Ronald Boellaard, Gerben J.C. Zwezerijnen, Irène Buvat, Laurence Champion, Narinée Hovhannisyan-Baghdasarian, Fanny Orlhac, Anne I.J. Arens, Daphne Lobeek, Filiz Celik, Cristina Mitea, Julia E. Huijbregts, Nelleke Tolboom, Bart de Keizer, Roelf Valkema, Floris H.P. van Velden, Petra Dibbets-Schneider, Sanne E. Wiegers, Pieternella J. Lugtenburg, Sally F. Barrington, Josée M. Zijlstra
{"title":"Measuring Total Metabolic Tumor Volume from 18F-FDG PET: A Reality Check","authors":"Ronald Boellaard, Gerben J.C. Zwezerijnen, Irène Buvat, Laurence Champion, Narinée Hovhannisyan-Baghdasarian, Fanny Orlhac, Anne I.J. Arens, Daphne Lobeek, Filiz Celik, Cristina Mitea, Julia E. Huijbregts, Nelleke Tolboom, Bart de Keizer, Roelf Valkema, Floris H.P. van Velden, Petra Dibbets-Schneider, Sanne E. Wiegers, Pieternella J. Lugtenburg, Sally F. Barrington, Josée M. Zijlstra","doi":"10.2967/jnumed.124.269271","DOIUrl":null,"url":null,"abstract":"<p>Measuring total metabolic tumor volume (TMTV) on <sup>18</sup>F-FDG PET/CT images in clinical practice requires a fast, reliable, and easy-to-perform multilesional segmentation workflow. We conducted a field test to derive total metabolic volumes using 5 representative baseline <sup>18</sup>F-FDG PET/CT scans from patients with diffuse large B-cell lymphoma. The scans were transferred to 10 different sites or readers who used different commercially available software platforms to derive TMTV after a recently proposed benchmark workflow. Observed TMTVs were compared with reference values, and overall analysis times were reported. Our results show that TMTVs can be obtained with reasonable accuracy across readers and platforms (within 10% compared with reference benchmark values for most TMTVs) but that processing times can vary considerably depending on reader experience and the software platform. Our study showed that there is an urgent need to improve TMTV segmentation workflows in clinical practice, requiring closer collaboration between users and software vendors.</p>","PeriodicalId":22820,"journal":{"name":"The Journal of Nuclear Medicine","volume":"16 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Journal of Nuclear Medicine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2967/jnumed.124.269271","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Measuring total metabolic tumor volume (TMTV) on 18F-FDG PET/CT images in clinical practice requires a fast, reliable, and easy-to-perform multilesional segmentation workflow. We conducted a field test to derive total metabolic volumes using 5 representative baseline 18F-FDG PET/CT scans from patients with diffuse large B-cell lymphoma. The scans were transferred to 10 different sites or readers who used different commercially available software platforms to derive TMTV after a recently proposed benchmark workflow. Observed TMTVs were compared with reference values, and overall analysis times were reported. Our results show that TMTVs can be obtained with reasonable accuracy across readers and platforms (within 10% compared with reference benchmark values for most TMTVs) but that processing times can vary considerably depending on reader experience and the software platform. Our study showed that there is an urgent need to improve TMTV segmentation workflows in clinical practice, requiring closer collaboration between users and software vendors.
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