Combined Application of Deep Learning and Radiomic Features for Classification of Lung CT Images

Introduction. In oncology, accurate classification of lung cancer mutations plays a key role in developing personalized treatment strategies. Lung cancer, distinguished by its heterogeneity, presents significant challenges in diagnosis and treatment, requiring innovative approaches for precise mutation classification.

Aim. To introduce a new methodology combining deep learning and radiomic features extracted from computed tomography (CT) images for classification of lung cancer mutations.

Materials and methods. The ResNet18 architecture was adapted to integrate radiomic features directly into the deep learning workflow. The use of a convolutional neural network enabled large volumes of data to be processed, surpassing the performance of conventional methods. The analysis involved identification of significant radiomic features, such as texture, shape, and tumor boundaries, which were automatically extracted and used to train the model. The technique was tested on an extensive dataset containing CT images of various lung cancer subtypes, including adenocarcinoma and squamous cell carcinoma.

Results. The model demonstrated an overall mutation classification accuracy of 98.6 %, significantly exceeding the results achieved using conventional approaches. The high accuracy confirms the effectiveness of combining radiomic features with deep learning in identifying various genetic mutations in lung cancer. The results also indicate the high potential of the method in the development of non-invasive diagnostic tools and improving personalized treatment approaches.

Conclusion. This work emphasizes the importance of integrating high-level abstractions of deep learning models with detailed analysis of radiomic data to enhance the predictive accuracy of non-invasive diagnostic tools, which could significantly improve diagnostic processes and contribute to the development of treatment strategies in oncology.

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