THE ROLE OF ARTIFICIAL INTELLIGENCE IN THE DIAGNOSIS AND TREATMENT OF SOLITARY LUNG LESIONS

Solitary lung lesions are being detected increasingly often in modern clinical practice due to the widespread use of chest computed tomography (CT), the development of screening programs based on low-dose computed tomography (LDCT), and advances in medical imaging technologies. Although the majority of solitary pulmonary nodules are benign, a certain proportion may represent an early manifestation of lung cancer. Therefore, the main clinical challenge is to achieve early detection of malignant lesions while avoiding unnecessary invasive investigations and treatment of benign nodules.

In recent years, artificial intelligence, machine learning, deep learning, and radiomics have emerged as im­portant approaches for the assessment of solitary lung lesions. Artificial intelligence is being used for the automated detection of pulmonary nodules on CT scans, their segmentation, measurement of size and volume, assessment of growth dynamics, differentiation between benign and malignant lesions, and prediction of the probability of malig­nancy. A 2024 meta-analysis demonstrated that externally validated deep learning–based computer-aided diagnostic models increased sensitivity by 11.6% compared with physician assessment and by 14.5% compared with clinical risk models alone.

Radiomics enables the extraction of a large number of quantitative features from CT images that are difficult to fully assess by visual examination alone. According to a 2023 meta-analysis, CT radiomics models demonstrated an overall AUC of 0.91, sensitivity of 0.86, and specificity of 0.84 for predicting the malignancy of pulmonary nodules. However, methodological limitations and a high risk of bias were reported in the majority of studies.

The application of artificial intelligence is not limited to diagnosis and is expanding to the selection of follow-up intervals, automated detection of nodule growth, bronchoscopic navigation, surgical planning, and determination of treatment volumes in radiation therapy. At the same time, insufficient external validation of algorithms, differences between populations, variations in CT protocols, the “black box” problem, algorithmic errors, data security, and issues of clinical responsibility remain important barriers to the widespread implementation of these technologies in clinical practice.