Magnetic resonance-guided focused
ultrasound surgery (MRgFUS) constitutes a noninvasive treatment strategy to ablate deep-seated bone
metastases. However, limited evidence suggests that, although
cytokines are influenced by thermal
necrosis, there is still no
cytokine threshold for clinical responses. A prediction model to approximate the postablation immune status on the basis of circulating
cytokine activation is thus needed.
IL-6 and IP-10, which are proinflammatory
cytokines, decreased significantly during the acute phase. Wound-healing
cytokines such as
VEGF and PDGF increased after ablation, but the increase was not statistically significant. In this phase,
IL-6,
IL-13, IP-10, and eotaxin expression levels diminished the ongoing inflammatory progression in the treated sites. These
cytokine changes also correlated with the response rate of primary
tumor control after acute periods. The few-shot learning algorithm was applied to test the correlation between
cytokine levels and local control (p = 0.036). The best-fitted model included
IL-6,
IL-13, IP-10, and eotaxin as
cytokine parameters from the few-shot selection, and had an accuracy of 85.2%, sensitivity of 88.6%, and AUC of 0.95. The acceptable usage of this model may help predict the acute-phase prognosis of a patient with painful bone
metastasis who underwent local MRgFUS. The application of machine learning in bone
metastasis is equivalent or better than the current logistic regression.