Mammographic mass segmentation using fuzzy contours

Role of microRNAs in triple‑negative breast cancer and new therapeutic concepts (Review)

Breast cancer has surpassed lung cancer as the most prevalent malignancy affecting women worldwide. Triple-negative breast cancer (TNBC) is the type of breast cancer with the worst prognosis. As a heterogeneous disease, TNBC has a pathogenesis that involves multiple oncogenic pathways, including involvement of gene mutations and alterations in signaling pathways. MicroRNAs (miRNAs) are small endogenous, single-stranded non-coding RNAs that bind to the 3' untranslated region of target cell mRNAs to negatively regulate the gene expression of these specific mRNAs. Therefore, miRNAs are involved in cell growth, development, division and differentiation stages. miRNAs are also involved in gene targeting in tumorigenesis, tumor growth and the regulation of metastasis, including in breast cancer. Meanwhile, miRNAs also regulate components of signaling pathways. In this review, the role of miRNAs in the TNBC signaling pathway discovered in recent years is described in detail. The new concept of bi-targeted therapy for breast cancer using miRNA and artificial intelligence is also discussed.

A Review of Artificial Intelligence in Breast Imaging

With the increasing dominance of artificial intelligence (AI) techniques, the important prospects for their application have extended to various medical fields, including domains such as in vitro diagnosis, intelligent rehabilitation, medical imaging, and prognosis. Breast cancer is a common malignancy that critically affects women's physical and mental health. Early breast cancer screening-through mammography, ultrasound, or magnetic resonance imaging (MRI)-can substantially improve the prognosis for breast cancer patients. AI applications have shown excellent performance in various image recognition tasks, and their use in breast cancer screening has been explored in numerous studies. This paper introduces relevant AI techniques and their applications in the field of medical imaging of the breast (mammography and ultrasound), specifically in terms of identifying, segmenting, and classifying lesions; assessing breast cancer risk; and improving image quality. Focusing on medical imaging for breast cancer, this paper also reviews related challenges and prospects for AI.

Artificial Intelligence-Powered Mammography: Navigating the Landscape of Deep Learning for Breast Cancer Detection

Worldwide, breast cancer (BC) is one of the most commonly diagnosed malignancies in women. Early detection is key to improving survival rates and health outcomes. This literature review focuses on how artificial intelligence (AI), especially deep learning (DL), can enhance the ability of mammography, a key tool in BC detection, to yield more accurate results. Artificial intelligence has shown promise in reducing diagnostic errors and increasing early cancer detection chances. Nevertheless, significant challenges exist, including the requirement for large amounts of high-quality data and concerns over data privacy. Despite these hurdles, AI and DL are advancing the field of radiology, offering better ways to diagnose, detect, and treat diseases. The U.S. Food and Drug Administration (FDA) has approved several AI diagnostic tools. Yet, the full potential of these technologies, especially for more advanced screening methods like digital breast tomosynthesis (DBT), depends on further clinical studies and the development of larger databases. In summary, this review highlights the exciting potential of AI in BC screening. It calls for more research and validation to fully employ the power of AI in clinical practice, ensuring that these technologies can help save lives by improving diagnosis accuracy and efficiency.

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.

A Multi-Scale Information Fusion Level Set for Breast Tumor Segmentation

Purpose: Mammography is considered an effective method of examination in early breast cancer screening. Massive work by distinguished researchers of breast segmentation has been proposed. However, due to the blurry boundaries of the breast tumor, the variability of its shape and the overlap with surrounding tissue, the breast tumor’s accurate segmentation still is a challenge. Methods: In this paper, we proposed a novel level set model which based on the optimized local region driven gradient enhanced level set model (OLR-GCV) to segment tumor within a region of interest (ROI) in a mammogram. Firstly, Noise, labels and artifacts are removed from breast images. The ROI is then obtained using the intuitionistic fuzzy C-means method. Finally, we used OLR-GCV method to accurately segment the breast tumor. The OLR-GCV model combines regional information, enhanced edge information and optimized Laplacian of Gaussian (LOG) energy term. The regional and enhanced edge information are used to capture local, global and gradient information of breast images. The optimized Laplacian of Gaussian (LOG) energy term is introduced in the energy functional to further optimize edge information to improve segmentation accuracy. Results: We evaluated our method on the MIAS and DDSM datasets. It yielded a Dice value of 96.86% on the former and 95.51% on the latter. Our method proposed achieves higher accuracy of segmentation than other State-of-the-art Methods. Conclusions: Our method has better segmentation performance, and can be used in clinical practice.

