Detection of Brain Tumor in MRI Images Using Optimized ANFIS Classifier

Tumor is basically a most common disease of brain and the Brain Tumor (BT) treatment has crucial significance. A diagnostic procedure called MRI image that is employed for detecting BT. It is the utmost important and intricate tasks in numerous medical-image applications since it typically involves a huge quantity of data. A lot of methods were applied in BT detection ranging as of image processing to examine the BT; however, the prevailing BT technique is tedious and less effective. So, this paper proposed the detection of the BT in MRI images utilizing optimized ANFIS classifier. Originally, the input MR image is preprocessed utilizing Gaussian Filter (GF) that removes the noise from the inputted image, additionally, the non-brain tissues (NBT) are removed using the technique of skull stripping (SS). After that, segmentation is performed wherein the tumor part is segmented utilizing CBAC technique and edema part is segmented utilizing HLSS segmentation technique. Then, GLCM in addition to GLRLM features are extracted afterward that extorted features is chosen by BFO algorithm. Finally, the selected features inputted to the optimized ANFIS classifier that classifies the tumor class types as Meningioma, Glioma, along with Pituitary. In ANFIS, the optimization procedure is achieved utilizing the PSO. The proposed system’s performance is contrasted to the prevailing systems regarding precision, recall, specificity, sensitivity, accuracy, together with F-Measure.

Identification of mutational signatures active in individual tumors

The mutational processes responsible for the somatic mutations observed in tumor samples can significantly vary not only between tumor types but also among the individual cancers within a tumor class. Mutational processes can be represented by so called “mutational signatures” which reflect the occurrences of base changes within their sequence contexts (i.e., in dependence on their flanking bases). We present a user-friendly R package, called decompTumor2Sig, that can be used to evaluate the contribution of Shiraishi signatures to the somatic mutations found in an individual tumor.

Identification of mutational signatures active in individual tumors

The mutational processes responsible for the somatic mutations observed in tumor samples can significantly vary not only between tumor types but also among the individual cancers within a tumor class. Mutational processes can be represented by so called “mutational signatures” which reflect the occurrences of base changes within their sequence contexts (i.e., in dependence on their flanking bases). We present a user-friendly R package, called decompTumor2Sig, that can be used to evaluate the contribution of Shiraishi signatures to the somatic mutations found in an individual tumor.

A Fusion-Based Approach for Breast Ultrasound Image Classification Using Multiple-ROI Texture and Morphological Analyses

Ultrasound imaging is commonly used for breast cancer diagnosis, but accurate interpretation of breast ultrasound (BUS) images is often challenging and operator-dependent. Computer-aided diagnosis (CAD) systems can be employed to provide the radiologists with a second opinion to improve the diagnosis accuracy. In this study, a new CAD system is developed to enable accurate BUS image classification. In particular, an improved texture analysis is introduced, in which the tumor is divided into a set of nonoverlapping regions of interest (ROIs). Each ROI is analyzed using gray-level cooccurrence matrix features and a support vector machine classifier to estimate its tumor class indicator. The tumor class indicators of all ROIs are combined using a voting mechanism to estimate the tumor class. In addition, morphological analysis is employed to classify the tumor. A probabilistic approach is used to fuse the classification results of the multiple-ROI texture analysis and morphological analysis. The proposed approach is applied to classify 110 BUS images that include 64 benign and 46 malignant tumors. The accuracy, specificity, and sensitivity obtained using the proposed approach are 98.2%, 98.4%, and 97.8%, respectively. These results demonstrate that the proposed approach can effectively be used to differentiate benign and malignant tumors.

RET protein expression has no prognostic impact on the long-term outcome of papillary thyroid carcinoma

BACKGROUND: RET proto-oncogene rearrangements (RET/PTC) are causative events in the pathogenesis of a subset of papillary thyroid cancer (PTC). The prevalence of RET/PTC varies in different countries and according to specific clinical features: it is higher after radiation exposure and it is claimed to be higher in young patients. Conflicting results are reported regarding the prognostic role of RET/PTC activation. OBJECTIVE: To investigate the prognostic meaning of RET/PTC rearrangement on the long term outcome of PTC. METHODS: We have studied the expression of the RET encoded protein in 127 papillary thyroid carcinomas by immunohistochemistry using a polyclonal antibody against the tyrosine-kinase domain of the RET protein. These cases have been collected during 1970-1985, and have a mean (+/-S.D.) period of follow-up of 18.6+/-3.7 years (range 12-27 years). The results have been compared with the patients' outcome. RESULTS: The tyrosine-kinase domain of RET was expressed in 82 (64.6%) papillary carcinomas. Among them, RET was highly expressed in 65 (51.2%) cases and moderately expressed in 17 (13.4%). RET expression was absent in 45 (35.4%) cases. No correlation was found between RET expression and other parameters such as sex, age at diagnosis, tumor class and histological variant. Follow-up analysis showed no influence of RET expression on patients' outcome. By multivariate analysis, age (>45 years) and tumor class IV, but not sex and RET expression were adverse prognostic indicators of death. CONCLUSION: In conclusion, our analysis indicates that RET expression is frequently found in PTC, and has no influence on tumor outcome.

The tumor-rejection antigens of the 1591 ultraviolet fibrosarcoma. Potential origin and evolutionary implications.

Previously, we cloned and sequenced the three novel MHC class I genes expressed by the C3H UV fibrosarcoma, 1591. We have extended the analysis of the polymorphic nature of these genes relative to the C3H strain. Scattered nucleotide differences among the tumor genes as compared with the C3H H-2 and Qa sequences make it highly unlikely that the novel tumor genes were generated by recombination between endogenous C3H sequences. Given that two of the tumor clones, A149 and A166, are remarkably similar in amino acid and DNA sequence to H-2Lq and H-2Dq, respectively, we also examined the 1591 RP2 and GUS loci for evidence of polymorphism. Compared with C3H and B10.AKM, 1591 appears to be heterozygous at each of these loci, consistent with an H-2q origin for the two novel 1591 class I genes. Interestingly, the third tumor gene, designated A216, shares certain characteristics with the H-2Ks antigen, reminiscent of the naturally occurring combination of H-2Ks, H-2Dq, and H-2Lq antigens found in some Swiss mouse strains. As a result, we propose that the non-C3H/HeN characteristics displayed by the 1591 tumor point to a non-C3H origin for the novel tumor class I genes of 1591.