Relevance: Over the past decades, lung cancer (LC) incidence worldwide is adding about 1.5% each year. The risk of LC development increases 4-5 times with age. The mortality-to-incidence ratio (MIR) in LC is the most unfavorable – up to 95.6% of patients die. The purpose of this study was to analyze the epidemiological situation with lung cancer in the Republic of Kazakhstan over the past five years, with the assessment of key epidemiological indicators by gender. Results: In the previous five years (2014-2018), the men to women ratio among LC patients was equal to 4.2:1 and remained stable. A gradual decline in mortality (16‰ in 2014 to 12.9‰ in 2018) correlated with the MIR dynamic pattern (67.5% in 2014 to 60.0% in 2018). The standardized LC incidence in men was declining steadily (46.8‰ in 2014 to 43.0‰ in 2018). A more evident decrease in male mortality from LC (32.0‰ in 2014 to 24.2‰ in 2018) was due to the progress recently achieved in LC diagnostics and treatment in the Republic of Kazakhstan. On the contrary, the LC incidence in women increased by 2.6% a year (7.6‰ in 2014 to 7.8‰ in 2018). The female mortality from LC was declining steadily (4.6‰ in 2014 to 3.5‰ in 2018), with a negative growth rate of minus 31.4%. Conclusion: The analysis of epidemiologic indicators for LC in the Republic of Kazakhstan showed a general downward trend in incidence and mortality regardless of gender. However, we can expect an increase in the female incidence of LC in sync with the global trend. The study period has witnessed the improvement in both the primary diagnostics (due to the renewal of diagnostic equipment in the country) and treatment of LC (through the introduction in the Republic of methods of molecular genetic studies which are the basis for the state-financed personalized drug therapy with targeted agents).
Relevance: According to the International Agency for Research on Cancer (IARC), lung cancer (LC) currently ranks first in cancer incidence and mortality worldwide. The gold standard of LC diagnostics is the histological verification, the determination of the degree of invasion and tumor phenotype. At first glance, epigenetic methods seem to be secondary after determining the patient’s genetic profile. However, standard genetic analysis reveals only the DNA nucleotide sequence. Thus, epigenetic analysis is the only method that allows detecting potential abnormalities in cells. An important difference between genetic and epigenetic changes is that drugs are efficient against epigenetic changes but absolutely powerless against genetic mutations. The purpose of the study was to review and analyze the available molecular genetic methods for DNA methylation profiling in lung cancer. Results: All these observations support the hypothesis that methylation profiling in body fluids can help determine the people predisposed to or affected by LC. Circulating acellular DNA in the blood plasma contains tumor-specific mutations and disease-related DNA methylation patterns. Identifying new biomarkers-precursors of a potential cancer susceptibility or aggressiveness in such DNA would be a considerable advancement in prognostic medicine for patients at high risk of developing LC. Conclusion: A low level of LC detection might limit the number of DNA samples of patients with LC included in the studies. This is also the reason why specific methylation biomarkers have not yet been confirmed for clinical use. Future research on a larger number of blood samples, combined with the entire epigenome studies, may contribute to finding a group of LC biomarkers to improve LC detection.
Relevance: The 5-year overall survival rate(s) in NSCLC p-stage IA is 73%, and the recurrence rate in radically treated patients is almost 10%. The study aimed to evaluate the prognostic significance of several clinical and morphological factors and apply machine learning algorithms to predict the results of the overall survival of patients with lung cancer. Methods: The forms 030-6/y C34 – lung cancer (n=19,379) from the EROB database for 2014-2018 were analyzed, and the impact of risk factors on overall survival was assessed using the Kaplan-Meier method. Accordingly, the training data set for constructing forecasting models included 19,379 observations and 15 factors. The machine learning algorithms such as Random Forest Classifier, Gradient Boosting Classifier, Logistic Regression Model, Decision Tree Classifier, and K Nearest Neighbors (KNN) Classifier were implemented in the Python programming language. The results were evaluated by constructing an error matrix and calculating classification metrics: the proportion of correctly classified objects (accuracy) during training and validation (validation), accuracy (precision), completeness (recall), Kappa-Cohen. Results: In our study, 19,379 patients were analyzed, including 15,494 men (79.95%) and 3,885 women (20.04%). At the time of the study, 6,171 men (39.8%) and 1,962 women (49.5%) were alive. Median survival was 8.3 months (SE – 0.154 months, 95% CI – 7.96-8.56) in men and 15.43 months (SE – 1.0 months, 95% CI – 13.497-17.363) in women. At diagnosis, 1,037 patients (5.35%) had stage I disease, and 4,145 (21.38%) had stage II. Most patients (61.4%) had advanced stage NSCLC: 9,189 people (47.4%) were diagnosed with stage III, and 4,655 (24%) – with stage IV. The reliability of differences in median survival (χ2=3991.6, p=0.00) indicated the prognostic significance of the tumor process stage and its influence on the patient’s survival. Also, the revealed significant difference in the median survival of patients with various morphological forms of lung cancer suggests the prognostic significance of the morphological factor (the difference between those indicators was statistically significant, χ2=623.4 p=0.000). Conclusion: Machine learning models can predict the risk of fatal outcomes for patients after surgical treatment and registration in the EROB database. The creation of patient-oriented systems to support medical decision-making makes it possible to choose the optimal strategies for adjuvant therapy, dispensary observation, and frequency of diagnostic studies.
