Aim. To correlate the concentration of markers of extracellular matrix (ECM) destruction in peripheral blood with morphological characteristics of inflammatory activity and to evaluate their applicability in determining treatment strategy for patients with pulmonary tuberculoma (TUB). Materials and methods. Peripheral blood samples were taken from 87 patients diagnosed with TUB. The concentrations of matrix metalloproteinases (MMPs), such as collagenases (MMP-1 and MMP-8), stromelysin (MMP-3), gelatinase (MMP-9), and tissue inhibitors of metalloproteinases (TIMP-1), were measured using the ELBA method (R&D Systems, Minneapolis, MN, USA). The activity of a f macroglobulin (MG), neutrophil elastase (NE) and proteinase inhibitor (PI) were measured using enzyme assays; acute phase reactants (APR) - haptoglobin (GP) and a l -acid glycoprotein (AGP) - were measured using immunoturbidimetric assays (Thermo Fisher Scientific, USA). Statistica 7 software package and the predictive classification method (PCM) were employed for data analysis. Results. It has been established that TUB as a clinical form of pulmonary tuberculosis (TB) is characterised by enzyme imbalance between MMP, NE and their inhibitors, namely, by an increase in the levels of MMP-1, MMP-8, MMP-9, and NE and a decrease in MG without changes in MMP-3, TIMP-1 and PI. There is a clear correlation between markers of ECM destruction in blood and morphological characteristics of inflammatory activity. The combinations of MMP-1 and MG can serve as a diagnostic criterion for caseous necrosis in the TUB centre (the alterative component of inflammation), while the levels of MMP-8 and MG can be indicative of granulomatous changes in the capsule (the productive component of inflammation). Various combinations of markers of ECM destruction (with or without APR) enable to predict a particular morphological pattern with accuracy from 80% up to 92%. Conclusion. When determining a treatment strategy for patients with TUB, biochemical data which allow to assess the tempo and intensity of the inflammation process should be taken into account along with a dataset of clinical and radiological features.