Determination of the histological type of lung cancer based on radiomic analysis of computed tomography chest images; Определение гистологического типа рака легких на основе радиомического анализа данных компьютерной томографии

Lung cancer is one of the most common cancers. Bronchoscopy and transthoracic lung biopsy under the control of computed tomography (CT) are used for morphological verification of the tumor. Both of these technologies are invasive with certain risks and high costs. The accuracy of the morphologically verified diagnosis of lung cancer in Russia reaches an average of 88.2%. Treatment tactics, progression and prognosis of the disease depends on the histological type of lung cancer. The gold standard for lung cancer diagnosis is computed tomography of the chest. A developing area of CT image processing is radiomics, a mathematical analysis of data from radiation research methods that allows the detection of tissue texture features at a level inaccessible to the eye of a radiologist. The use of radiomics methods can contribute to the determination of the histotype of lung cancer even at the stage of diagnostic search. Objective: to determine the most common histological types of lung cancer based on the textural analysis of CT-scans of the chest organs. Materials and methods. The study included data from 200 patients treated at the RSCRR with histologically confirmed lung cancer, including 100 patients with small-cell lung cancer, 100 patients with non-small cell lung cancer (50 of them with adenocarcinoma and 50 with squamous cell carcinoma). 107 radiomic features were calculated for each tumor. Machine learning models were built in the Python 3.10 programming language using specialized libraries. To select the most effective models, standard machine learning metrics were used: precision, recall, accuracy, f1-measure and the area under the receiver operating characteristic curve (ROC-AUC). Results. Several machine learning models were developed, the best metrics were gradient boosting for differentiating non-small cell lung cancer and small-cell lung cancer with ROC-AUC 0.973 and a random forest based on three trees for differentiating adenocarcinoma and squamous cell carcinoma with ROC-AUC 0.833. Conclusion. Classification models developed by us have high metrics of diagnostic accuracy, which allows us to discourse about the applicability of radiomics features for differentiating various types of lung cancer at the stage of diagnostic search, as well as in situations where it is impossible to obtain material for histological examination. © 2025 Elsevier B.V., All rights reserved.