OBJECTIVE: The purposes of this study were to observe the relation between signal intensity (SI) on MR images with a high b value and the apparent diffusion coefficient (ADC) of bone marrow on body diffusion-weighted MR images, to determine cutoff values that enable separation of malignant and normal bone marrow, and to identify the upper ADC values of untreated multiple myeloma lesions and bone metastatic lesions of breast cancer. MATERIALS AND METHODS: Retrospective evaluations of 16 patients without bone disease, 21 patients with untreated metastases of breast cancer, and 12 patients with myeloma undergoing body diffusion-weighted MRI were performed (b values, 50 s/mm(2) and 800 or 900 s/mm(2)). Normal yellow and red bone marrow regions were compared with metastatic breast and myeloma bone marrow lesions (one to five regions of interest per patient). SI values were normalized to kidney, muscle, and spinal cord SI. Signal-to-noise ratio and ADC for each lesion were recorded. Nonparametric, receiver operating characteristic, and nonlinear regression analyses were performed. RESULTS: Yellow bone marrow and red bone marrow ADC values were lower than the tumor values (p < 0.001; area under the curve, 0.94; cutoff, 774 μm(2)/s). Tissue-normalized SI and the signal-to-noise ratio of normal bone marrow were also lower than those in tumor regions (p < 0.001; area under the curve, 0.86-0.88). Second-order polynomial curve fitting between SI and ADC was observed (muscle normalized SI, R(2) = 0.4). The 95th percentile and maximum values for mean tumor ADC distribution were 1209 μm(2)/s and 1433 μm(2)/s. CONCLUSION: Both tissue-normalized SI and ADC measurements allow differentiation between normal bone marrow and tumors of myeloma and breast cancer. The presence of a nonlinear relation between bone marrow SI and ADC values enables definition of an upper limit of ADC value for untreated myeloma lesions and metastatic lesions of breast cancer.
OBJECTIVE: The purposes of this study were to observe the relation between signal intensity (SI) on MR images with a high b value and the apparent diffusion coefficient (ADC) of bone marrow on body diffusion-weighted MR images, to determine cutoff values that enable separation of malignant and normal bone marrow, and to identify the upper ADC values of untreated multiple myeloma lesions and bone metastatic lesions of breast cancer. MATERIALS AND METHODS: Retrospective evaluations of 16 patients without bone disease, 21 patients with untreated metastases of breast cancer, and 12 patients with myeloma undergoing body diffusion-weighted MRI were performed (b values, 50 s/mm(2) and 800 or 900 s/mm(2)). Normal yellow and red bone marrow regions were compared with metastatic breast and myeloma bone marrow lesions (one to five regions of interest per patient). SI values were normalized to kidney, muscle, and spinal cord SI. Signal-to-noise ratio and ADC for each lesion were recorded. Nonparametric, receiver operating characteristic, and nonlinear regression analyses were performed. RESULTS: Yellow bone marrow and red bone marrow ADC values were lower than the tumor values (p < 0.001; area under the curve, 0.94; cutoff, 774 μm(2)/s). Tissue-normalized SI and the signal-to-noise ratio of normal bone marrow were also lower than those in tumor regions (p < 0.001; area under the curve, 0.86-0.88). Second-order polynomial curve fitting between SI and ADC was observed (muscle normalized SI, R(2) = 0.4). The 95th percentile and maximum values for mean tumor ADC distribution were 1209 μm(2)/s and 1433 μm(2)/s. CONCLUSION: Both tissue-normalized SI and ADC measurements allow differentiation between normal bone marrow and tumors of myeloma and breast cancer. The presence of a nonlinear relation between bone marrow SI and ADC values enables definition of an upper limit of ADC value for untreated myeloma lesions and metastatic lesions of breast cancer.
Authors: Frederic Carsten Schmeel; Julian Alexander Luetkens; Peter Johannes Wagenhäuser; Michael Meier-Schroers; Daniel Lloyd Kuetting; Andreas Feißt; Jürgen Gieseke; Leonard Christopher Schmeel; Frank Träber; Hans Heinz Schild; Guido Matthias Kukuk Journal: Eur Radiol Date: 2018-01-08 Impact factor: 5.315
Authors: Nicolas F Michoux; Jakub W Ceranka; Jef Vandemeulebroucke; Frank Peeters; Pierre Lu; Julie Absil; Perrine Triqueneaux; Yan Liu; Laurence Collette; Inneke Willekens; Carola Brussaard; Olivier Debeir; Stephan Hahn; Hubert Raeymaekers; Johan de Mey; Thierry Metens; Frédéric E Lecouvet Journal: Eur Radiol Date: 2021-01-06 Impact factor: 5.315