Machine Learning Techniques for Autonomous Lesion Detection in Microwave Breast Imaging Clinical Data

Microwave breast imaging is becoming a promising new approach in the early detection of breast cancer. In this study, we have used advanced machine learning techniques to identify breast lesions from clinical data obtained via MammoWave scanner. To improve the performance of our model, we have explored refinements such as adjusting the number of principal components (PCs) in principal component analysis (PCA) and transitioning to generative adversarial network (GAN)-generated raw data. Leveraging this data, we have used PCA feature extraction, and utilized it to train a support vector machine (SVM). Our analysis has yielded promising results, with a ternary classification approach achieving an accuracy of 0.80

CD161++ CD8+ T cells, including the MAIT cell subset, are specifically activated by IL-12+IL-18 in a TCR-independent manner.

CD161(++) CD8(+) T cells represent a novel subset that is dominated in adult peripheral blood by mucosal-associated invariant T (MAIT) cells, as defined by the expression of a variable-α chain 7.2 (Vα7.2)-Jα33 TCR, and IL-18Rα. Stimulation with IL-18+IL-12 is known to induce IFN-γ by both NK cells and, to a more limited extent, T cells. Here, we show the CD161(++) CD8(+) T-cell population is the primary T-cell population triggered by this mechanism. Both CD161(++) Vα7.2(+) and CD161(++) Vα7.2(-) T-cell subsets responded to IL-12+IL-18 stimulation, demonstrating this response was not restricted to the MAIT cells, but to the CD161(++) phenotype. Bacteria and TLR agonists also indirectly triggered IFN-γ expression via IL-12 and IL-18. These data show that CD161(++) T cells are the predominant T-cell population that responds directly to IL-12+IL-18 stimulation. Furthermore, our findings broaden the potential role of MAIT cells beyond bacterial responsiveness to potentially include viral infections and other inflammatory stimuli