimating the risk of malignancy in breast lesions. &lt;/br&gt;&lt;/br&gt; &lt;b&gt;Aim:&lt;/b&gt; The aim of this study was to compare the results of core needle biopsy of breast lesions and sonographic findings based on the BIRADS category in Yazd. &lt;/br&gt;&lt;/br&gt; &lt;b&gt;Materials and methods:&lt;/b&gt; This retrospective analytical study was done on all core needle biopsy specimens referred to Mortaz hospital, Yazd, Iran from 2010 to 2019. Demographic data such as age, laterality of the lesion, BIRADS category, and pathology reports were extracted from patients&apos; hospital folders. Data were analyzed by SPSS version 21. P &lt; 0.05 was considered statistically significant. &lt;/br&gt;&lt;/br&gt; &lt;b&gt;Results:&lt;/b&gt; In total, 514 cases with a mean age of 43.9 9.4 years were studied. Among them, 104 cases (20.2%) were malignant and 410 cases (79.8%) were benign. The most common benign and malignant lesions were fibroadenoma (24.9%), and infiltrative ductal carcinoma (83.7%) respectively. The most common BIRADS was class 4A (54.9%). Patients with benign lesions were mostly in the 3rd and 4th decade of life, while malignant lesions were more in the 4th and 5th decades, and this difference was statistically significant (P = 0.001). The correlation between ultrasound diagnoses (BIRADS) and pathology findings was statistically significant (P &lt; 0.001). &lt;/br&gt;&lt;/br&gt; &lt;b&gt;Conclusion&lt;/b&gt;: Based on the results, there is a significant correlation between ultrasound outcomes according to BIRADS and pathology results, and the radiology-pathology accordance, owing to its high accuracy, can be very helpful in correctly diagnosing, monitoring, and managing the lesion.

Improved Machine Learning-Based Predictive Models for Breast Cancer Diagnosis

Breast cancer death rates are higher than any other cancer in American women. Machine learning-based predictive models promise earlier detection techniques for breast cancer diagnosis. However, making an evaluation for models that efficiently diagnose cancer is still challenging. In this work, we proposed data exploratory techniques (DET) and developed four different predictive models to improve breast cancer diagnostic accuracy. Prior to models, four-layered essential DET, e.g., feature distribution, correlation, elimination, and hyperparameter optimization, were deep-dived to identify the robust feature classification into malignant and benign classes. These proposed techniques and classifiers were implemented on the Wisconsin Diagnostic Breast Cancer (WDBC) and Breast Cancer Coimbra Dataset (BCCD) datasets. Standard performance metrics, including confusion matrices and K-fold cross-validation techniques, were applied to assess each classifier's efficiency and training time. The models' diagnostic capability improved with our DET, i.e., polynomial SVM gained 99.3%, LR with 98.06%, KNN acquired 97.35%, and EC achieved 97.61% accuracy with the WDBC dataset. We also compared our significant results with previous studies in terms of accuracy. The implementation procedure and findings can guide physicians to adopt an effective model for a practical understanding and prognosis of breast cancer tumors.

Advances of Exosomal miRNAs in Breast Cancer Progression and Diagnosis

Breast cancer is one of the most commonly diagnosed malignancies and the leading cause of cancer death in women worldwide. Although many factors associated with breast cancer have been identified, the definite etiology of breast cancer is still unclear. In addition, early diagnosis of breast cancer remains challenging. Exosomes are membrane-bound nanovesicles secreted by most types of cells and contain a series of biologically important molecules, such as lipids, proteins, and miRNAs, etc. Emerging evidence shows that exosomes can affect the status of cells by transmitting substances and messages among cells and are involved in various physiological and pathological processes. In breast cancer, exosomes play a significant role in breast tumorigenesis and progression through transfer miRNAs which can be potential biomarkers for early diagnosis of breast cancer. This review discusses the potential utility of exosomal miRNAs in breast cancer progression such as tumorigenesis, metastasis, immune regulation and drug resistance, and further in breast cancer diagnosis.