Evaluation of Effectiveness of CyberKnife Stereotactic Radiosurgery for Spinal Tumors

Spinal Neoplasms

Spinal Neoplasms, Stereotactic Body Radiotherapy, CyberKnife Radiosurgery, Functional MRI, treatment outcome

This study aims to explore the reliability of the combination of functional magnetic resonance imaging and radiomics for evaluation of the therapeutic efficacy of CyberKnife stereotactic radiosurgery for spinal tumors. Accurate imaging assessment can help clinicians plan personalized therapeutic schedules for patients with spinal tumors .

Spinal tumors may be metastases or primary tumors; the former are more common. About 40% of cancer patients will have spinal metastasis. Primary spinal tumor is relatively rare, accounting for only about 8% of spinal tumors. For both metastasis and primary tumor, the aim of treatment is to reduce pain, maintain or improve neurological function, and maintain or restore spinal stability. In patients who cannot undergo surgery or need additional treatment after surgery, radiation therapy can relieve pain, prolong survival, improve the success rate of surgery, and reduce risk of metastasis and recurrence. However, the complex anatomy of the spine, and the numerous important organs around it, makes radiation treatment challenging. High-dose radiation therapy is necessary for long-term control of the tumor and for prevention of spinal column instability; however, this is impossible with traditional radiotherapy due to the presence of the radiosensitive spinal cord. Outcomes therefore tend to be poor for large and complex lesions. The CyberKnife-a stereotactic body radiation therapy (SBRT) platform that combines a lightweight linear accelerator, a robotic arm, an imaging system, and a respiratory tracking system-offers a feasible approach. It can achieve submillimeter level-precision treatment under imaging guidance. Currently, the effectiveness of CyberKnife radiosurgery for spinal tumors is decided by assessing imaging changes, relief of clinical symptoms, and needle biopsy, but all of these methods have limitations. On imaging, for example, change in lesion volume is used to assess tumor regression, but the size of a spinal tumor is not easy to measure and, moreover, decrease in tumor volume after treatment may take time . Post-treatment signal intensity changes in conventional magnetic resonance imaging (MRI) T1-weighted and T2-weighted sequences are difficult to interpret, and their relationship with treatment efficacy is also not clear. Generally speaking, alterations in microscopic structure and biological activity of the tumor occur much earlier than changes in gross morphology . Functional magnetic resonance imaging (fMRI) can therefore be more useful than conventional MRI for assessing treatment response. In fMRI, sequences such as diffusion-weighted imaging (DWI), dynamic contrast-enhanced MRI (DCE-MRI), and diffusion kurtosis imaging (DKI) reflect functional information of tissues from different perspectives. Application of artificial intelligence technology for analysis of medical imaging data is now an area of intense research. This new method, which is called radiomics, can help in solving many difficult clinical problems. By extracting a large number of highly representative quantitative imaging features from high-throughput medical image data, radiomics can help in evaluating treatment efficacy and predicting prognosis of spinal tumors. Investigators intend to explore the use of the combination of fMRI and radiomics for evaluating the effectiveness of CyberKnife radiosurgery for spinal tumors. The method will be able to evaluate both volume and functional changes in the tumor, and thus provide important information for planning of individualized therapeutic schedules for patients with spinal tumors.

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