Relevance: The 5-year overall survival rate(s) in NSCLC p-stage IA is 73%, and the recurrence rate in radically treated patients is almost 10%. The study aimed to evaluate the prognostic significance of several clinical and morphological factors and apply machine learning algorithms to predict the results of overall survival of patients with lung cancer. Methods: The forms 030-6/y C34 – lung cancer (n=19,379) from the EROB database for 2014-2018 were analyzed, and the impact of risk factors on overall survival was assessed using the Kaplan-Meier method. Accordingly, the training data set for constructing forecasting models included 19,379 observations and 15 factors. The machine learning algorithms such as Random Forest Classifier, Gradient Boosting Classifier, Logistic Regression Model, Decision Tree Classifier, and K Nearest Neighbors (KNN) Classifier were implemented in the Python programming lan- guage. The results were evaluated by constructing an error matrix, calculating classification metrics: the proportion of correctly classified objects (accuracy) during training and validation (validation), accuracy (precision), completeness (recall), Kappa-Cohen. Results: In our study, 19,379 patients were analyzed, including 15,494 men (79.95%) and 3,885 women (20.04%). At the time of the study, 6,171 men (39.8%) and 1,962 women (49.5%) were alive. Median survival was 8.3 months (SE – 0.154 months, 95% CI – 7.96-8.56) in men and 15.43 months (SE – 1.0 months, 95% CI – 13.497-17.363) in women. At diagnosis, 1,037 patients (5.35%) had stage I disease, other 4,145 (21.38%) had stage II. Most patients (61.4%) had advanced stage NSCLC: 9,189 people (47.4%) were diagnosed with stage III, and 4,655 (24%) – with stage IV. The reliability of differences in median survival (χ2=3991.6, p=0.00) indicated the prognostic significance of the tumor process stage and its influence on the patient’s survival. Also, the revealed significant difference in the median survival of patients with various morphological forms of lung cancer sug- gests the prognostic significance of the morphological factor (the difference between those indicators was statistically significant, χ2=623.4 p=0.000). Conclusion: Machine learning models can predict the risk of fatal outcomes for patients after surgical treatment and registration in the EROB database. The creation of patient-oriented systems to support medical decision-making makes it possible to choose the optimal strategies for adju- vant therapy, dispensary observation, and frequency of diagnostic studies
Objective The purpose of the study is to analyze the immediate outcomes and results of video-assisted thoracoscopic lobectomy and lung resection performed in the surgical department of the AOC between 2014 and 2018. Methods For the period from 2014 to 2018, 118 patients with peripheral lung cancer were operated on in the surgical department of the AOC. The following operations were performed: lobectomy in 92 cases (78%), of which: upper lobectomy, 44 (47.8%); average lobectomy, 13 (14.1%); lower lobectomy, 32 (35%); bilobectomy, 3 (3.3%). All patients underwent extensive lymphadenectomy on the side of the operation. In 22 patients, for various reasons, preservation of thoracotomy was performed. Results The absence of N0 lymph node damage was observed in 82 patients (70%), the first-order lymph node damage N1 in 13 (11%), N2 in 13 (11%), N3 in 5 (4%), and NX in 5 (4%). Histological examination revealed: squamous cell carcinoma − 35.1%, adenocarcinoma − 28.5%, undifferentiated carcinoma − 8.3%, NSCLC − 5.6%, NEO − 4.6%, sarcoma − 1.8%. At the same time, in 12.7% of patients, mts was detected − lung damage, and in 3.4%, malignant cells were not detected. Most patients were activated on the first day after surgery. Conclusion An analysis of the direct results of the study allows us to conclude that video-assisted thoracoscopic surgery is a highly effective, minimally invasive, safe method for treating peripheral lung cancer, which allows us to recommend it for wider use in oncological practice.
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