期刊
THROMBOSIS AND HAEMOSTASIS
卷 114, 期 6, 页码 1251-1259出版社
GEORG THIEME VERLAG KG
DOI: 10.1160/TH15-04-0357
关键词
Lung cancer; venous thromboembolism; risk assessment; biomarker; fractal
资金
- NISCHR Biomedical Research Unit (BRU) grant (BRO1)
- Engineering and Physical Sciences Research Council [EP/I019405/1, EP/L024799/1, 1339757, EP/M000621/1] Funding Source: researchfish
- Marie Curie [MCCC-FCO-11-C] Funding Source: researchfish
- EPSRC [EP/I019405/1, EP/M000621/1, EP/L024799/1] Funding Source: UKRI
Venous thromboembolism (VTE) is common in cancer patients, and is the second commonest cause of death associated with the disease. Patients with chronic inflammation, such as cancer, have been shown to have pathological clot structures with modulated mechanical properties. Fractal dimension (d(f)) is a new technique which has been shown to act as a marker of the microstructure and mechanical properties of blood clots, and can be performed more readily than current methods such as scanning electron microscopy (SEM). We measured d(f) in 87 consecutive patients with newly diagnosed lung cancer prior to treatment and 47 matched-controls. Mean group values were compared for all patients with lung cancer vs controls and for limited disease vs extensive disease. Results were compared with conventional markers of coagulation, fibrinolysis and SEM images. Significantly higher values of df were observed in lung cancer patients compared with controls and patients with extensive disease had higher values than those with limited disease (p<0.05), whilst conventional markers failed to distinguish between these groups. The relationship between d(f) of the incipient clot and mature clot microstructure was confirmed by SEM and computational modelling: higher d(f) was associated with highly dense clots formed of smaller fibrin fibres in lung cancer patients compared to controls. This study demonstrates that d(f) is a sensitive technique which quantifies the structure and mechanical properties of blood clots in patients with lung cancer. Our data suggests that d(f) has the potential to identify patients with an abnormal clot microstructure and greatest VTE risk.
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