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Automated segmentation of baseline metabolic total tumor burden in diffuse large B-cell lymphoma: which method is most successful ?

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Sally F Barrington, Ben Gjc Zwezerijnen, Henrica Cw de Vet, Martijn W Heymans, N George Mikhaeel, Coreline N Burggraaff, Jakoba J Eertink, Lucy C Pike, Otto S Hoekstra, Josee M Zijlstra, Ronald Boellaard

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Copyright © 2020 by the Society of Nuclear Medicine and Molecular Imaging, Inc.


  • BarringtonJNM2020

    BarringtonJNM2020.pdf, 1.26 MB, application/pdf

    Uploaded date:21 Aug 2020

    Version:Accepted author manuscript

    Licence:CC BY

King's Authors


Introduction: Metabolic tumor volume (MTV) is a promising biomarker of pretreatment risk in diffuse large B-cell lymphoma (DLBCL). Different segmentation methods can be used which predict prognosis equally well but give different optimal cut-offs for risk stratification. Segmentation can be cumbersome meaning a fast, easy and robust method is needed. Aims were to i) evaluate the best automated MTV workflow in DLBCL ii) determine if uptake time, (non)compliance with standardized recommendations for FDG scanning and subsequent disease progression influenced the success of segmentation iii) assess differences in MTV values and discriminatory power of segmentation methods. Methods: 140 baseline FDG-PET/CT scans were selected from UK and Dutch studies in DLBCL to provide a balance between scans at 60- or 90-minutes uptake, parameters compliant or non-compliant with standardized recommendations for scanning and patients with or without progression. An automated tool was used for segmentation using i) standardized uptake value (SUV) 2.5 ii) SUV 4.0 iii) adaptive thresholding [A50P] iv) 41% of maximum SUV [41%] v) majority vote including voxels detected by ≥2 methods [MV2] and vi) detected by ≥3 methods [MV3]. Two independent observers rated the success of the tool to delineate MTV. Scans that required minimal interaction were rated "success"; scans where > 50% of tumor was missed or required more than 2 editing steps were rated as "failure". Results: 138 scans were evaluable, with significant differences in success and failure ratings between methods. The best performing was SUV4.0, with higher success and lower failure rates than all other methods except MV2 which also performed well. SUV4.0 gave a good approximation of MTV in 105 (76%) scans, with simple editing for a satisfactory result in additionally 20% of cases. MTV was significantly different for all methods between patients with and without progression. SUV41% performed slightly worse with longer uptake times, otherwise scanning conditions and patient outcome did not influence the tool's performance. The discriminative power of methods was similar, but MTV values were significantly greater using SUV4.0 and MV2 than other thresholds except for SUV2.5. Conclusion: SUV4.0 and MV2 are recommended for further evaluation. Automated estimation of MTV is feasible.

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