Application and Analysis of Biomedical Imaging Technology in Early Diagnosis of Breast Cancer

MDFF-Net: A multi-dimensional feature fusion network for breast histopathology image classification

Breast cancer is a common malignancy and early detection and treatment of it is crucial. Computer-aided diagnosis (CAD) based on deep learning has significantly advanced medical diagnostics, enhancing accuracy and efficiency in recent years. Despite the convenience, this technology also has certain limitations. When the morphological characteristics of the patient's pathological section are not evident or complex, certain small lesions or cells deep within the lesion cannot be recognized, and misdiagnosis is prone to occur. As a result, MDFF-Net, a CNN-based multidimensional feature fusion network, is proposed. The model consists of a one-dimensional feature extraction network, a two-dimensional feature extraction network, and a feature fusion classification network. The basic part of the two-dimensional feature extraction network is stacked by modules integrated with multi-scale channel shuffling networks and channel attention modules. Furthermore, inspired by natural language processing, this model integrates a one-dimensional feature extraction network to extract detailed information in the image to avoid misdiagnosis caused by insufficient information extraction such as cell morphological characteristics and differentiation degree. Finally, the extracted one-dimensional and two-dimensional features are fused in the feature fusion network and employed for the final classification. The effectiveness of MDFF-Net and classical classification models were evaluated on the BreakHis and the BACH datasets. According to experimental results, MDFF-Net achieves an accuracy of 98.86% on the BreakHis and 86.25% on the BACH dataset. Furthermore, to further assess the effectiveness of the model in other classification tasks, the colon cancer and the lung cancer datasets were employed for additional experiments, achieving a classification accuracy of 100% in both cases.

Prolonged Exposure to Simulated Microgravity Changes Release of Small Extracellular Vesicle in Breast Cancer Cells

Breast cancer is the leading cause of cancer incidence worldwide and among the five leading causes of cancer mortality. Despite major improvements in early detection and new treatment approaches, the need for better outcomes and quality of life for patients is still high. Extracellular vesicles play an important role in tumor biology, as they are able to transfer information between cells of different origins and locations. Their potential value as biomarkers or for targeted tumor therapy is apparent. In this study, we analyzed the supernatants of MCF-7 breast cancer cells, which were harvested following 5 or 10 days of simulated microgravity on a Random Positioning Machine (RPM). The primary results showed a substantial increase in released vesicles following incubation under simulated microgravity at both time points. The distribution of subpopulations regarding their surface protein expression is also altered; the minimal changes between the time points hint at an early adaption. This is the first step in gaining further insight into the mechanisms of tumor progression, metastasis, the education of the tumor microenvironments, and preparation of the metastatic niche. Additionally, this may lighten up the processes of the rapid cellular adaptions in the organisms of space travelers during spaceflights.

Radiogenomics, Breast Cancer Diagnosis and Characterization: Current Status and Future Directions

Breast cancer (BC) is a heterogeneous disease, affecting millions of women every year. Early diagnosis is crucial to increasing survival. The clinical workup of BC diagnosis involves diagnostic imaging and bioptic characterization. In recent years, technical advances in image processing allowed for the application of advanced image analysis (radiomics) to clinical data. Furthermore, -omics technologies showed their potential in the characterization of BC. Combining information provided by radiomics with -omics data can be important to personalize diagnostic and therapeutic work up in a clinical context for the benefit of the patient. In this review, we analyzed the recent literature, highlighting innovative approaches to combine imaging and biochemical/biological data, with the aim of identifying recent advances in radiogenomics applied to BC. The results of radiogenomic studies are encouraging approaches in a clinical setting. Despite this, as radiogenomics is an emerging area, the optimal approach has to face technical limitations and needs to be applied to large cohorts including all the expression profiles currently available for BC subtypes (e.g., besides markers from transcriptomics, proteomics and miRNomics, also other non-coding RNA profiles).