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Assessing visibility and bone changes of spinal metastases in CT scans: a comprehensive analysis across diverse cancer types

  • Jung Oh Lee,
  • Dong Hyun Kim,
  • Hee-Dong Chae,
  • Eugene Lee,
  • Ji Hee Kang,
  • Ji Hyun Lee,
  • Hyo Jin Kim,
  • Jiwoon Seo,
  • Jee Won Chai

摘要

Objectives

To analyze the characteristics of spinal metastasis in CT scans across diverse cancers for effective diagnosis and treatment, using MRI as the gold standard.

Methods

A retrospective study of 309 patients from four centers, who underwent concurrent CT and spinal MRI, revealing spinal metastasis, was conducted. Data on metastasis including total number, volume, visibility on CT (visible, indeterminate, or invisible), and type of bone change were collected. Through chi-square and Mann–Whitney U tests, we characterized the metastasis across diverse cancers and investigated the variation in the intra-individual ratio representing the percentage of lesions within each category for each patient.

Results

Out of 3333 spinal metastases from 309 patients, 55% were visible, 21% indeterminate, and 24% invisible. Sclerotic and lytic lesions made up 47% and 43% of the visible and indeterminate categories, respectively. Renal cell carcinoma (RCC), prostate cancer, and hepatocellular carcinoma (HCC) had the highest visibility at 86%, 73%, and 67% (p < 0.0001, p < 0.0001, and p = 0.003), while pancreatic cancer was lowest at 29% (p < 0.0001). RCC and HCC had significantly high lytic metastasis ratios (interquartile range (IQR) 0.96–1.0 and 0.31–1.0, p < 0.001 and p = 0.005). Prostate cancer exhibited a high sclerotic lesion ratio (IQR 0.52–0.97, p < 0.001). About 39% of individuals had invisible or indeterminate lesions, even with a single visible lesion on CT. The intra-individual ratio for indeterminate and invisible metastases surpassed 18%, regardless of the maximal size of the visible metastasis.

Conclusions

This study highlights the variability in characteristics of spinal metastasis based on the primary cancer type through unique lesion-centric analysis.