To investigate the involvement of the CYP17, SRD5A2, CYP1B1, and CYP2D6 variants with prostate cancer, a case-control study of 100 patients and an equal number of age-matched control men was conducted. There appears to be a nonsignificant increase with risk of prostate cancer for individuals carrying one copy of the CYP17 A2 allele (OR, 1.80; 95% CI, 0.99-3.29, P=0.05). The risk was increased in individuals having two A2 alleles (OR; 2.81, 95% CI, 1.06-7.40, P=0.03). Compared with men having the VV genotype of SRD5A2 gene, there was no significant association between the VL genotype and the risk of prostate cancer (OR; 0.54, 95% CI; 0.29-1.03, P=0.06). There was no difference in the occurrence of the genotype LL between controls and prostate cancer patients (OR; 0.90, 95% CI; 0.43-1.89, P=0.79). There was a nonsignificant increased risk of prostate cancer for individuals carrying the CYP1B1Leu/Val genotype (OR, 1.70, 95% CI, 0.91-3.17, P =0.09), which was increased in those having the Val/Val allele (OR, 3.38; 95% CI, 1.13-10.07, P=0.02). Relative to men homozygous for the wild-type allele in CYP2D6 gene, those heterozygous for the B allele had an odds ratio of 1.78 (95% CI, 0.76-4.17, P=0.18) for patients, and for homozygous individuals, it was 1.95 (0.55-6.93, P=0.30). These observations have suggested that the CYP17 A2/A2, CYP1B1 Val/Val, and CYP2D6 genotypes may be associated with an altered risk of prostate cancer, while the CYP2D6 and SRD5A2 V89L polymorphism have no association with its risk in the North Indian population.

MSR1 repeats act as molecular switches that modulate gene expression. It is likely that CNV of MSR1 will affect risk of development of various forms of cancer, including that of breast and prostate. The MSR1 cluster at KLK14 represents the strongest risk factor identified to date in non-familial breast cancer and a significant risk factor for prostate cancer. Analysis of MSR1 genotype will allow development of precise stratification of disease risk and provide a novel target for therapeutic agents.

Computed tomography (CT) images are commonly used to diagnose liver disease. It is sometimes very difficult to comment on the type, category and level of the tumor, even for experienced radiologists, directly from the CT image, due to the varying intensities. In recent years, it has been important to design and develop computer-assisted imaging techniques to help doctors/physicians improve their diagnosis. The proposed work is to detect the presence of a tumor region in the liver and classify the different stages of the tumor from CT images. CT images of the liver have been classified between normal and tumor classes. In addition, CT images of the tumor have been classified between Hepato Cellular Carcinoma (HCC) and Metastases (MET). The performance of six different classifiers was evaluated on different parameters. The accuracy achieved for different classifiers varies between 98.39% and 100% for tumor identification and between 76.38% and 87.01% for tumor classification. To further, improve performance, a multi-level ensemble model is developed to detect a tumor (liver cancer) and to classify between HCC and MET using features extracted from CT images. The k-fold cross-validation (CV) is also used to justify the robustness of the classifiers. Compared to the individual classifier, the multi-level ensemble model achieved high accuracy in both the detection and classification of different tumors. This study demonstrates automated tumor characterization based on liver CT images and will assist the radiologist in detecting and classifying different types of tumors at a very early stage.

Our proposed research technique intends to provide an effective liver magnetic resonance imaging (MRI) and computed tomography (CT) scan image classification which would play a significant role in medical dataset especially in feature selection and classification. There are a number of existing research works classifying the liver tumor disease. Early detection of liver tumor will help the patients to get cured rapidly. Our proposed research focuses on the classification of medical images with respect to the classification technique artificial neural network (ANN) to classify an image as normal or abnormal. In the pre-processing step, the input image is selected from the database and adaptive median filtering is used for noise removal. For better enhancement, histogram equalization (HE) is done in the noise-removed images. In the pre-processed images, the texture feature such as gray-level co-occurrence matrix (GLCM) and statistical features are extracted. From the extensive feature set, optimal features are selected using the optimal kernel K-means (OKK-means) clustering algorithm along with the oppositional firefly algorithm (OFA). The proposed method obtained 97.5% accuracy in the classification when compared to the existing method.

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