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Background. A personalized approach is the basis for the specialized care for cancer patients. The relevance of cervical cancer (CC) is still high. The searches for diagnostic criteria of cervical epithelium malignancy are continuing. The application ohm technologies has led to a big number results, the analysis of which is often difficult. The neural network data analysis allows to solve these problems.Objective: to create a technology for diagnosing cervical intraepithelial neoplasia (CIN) and CC, based on a neural network analysis of some molecular parameters.Materials and methods. The research carried out among patients with CIN III (n = 15), patients with CC stages I–IV (n = 49). The control group consisted of female volunteers without cervical pathology (n = 15). Studied molecular parameters: the spectrum of fatty acids was determined in cervical biopsies, proteins OPN, ICAM-1 were studied in blood serum, proteins of the immune cycle sCD25, sCD27 – in the cervical epithelium. Research methods: gas-liquid chromatography, flow cytometry.Results. Significant differences of fatty acids spectrum, local level sCD27 were revealed in among the studied groups. The multilayer perceptron included C18:2ω6, OPN, ICAM-1, sCD25, sCD27. The performed neural network analysis of the molecular data allows to diagnose CIN III (Se = 0.92; Sp = 0.87; AUC = 0.94; p˂0.001) and CC (Se = 1.00; Sp = 1.00; AUC = 1.00; p˂0.001).Conclusion. The created model makes it possible to diagnose CIN III and CC with high accuracy. The configuration of the multilayer perceptron allows confirming the pathophysiological relationships between the studied molecular parameters, to expand the understanding of the mechanisms of cervical carcinogenesis.
Background. A personalized approach is the basis for the specialized care for cancer patients. The relevance of cervical cancer (CC) is still high. The searches for diagnostic criteria of cervical epithelium malignancy are continuing. The application ohm technologies has led to a big number results, the analysis of which is often difficult. The neural network data analysis allows to solve these problems.Objective: to create a technology for diagnosing cervical intraepithelial neoplasia (CIN) and CC, based on a neural network analysis of some molecular parameters.Materials and methods. The research carried out among patients with CIN III (n = 15), patients with CC stages I–IV (n = 49). The control group consisted of female volunteers without cervical pathology (n = 15). Studied molecular parameters: the spectrum of fatty acids was determined in cervical biopsies, proteins OPN, ICAM-1 were studied in blood serum, proteins of the immune cycle sCD25, sCD27 – in the cervical epithelium. Research methods: gas-liquid chromatography, flow cytometry.Results. Significant differences of fatty acids spectrum, local level sCD27 were revealed in among the studied groups. The multilayer perceptron included C18:2ω6, OPN, ICAM-1, sCD25, sCD27. The performed neural network analysis of the molecular data allows to diagnose CIN III (Se = 0.92; Sp = 0.87; AUC = 0.94; p˂0.001) and CC (Se = 1.00; Sp = 1.00; AUC = 1.00; p˂0.001).Conclusion. The created model makes it possible to diagnose CIN III and CC with high accuracy. The configuration of the multilayer perceptron allows confirming the pathophysiological relationships between the studied molecular parameters, to expand the understanding of the mechanisms of cervical carcinogenesis.
BACKGROUND: Since 2020, staging accuracy improvements in cervical cancer (CC) revealed that major errors occur due to missed metastases to regional lymph nodes. The presence or absence of metastases seriously affects the staging of cancer and, consequently, its treatment methods, since metastases make cancer inoperable. AIM: To develop an application to help the doctor in predicting metastases to regional lymph nodes in CC, with the ability of installing on any personal computer regardless of the operating system (Windows, Linux, or Mac OS) and saving data about the patients. METHODS: The development was performed on the .NET platform (framework v4.7.2), by means of C# language, based on a proprietary prognostic model using seven blood parameters such as erythrocyte sedimentation rate, erythrocytes, hemoglobin, fibrinogen, D-dimer, and platelet aggregation with adenosine diphosphate and soluble fibrin monomer complexes. RESULTS: A window application was developed that automatically calculates the probability of metastases to regional lymph nodes in СС and provides the ability to save the data from the input form and the predicted value into TXT and/or CSV files. TXT and CSV formats are supported in various operating systems. The .txt file allows each individual patients prognosis results to be saved in a separate file for easy printing and attachment to the patients electronic or paper medical record. The CSV file format enables the aggregation of data of all patients by adding line-by-line data from the input form to the end of the file. CONCLUSIONS: The developed application facilitates the process of using the patented formula to diagnose metastases to regional lymph nodes in СС at the early stages of examination, which allows choosing the optimal tactics and, consequently, improving the prognosis of the disease.
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