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The T1 Ratio of Marrow (TROM) as a Novel Tool to Identify Metastatic from Nonmalignant Marrow Lesions of the Spine: A Pilot Study.

Neha NischalMahtab AfzaliParham ShojaieChristine AzzopardiKarthikeyan Parthasarathy IyengarShahnawaz HaleemJonathan Daniel StevensonRajesh Botchu
Published in: The Indian journal of radiology & imaging (2023)
Objective  The purpose of this study was to analyze quantitative values of normal and abnormal marrow on T1-weighted images of spine, to propose a ratio for T1 values of abnormal to normal vertebrae, and to assess whether this ratio could be helpful in predicting presence of neoplastic lesions in the spine. Materials and Methods  One-hundred randomly selected magnetic resonance imagings of lumbar spine without infection, fracture, and tumor were selected to form normal cohort. A second cohort of 100 metastasis of lumbar spine was identified. Ratio of T1 value of vertebral body to the T1 value of the inferior vertebral body was performed for normal cohort from D11 to L5. Ratio of T1 value of metastasis to adjacent normal vertebral body was done for metastatic cohort. Data was analyzed using standard t -test and kappa was performed for intra- and inter-observer reliability. Results  A decline in T1 value of abnormal to normal marrow was seen in patients with metastasis that was statistically significant. We call this the T1 ratio of marrow (TROM). The sensitivity and accuracy with the cutoff value of TROM at 0.7 (92% sensitivity, 97.1% accuracy) are better than at 0.6 (75% sensitivity, 96.2% accuracy) or 0.5 (47% sensitivity, 93.2% accuracy). A subset analysis of the other T1 hypointense benign lesions including atypical hemangiomas and focal marrow hyperplasia, however, revealed overlapping TROM values with the metastatic cohort. Conclusion  Using the TROM on T1-weighted images could not confidently differentiate malignant from benign T1 hypointense lesions of the spine.
Keyphrases
  • magnetic resonance
  • squamous cell carcinoma
  • small cell lung cancer
  • bone mineral density
  • computed tomography
  • big data
  • high resolution
  • body composition
  • inflammatory response