Correlation between glycolytic activity on [18F]‐FDG‐PET and cell density on diffusion‐weighted MRI in lymphoma at staging. Issue 5 (31st October 2017)

• Record Type: Journal Article
• Authors: Giraudo, Chiara; Karanikas, Georgios; Weber, Michael; Raderer, Markus; Jaeger, Ulrich; Simonitsch‐Klupp, Ingrid; Mayerhoefer, Marius E.
• Is Part Of: Journal of magnetic resonance imaging. Volume 47:Issue 5(2018)
• Page Start: 1217
• Page End: 1226
• Publication Date: 2017-10-31
• Subjects: lymphoma -- PET/MR -- diffusion weighted imaging -- staging -- biomarkers; Magnetic resonance imaging -- Periodicals; 616
• DOI: 10.1002/jmri.25884
• Languages: English
• ISSNs: 1053-1807

Abstract

Abstract : Background: [18F]‐FDG‐PET/MR carries a high diagnostic value in whole‐body oncologic imaging and allows simultaneous quantitative measurements of glucose metabolism (SUV) and cell density (ADC). Purpose: To determine the relationship between SUV and ADC values extracted from simultaneously acquired [18F]‐FDG‐PET/MR data of patients with FDG‐avid lymphomas at staging. Study Type: Prospective. Population: Patients with histologically proven lymphoma referred for staging. Field Strength/Sequences: Hybrid PET/MR device (3T); axial, two‐point Dixon, 3D, volume‐interpolated, T1 ‐weighted breath‐hold sequence; coronal T2 ‐weighted half‐Fourier acquisition single‐shot turbo spin‐echo. Single‐shot, echo‐planar imaging‐based, spectral adiabatic inversion recovery diffusion‐weighted imaging. Assessment: Staging was performed according to the modified Ann Arbor system by a board‐certified radiologist and a board‐certified nuclear medicine physician, blinded to the clinical and histological information, in consensus. SUVs and ADCs values were collected, for each positive nodal and extranodal region, from the lesion demonstrating the largest diameter. Statistical Tests: Descriptive data included absolute frequencies and percentages for categorical data, and arithmetic means and 95% confidence intervals for scale‐type data. The Pearson correlation coefficient was used to assess the relationship between SUVs and ADCs ( P ≤ 0.05). Additional separate analyses were performed according to histological lymphoma subtype, for nodal and extranodal lesions and excluding bone lesions. Results: Overall, 100 patients were examined (55 males, 45 females; age ± SD in years, 51.6 ± 19.5). Histology revealed Hodgkin‐lymphoma and non‐Hodgkin‐lymphoma in 26 and 74 patients, respectively. Twenty patients were stage I, 21 stage II, 24 stage III, and 31 stage IV on [18F]‐FDG‐PET/MR (ie, four patients negative at imaging). Based on 391 lesions (ie, 367 excluding bone lesions) no significant correlations between SUVmax and ADCmin, or between SUVmean and ADCmean, emerged (respectively, r = 0.091, P = 0.073, 95% CI [–0.01, 0.19] and r = –0.032, P = 0.527, 95% CI [–0.13, 0.07] including bone lesions; r = 0.06, P = 0.21, 95% CI [–0.04, 0.17] and r = –0.05, P = 0.32, 95% CI [–0.15, 0.05] excluding bone lesions). A significant correlation was observed only between ADCmean and SUVmean for follicular lymphoma ( r = −0.33, P = 0.001). Data Conclusion: SUVs and ADCs were demonstrated to be independent biomarkers in lymphomas. A moderate correlation between SUVs and ADCs likely is present in follicular lymphoma. Level of Evidence : 1 Technical Efficacy : Stage 3 J. Magn. Reson. Imaging 2018;47:1217–1226.