Residual U-Network for Breast Tumor Segmentation from Magnetic Resonance Images

Breast cancer positions as the most well-known threat and the main source of malignant growth-related morbidity and mortality throughout the world.It is apical of all new cancer incidences analyzed among females.Two features substantially inuence the classication accuracy of malignancy and benignity in automated cancer diagnostics.These are the precision of tumor segmentation and appropriateness of extracted attributes required for the diagnosis.In this research,the authors have proposed a ResU-Net(Residual U-Network)model for breast tumor segmentation.The proposed methodology renders augmented,and precise identication of tumor regions and produces accurate breast tumor segmentation in contrast-enhanced MR images.Furthermore,the proposed framework also encompasses the residual network technique,which subsequently enhances the performance and displays the improved training process.Over and above,the performance of ResU-Net has experimentally been analyzed with conventional U-Net,FCN8,FCN32.Algorithm performance is evaluated in the form of dice coefcient and MIoU(Mean Intersection of Union),accuracy,loss,sensitivity,specicity,F1score.Experimental results show that ResU-Net achieved validation accuracy&dice coefcient value of 73.22%&85.32%respectively on the Rider Breast MRI dataset and outperformed as compared to the other algorithms used in experimentation.

A hierarchical clustering approach for colorectal cancer molecular subtypes identification from gene expression data

Background Colorectal cancer(CRC)is the second leading cause of cancer fatalities and the third most common human disease.Identifying molecular subgroups of CRC and treating patients accordingly could result in better therapeutic success compared with treating all CRC patients similarly.Studies have highlighted the significance of CRC as a major cause of mortality worldwide and the potential benefits of identifying molecular subtypes to tailor treatment strategies and improve patient outcomes.Methods This study proposed an unsupervised learning approach using hierarchical clustering and feature selection to identify molecular subtypes and compares its performance with that of conventional methods.The proposed model contained gene expression data from CRC patients obtained from Kaggle and used dimension reduction techniques followed by Z-score-based outlier removal.Agglomerative hierarchy clustering was used to identify molecular subtypes,with a P-value-based approach for feature selection.The performance of the model was evaluated using various classifiers including multilayer perceptron(MLP).Results The proposed methodology outperformed conventional methods,with the MLP classifier achieving the highest accuracy of 89%after feature selection.The model successfully identified molecular subtypes of CRC and differentiated between different subtypes based on their gene expression profiles.Conclusion This method could aid in developing tailored therapeutic strategies for CRC patients,although there is a need for further validation and evaluation of its clinical significance.

Benign lymph node microenvironment is associated with response to immunotherapy

Introduction:Benign lymph nodes have been considered the hubs of immune surveillance in cancer patients.The microenvironment of these lymphoid tissues can be immune suppressed,hence allowing for tumor progression.Understanding the spectrum of benign findings in bystander lymph nodes in immune checkpoint blockade therapy could prove to be key to understanding the mechanism and assessing treatment response.Methods:Benign lymph nodes and spleen were evaluated from patients treated with immunotherapy who subsequently received postmortem examination.We used quantitative immunofluorescence(QIF)to assess tumor infiltrating lymphocytes(TIL)and macrophage marker expression and characterized activation status using a novelmultiplexed QIF assay including CD3,GranzymeB,and Ki67.We performedimmunohistochemistry to correlate results of QIF.Results:Benign lymph nodes from non-responders to immunotherapy showed significantly higher expression of cytotoxic markers and proliferation index(Ki67)in T cells compared to responders.Higher expression of PD-L1 in macrophages was also observed.There was no significant difference in CD3+expression,but higher levels of CD8+T cells as well as CD20+B cells were seen in lymph nodes of non-responders.No significant differences were seen between responder and non-responder splenic tissue.Findings were supported by traditional immunostaining methods.Conclusions:While most studies in biomarkers for immunotherapy focus on tumor microenvironment,we show that benign lymph node microenvironment may predict response to immunotherapy.In responding patients,bystander lymph nodes appear to have been mobilized,resulting in reduced cytotoxic T cells.Conversely,patients whose disease progressed on immunotherapy demonstrate higher levels of macrophages that express increased PD-L1,and activated T cells not recruited to the tumor site.