Baseline PET/CT imaging parameters for prediction of treatment outcome in Hodgkin and diffuse large B cell lymphoma: a systematic review.

Published on Feb 18, 2021in European Journal of Nuclear Medicine and Molecular Imaging7.081
· DOI :10.1007/S00259-021-05233-2
R. Frood6
Estimated H-index: 6
(University of Leeds),
C. Burton (Leeds Teaching Hospitals NHS Trust)+ 4 AuthorsAndrew Scarsbrook8
Estimated H-index: 8
(University of Leeds)
PURPOSE To systematically review the literature evaluating clinical utility of imaging metrics derived from baseline fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (PET/CT) for prediction of progression-free (PFS) and overall survival (OS) in patients with classical Hodgkin lymphoma (HL) and diffuse large B cell lymphoma (DLBCL). METHODS A search of MEDLINE/PubMed, Web of Science, Cochrane, Scopus and databases was undertaken for articles evaluating PET/CT imaging metrics as outcome predictors in HL and DLBCL. PRISMA guidelines were followed. Risk of bias was assessed using the Quality in Prognosis Studies (QUIPS) tool. RESULTS Forty-one articles were included (31 DLBCL, 10 HL). Significant predictive ability was reported in 5/20 DLBCL studies assessing SUVmax (PFS: HR 0.13-7.35, OS: HR 0.83-11.23), 17/19 assessing metabolic tumour volume (MTV) (PFS: HR 2.09-11.20, OS: HR 2.40-10.32) and 10/13 assessing total lesion glycolysis (TLG) (PFS: HR 1.078-11.21, OS: HR 2.40-4.82). Significant predictive ability was reported in 1/4 HL studies assessing SUVmax (HR not reported), 6/8 assessing MTV (PFS: HR 1.2-10.71, OS: HR 1.00-13.20) and 2/3 assessing TLG (HR not reported). There are 7/41 studies assessing the use of radiomics (4 DLBCL, 2 HL); 5/41 studies had internal validation and 2/41 included external validation. All studies had overall moderate or high risk of bias. CONCLUSION Most studies are retrospective, underpowered, heterogenous in their methodology and lack external validation of described models. Further work in protocol harmonisation, automated segmentation techniques and optimum performance cut-off is required to develop robust methodologies amenable for clinical utility.
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