[Differential diagnostics of gastric cancer and precancerous changes of the gastric mucosa using analysis of expression of six microRNAS.]

The lack of specific symptoms for the early detection of gastric cancer leads to the fact that it is often diagnosed at a late stage, when the prognosis is unfavorable. The analysis of molecular markers in addition to standard diagnostic procedures is a promising approach for improving the preoperative diagnosis of both gastric cancer and precancerous changes in the mucosa. Therefore, the aim of our study was to analyze the diagnostic significance of using miRNA expression to diagnosis gastric cancer and precancerous conditions (dysplasia) in histological material. In this work, 122 samples of archival histological material in the form of paraffin blocks were used: 34 samples of gastric adenocarcinoma, 54 samples of gastric ulcers with dysplasia and 34 samples of normal gastric mucosa obtained from patients after bariatric surgery. The expression level of miRNA-145-5p, -150-5p, -20a-5p, -21-5p, -31-5p, -34a-5p, -375 was determined using real-time RT-PCR. Samples were stratified into different groups using the C-RT decision tree algorithm. All miRNAs, except miRNA-20a, were included in the decision tree, which allows stratification of samples for normal mucosa, dysplasia, and gastric cancer. Normal mucosa can be distinguished from gastric cancer only by miRNA-34a, -21, -375. Diagnostic characteristics for the detection of dysplasia: specificity - 97%, sensitivity - 87%; for the detection of gastric cancer: specificity - 91%, sensitivity - 93%. The sufficiently high values of the diagnostic characteristics for detecting dysplasia of the gastric mucosa and gastric cancer obtained in our study indicate the possibility of using expression data of a small amount of miRNAs for the effective separation of samples with tumor and precancerous changes in the stomach tissue.

Potentialities of differential immunohistochemical diagnosis of some follicular tumors of the thyroid gland.

Differential diagnosis of thyroid follicular adenomas, follicular cancer, and papillary cancer with follicular structure was carried out. Proliferation coefficients, probability of atypia, and tumor growth activity were evaluated on the basis of quantitative proportions of proliferation and apoptosis genes expression, derived from indexes of labeled nuclei expressing Ki-67, bcl2, p53 and cytoplasm with expression of bcl2, p53 in cells of these tumors. Significant differences between all the studied tumors of the thyroid grand with follicular structure were detected. The proposed criteria supplement the postoperative morphological differential diagnosis of thyroid tumors.

[Computer morphometry in the differential diagnosis of thyroid neoplasms].

The quantitative studies of follicular thyroid tumors were applied for their morphological differentiation. The computer-aided analysis of the images permits objective and prompt estimation of the quantitative parameters of the structural components of a tumor in the histological sections. The comparison of the values obtained in the tissue samples could identify the most typical differentially diagnostic criteria that could help to define the type of a tumor, by replacing a routine morphological study based on the analysis of qualitative morphological signs. Its effectiveness is largely determined by the skill of a physician and the subjectivity of assessments of observed phenomena.

[The results of quantitative cytological analysis of the structure of follicular thyroid tumors using computer and neural network technologies].

Artificial neural network is a complex system capable of gathering and synthesizing data and capable of learning using particular given parameters. This technology was applied to studying cytological diagnostics of thyroid diseases to differentiate between follicular cancer and follicular adenoma. A complex of quantitative cytological characteristics was used to describe cytological preparations by examples of known cytological diagnosis to train the neural network. Among offered cytological examples, 96% were recognized correctly during testing.

[Differential diagnostic capacities of computer-aided analysis of thyroid follicular tumor images].

To establish the objective quantitative cytological parameters of thyroid follicular tumor cells for their further use as criteria for differential cytological diagnosis, the authors made a mathematical analysis of their values obtained by computer-aided karyocytometry of thyroid follicular tumors. It is recommended that the arithmetic mean values of nuclear areas, perimeters, and optic densities should be used as criteria for differential diagnosis when there are qualitative cytological signs indicating that the tumor under study may belong to the follicular type of papillary carcinoma.