Background. According to a report by the International Agency for Research on Cancer (IARC), lung cancer (LC) is among the leading causes of morbidity and mortality worldwide, with an estimated incidence of 14.1                million new cases of the disease and 8.2 million cancer deaths in 2012. Lung cancer is the most common cancer worldwide, accounting for 13 % of all new cancer cases and 19.4 % of deaths.The purpose of the study was to evaluate LC prevalence and to measure the quality of population-based cancer registries by the indices of the proportion of total incident cases.Material and methods. The study material was given from the monograph «Cancer on Five Continents», which included data from the database of the Cancer Registry of PA. Herzen Moscow Research Oncology Institute (St. Petersburg), surveys of morbidity and mortality in the North-West Federal District, estimations of proportion of the true incidence that was registered in population-based registries.Results. The analysis of lung cancer morbidity and mortality in Russia showed a significant improvement in analytical indices over the past 10 years, however, underestimation of primary cases, reduced the overall lung cancer incidence rate.Conclusions. The dynamics of age-specific lung cancer incidence was shown. The loss of primary lung cancer cases was estimated to be 15-20 % annually.

Oncologists nowadays are faced with big amount of heterogeneous medical data of diagnostic studies. Possible errors in determining the nature and extent of spread the tumor process will inevitably reduce the effectiveness of treatment and increase the unnecessary costs to it. To reduce the burden on clinicians, various computer-aided solutions based on machine learning algorithms are being developed. We made an attempt to evaluate effectiveness of thirteen machine learning algorithms in the tasks of classification of pathologic tissue samples in cancerous thorax based on gene expression levels. For a preliminary study we used open data set of molecular genetics composition of lung adenocarcinoma and pleural mesothelioma. Effectiveness of machine learning algorithms was evaluated by Matthews correlation coefficient and Area Under ROC Curve. Best results were showed by two methods: Bayesian logistic regression and Discriminative Multinomial Naive Bayes classifier. Nevertheless, all of the methods were effective at automatic discrimination of two types of cancer. That proves machine learning algorithms are applicable in lung cancer classification. In the future studies it will be carried out a similar analysis of the diagnostic value of methods for other malignancies with more complex differential morphological diagnosis. Similar methods can be applied to other diagnostic studies including computerized tomography image analysis in the differential diagnosis of lung nodules.

Cancer screening literature was discussed in this review publication. Broad spectrum of studies was used to make conclusion about effectiveness of screening methods in reaching its major objectives, perspective of screening methods for several cancer types were also discussed. Qualitative assessment of studies was done. Cervical cancer, breast cancer and colorectal cancer screening was proved to be effective. Effectiveness of prostate and lung cancer screening as well as population-based stomach cancer prevention is also discussed. Negative and inconclusive results of screening studies of the other cancer types were also mentioned and perspectives for future diagnostics option for cancer screening were given.

This article reviews the literature and summarizes single institution experience of applying different diagnostic algorithms for lung cancer. All diagnostic methods can be divided into three groups: non-invasive; minimally invasive and invasive. The non-invasive methods include clinical examination; imaging methods for anatomical, functional and multimodal visualization; sputum cytological, analysis of the exhaled breath, detection of various blood and sputum markers. Minimally invasive methods include endoscopy, percutaneous fine-needle and core-needle biopsy. Invasive methods include diagnostic thoracoscopy and laparoscopy, mediastinoscopy, parasternal mediastinotomy and diagnostic thoracotomy. While creating an individual diagnostic plan for each patient it is necessary to carefully analyze the effectiveness, safety, sensitivity, specificity and of different methods available among wide range of modern diagnostic techniques. Optimization of lung cancer diagnosis methods, which includes early cancer detection, is one of priority areas of modern oncology. Many aspects of this problem remain unresolved and require further research

2 ФБГУ «Научноисследовательский институт онкологии им. проф. Н.Н. Петрова» Минздрава РФ (Санкт-Петербург, Россия) 3 ГБУЗ «Санкт-Петербургский клинический научно-практический центр специализированных видов медицинской помощи (онкологический)»