Artificial Intelligence in Medical Imaging of the Breast

Artificial intelligence (AI) has invaded our daily lives, and in the last decade, there have been very promising applications of AI in the field of medicine, including medical imaging, in vitro diagnosis, intelligent rehabilitation, and prognosis. Breast cancer is one of the common malignant tumors in women and seriously threatens women’s physical and mental health. Early screening for breast cancer via mammography, ultrasound and magnetic resonance imaging (MRI) can significantly improve the prognosis of patients. AI has shown excellent performance in image recognition tasks and has been widely studied in breast cancer screening. This paper introduces the background of AI and its application in breast medical imaging (mammography, ultrasound and MRI), such as in the identification, segmentation and classification of lesions; breast density assessment; and breast cancer risk assessment. In addition, we also discuss the challenges and future perspectives of the application of AI in medical imaging of the breast.

A Review of Applications of Machine Learning in Mammography and Future Challenges

BACKGROUND

The aim of this study is to systematically review the literature to summarize the evidence surrounding the clinical utility of artificial intelligence (AI) in the field of mammography. Databases from PubMed, IEEE Xplore, and Scopus were searched for relevant literature. Studies evaluating AI models in the context of prediction and diagnosis of breast malignancies that also reported conventional performance metrics were deemed suitable for inclusion. From 90 unique citations, 21 studies were considered suitable for our examination. Data was not pooled due to heterogeneity in study evaluation methods.

SUMMARY

Three studies showed the applicability of AI in reducing workload. Six studies demonstrated that AI can aid in diagnosis, with up to 69% reduction in false positives and an increase in sensitivity ranging from 84 to 91%. Five studies show how AI models can independently mark and classify suspicious findings on conventional scans, with abilities comparable with radiologists. Seven studies examined AI predictive potential for breast cancer and risk score calculation. Key Messages: Despite limitations in the current evidence base and technical obstacles, this review suggests AI has marked potential for extensive use in mammography. Additional works, including large-scale prospective studies, are warranted to elucidate the clinical utility of AI.

Whole mammographic mass segmentation using attention mechanism and multiscale pooling adversarial network

Purpose: Since breast mass is a clear sign of breast cancer, its precise segmentation is of great significance for the diagnosis of breast cancer. However, the current diagnosis relies mainly on radiologists who spend time extracting features manually, which inevitably reduces the efficiency of diagnosis. Therefore, designing an automatic segmentation method is urgently necessary for the accurate segmentation of breast masses. Approach: We propose an effective attention mechanism and multiscale pooling conditional generative adversarial network (AM-MSP-cGAN), which accurately achieves mass automatic segmentation in whole mammograms. In AM-MSP-cGAN, U-Net is utilized as a generator network by incorporating attention mechanism (AM) into it, which allows U-Net to pay more attention to the target mass regions without additional cost. As a discriminator network, a convolutional neural network with multiscale pooling module is used to learn more meticulous features from the masses with rough and fuzzy boundaries. The proposed model is trained and tested on two public datasets: CBIS-DDSM and INbreast. Results: Compared with other state-of-the-art methods, the AM-MSP-cGAN can achieve better segmentation results in terms of the dice similarity coefficient (Dice) and Hausdorff distance metrics, achieving top scores of 84.49% and 5.01 on CBIS-DDSM, and 83.92% and 5.81 on INbreast, respectively. Therefore, qualitative and quantitative experiments illustrate that the proposed model is effective and robust for the mass segmentation in whole mammograms. Conclusions: The proposed deep learning model is suitable for the automatic segmentation of breast masses, which provides technical assistance for subsequent pathological structure analysis.