Automatic Detection and Segmentation of Colorectal Cancer with Deep Residual Convolutional Neural Network.

Early and automatic detection of colorectal tumors is essential for cancer analysis, and the same is implemented using computer-aided diagnosis (CAD). A computerized tomography (CT) image of the colon is being used to identify colorectal carcinoma. Digital imaging and communication in medicine (DICOM) is a standard medical imaging format to process and analyze images digitally. Accurate detection of tumor cells in the complex digestive tract is necessary for optimal treatment. The proposed work is divided into two phases. The first phase involves the segmentation, and the second phase is the extraction of the colon lesions with the observed segmentation parameters. A deep convolutional neural network (DCNN) based residual network approach for the colon and polyps' segmentation from the CT images is applied over the 2D CT images. The residual stack block is being added to the hidden layers with short skip nuance, which helps to retain spatial information. ResNet-enabled CNN is employed in the current work to achieve complete boundary segmentation of the colon cancer region. The results obtained through segmentation serve as features for further extraction and classification of benign as well as malignant colon cancer. Performance evaluation metrics indicate that the proposed network model has effectively segmented and classified colorectal tumors with dice scores of 91.57% (on average), sensitivity = 98.28, specificity = 98.68, and accuracy = 98.82.

Therapeutically Significant MicroRNAs in Primary and Metastatic Brain Malignancies.

Brain cancer is one among the rare cancers with high mortality rate that affects both children and adults. The most aggressive form of primary brain tumor is glioblastoma. Secondary brain tumors most commonly metastasize from primary cancers of lung, breast, or melanoma. The five-year survival of primary and secondary brain tumors is 34% and 2.4%, respectively. Owing to poor prognosis, tumor heterogeneity, increased tumor relapse, and resistance to therapies, brain cancers have high mortality and poor survival rates compared to other cancers. Early diagnosis, effective targeted treatments, and improved prognosis have the potential to increase the survival rate of patients with primary and secondary brain malignancies. MicroRNAs (miRNAs) are short noncoding RNAs of approximately 18-22 nucleotides that play a significant role in the regulation of multiple genes. With growing interest in the development of miRNA-based therapeutics, it is crucial to understand the differential role of these miRNAs in the given cancer scenario. This review focuses on the differential expression of ten miRNAs (miR-145, miR-31, miR-451, miR-19a, miR-143, miR-125b, miR-328, miR-210, miR-146a, and miR-126) in glioblastoma and brain metastasis. These miRNAs are highly dysregulated in both primary and metastatic brain tumors, which necessitates a better understanding of their role in these cancers. In the context of the tumor microenvironment and the expression of different genes, these miRNAs possess both oncogenic and/or tumor-suppressive roles within the same cancer.