Translational Oncology (Apr 2018)

Whole Tumor Histogram-profiling of Diffusion-Weighted Magnetic Resonance Images Reflects Tumorbiological Features of Primary Central Nervous System Lymphoma

  • Stefan Schob,
  • Benno Münch,
  • Julia Dieckow,
  • Ulf Quäschling,
  • Karl-Titus Hoffmann,
  • Cindy Richter,
  • Nikita Garnov,
  • Clara Frydrychowicz,
  • Matthias Krause,
  • Hans-Jonas Meyer,
  • Alexey Surov

Journal volume & issue
Vol. 11, no. 2
pp. 504 – 510

Abstract

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PURPOSE: Diffusion weighted imaging (DWI) quantifies motion of hydrogen nuclei in biological tissues and hereby has been used to assess the underlying tissue microarchitecture. Histogram-profiling of DWI provides more detailed information on diffusion characteristics of a lesion than the standardly calculated values of the apparent diffusion coefficient (ADC)—minimum, mean and maximum. Hence, the aim of our study was to investigate, which parameters of histogram-profiling of DWI in primary central nervous system lymphoma can be used to specifically predict features like cellular density, chromatin content and proliferative activity. PROCEDURES: Pre-treatment ADC maps of 21 PCNSL patients (8 female, 13 male, 28–89 years) from a 1.5T system were used for Matlab-based histogram profiling. Results of histopathology (H&E staining) and immunohistochemistry (Ki-67 expression) were quantified. Correlations between histogram-profiling parameters and neuropathologic examination were calculated using SPSS 23.0. RESULTS: The lower percentiles (p10 and p25) showed significant correlations with structural parameters of the neuropathologic examination (cellular density, chromatin content). The highest percentile, p90, correlated significantly with Ki-67 expression, resembling proliferative activity. Kurtosis of the ADC histogram correlated significantly with cellular density. CONCLUSIONS: Histogram-profiling of DWI in PCNSL provides a comprehensible set of parameters, which reflect distinct tumor-architectural and tumor-biological features, and hence, are promising biomarkers for treatment response and prognosis.