Anticancer and Cytotoxicity Activity of Native and Modified Black Rice Flour on Colon Cancer Cell Lines.

This study is intended to evaluate the cytotoxicity of native and dual-modified black rice flour against the colon cancer cell line (HCT116) and mouse embryo cell line (3T3-L1) by using the MTT assay. The modification techniques applied to prepare rice flour samples were enzymatic modification and heat moisture treatment. In this study, the IC50 of native black rice flour and modified black rice flour was 255.78 µg/mL and 340.85 µg/mL, respectively. The result confirms that the native black rice flour has significant cytotoxic and anticancer potential against human colon cancer cells. In addition, the IC50 of native black rice flour and modified black rice flour on the 3T3-L1 cell line was found to be 345.96 µg/mL and 1106.94 µg/mL, respectively. The results showed that the native black rice flour had weak cytotoxicity, and modified black rice flour was nontoxic in both the cell lines. The active component of phytochemicals present in black rice flour has a potential role in preventing colon cancer.

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.