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Artificial neural networks approach to early lung cancer detection

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Goryński K., Safian I., Grądzki W., Marszałł M., Krysiński J., Goryński S., Bitner A., Romaszko J., Buciński A.
Open Medicine
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2014
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vol. 9
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issue 5
632-641
EN
Lung cancer is rated with the highest incidence and mortality every year compared with other forms of cancer, therefore early detection and diagnosis is essential. Artificial Neural Networks (ANNs) are “artificial intelligence” software which have been used to assess a few prognostic situations. In this study, a database containing 193 patients from Diagnostic and Monitoring of Tuberculosis and Illness of Lungs Ward in Kuyavia and Pomerania Centre of the Pulmonology (Bydgoszcz, Poland) was analysed using ANNs. Each patient was described using 48 factors (i.e. age, sex, data of patient history, results from medical examinations etc.) and, as an output value, the expected presence of lung cancer was established. All 48 features were retrospectively collected and the database was divided into a training set (n=97), testing set (n=48) and a validating set (n=48). The best prediction score of the ANN model (MLP 48-9-2) was above 0.99 of the area under a receiver operator characteristic (ROC) curve. The ANNs were able to correctly classify 47 out of 48 test cases. These data suggest that Artificial Neural Networks can be used in prognosis of lung cancer and could help the physician in diagnosis of patients with the suspicion of lung cancer.
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Prediction of antimicrobial activity of imidazole derivatives by artificial neural networks

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Wnuk M., Marszałł M., Zapęcka A., Nowaczyk A., Krysiński J., Romaszko J., Kawczak P., Bączek T., Buciński A.
Open Medicine
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2013
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vol. 8
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issue 1
1-15
EN
The main goal of our study is the analysis of data obtained from molecular modeling for a series of imidazole derivatives that possess strong antifungal activity. The research was designed to use artificial neural network (ANN) analysis to determine quantitative relationships between the structural parameters and anti-Streptococcus pyogenes activity of a series of imidazole derivatives. ANN in association with quantitative structure-activity relationships (QSAR) represents a promising tool in the search for drug candidates among the practically unlimited number of possible derivatives. In this work, a series of 286 imidazole derivatives presented as cationic three-dimensional structures was used. The activity was expressed as a logarithm of the reciprocal of the minimal inhibitory concentrations, log 1/MIC. Multilayer perceptron ANN was used for predictions of antimicrobial potency of new imidazole derivatives on the basis of their structural descriptors. The obtained correlation coefficient equaled 0.9461 for the learning set, 0.9060 for the validation set and 0.8824 for the testing set of imidazole derivatives. Hence, satisfactory and practically useful predictions of anti-Streptococcus pyogenes activity for a series of imidazole derivatives was obtained, supporting the future successful interpretation of QSAR analysis for those compounds.
3
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ANN as a prognostic tool after treatment of non-seminoma testicular cancer

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Marszałł M., Krysiński J., Sroka W., Nyczak Z., Stefanowicz M., Waśniewski T., Romaszko J., Buciński A.
Open Medicine
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2012
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vol. 7
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issue 5
672-679
EN
Testicular cancer is rare but is the most common cancer in males between 15 and 34 years of age. Two principal types of testicular cancer are distinguished: seminomas and non-seminomas. If detected early, the overall cure rate for testicular cancer exceeds 90%. In this study, artificial neural network (ANN) analysis as a prognostic tool was demonstrated regard to five year recurrence after the non-seminoma treatment. Data from 202 patients treated for non-seminoma were available for evaluation and comparison. A total of 32 variables were analysed using the ANN. The ANN approach, as an advanced multivariate data processing method, was demon-strated to provide objective prognostic data. Some of these prognostic factors are consistent or even imperceptible with previously evaluated by other statistical methods.
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