Literature DB >> 12483409

Comparison of SUV and Patlak slope for monitoring of cancer therapy using serial PET scans.

Nanette M T Freedman1, Senthil K Sundaram, Karen Kurdziel, Jorge A Carrasquillo, Millie Whatley, Joann M Carson, David Sellers, Steven K Libutti, James C Yang, Stephen L Bacharach.   

Abstract

The standardized uptake value (SUV) and the slope of the Patlak plot ( K) have both been proposed as indices to monitor the progress of disease during cancer therapy. Although a good correlation has been reported between SUV and K, they are not equivalent, and may not be equally affected by metabolic changes occurring during disease progression or therapy. We wished to compare changes in tumor SUV with changes in K during serial positron emission tomography (PET) scans for monitoring therapy. Thirteen patients enrolled in a protocol to treat renal cell carcinoma metastases were studied. Serial dynamic fluorodeoxyglucose (FDG) PET scans and computed tomography (CT) and magnetic resonance (MR) scans were performed once prior to treatment, once at 36+/-2 days after the start of treatment, and (in 7/13 subjects, 16/27 lesions) a third time at 92+/-9 days after the start of treatment. This resulted in a total of 33 scans, and 70 tumor Patlak and SUV values (one value for each lesion at each time point). SUV and K were measured over one to four predefined tumors/patient at each time point. The input function was obtained from regions of interest over the heart, combined, if necessary, with late blood samples. Over all tumors and scans, SUV and K correlated well ( r=0.97, P<0.0001). However, change in SUV with treatment over all tumor scan pairs was much less well correlated with the corresponding change in K ( r=0.73, P<0.0001). The absolute difference in % change was outside the 95% confidence limits expected from previous variability studies in 6 of 43 pairs of tumor scans, and greater than 50% in 2 of 43 tumor scan pairs. In four of the six cases, the two indices predicted opposing therapeutic outcomes. Similar results were obtained for SUV normalized by body weight or body surface area and for SUVs using mean or maximum count. Changes in CT and MR tumor cross-product dimensions correlated poorly with each other ( r=0.47, P=NS), and so could not be used to determine the "correct" PET index. Absolute values of SUV and K correlated well over the patient population. However, when monitoring individual patient therapy serially, large differences in the % changes in the two indices were occasionally found, sometimes sufficient to produce opposing conclusions regarding the progression of disease.

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Year:  2002        PMID: 12483409     DOI: 10.1007/s00259-002-0981-4

Source DB:  PubMed          Journal:  Eur J Nucl Med Mol Imaging        ISSN: 1619-7070            Impact factor:   9.236


  36 in total

1.  A virtual clinical trial comparing static versus dynamic PET imaging in measuring response to breast cancer therapy.

Authors:  Kristen A Wangerin; Mark Muzi; Lanell M Peterson; Hannah M Linden; Alena Novakova; David A Mankoff; Paul E Kinahan
Journal:  Phys Med Biol       Date:  2017-02-13       Impact factor: 3.609

2.  Determination of the unmetabolised (18)F-FDG fraction by using an extension of simplified kinetic analysis method: clinical evaluation in paragangliomas.

Authors:  Dominique Barbolosi; Sebastien Hapdey; Stephanie Battini; Christian Faivre; Julien Mancini; Karel Pacak; Bardia Farman-Ara; David Taïeb
Journal:  Med Biol Eng Comput       Date:  2015-06-05       Impact factor: 2.602

Review 3.  Dynamic whole-body PET imaging: principles, potentials and applications.

Authors:  Arman Rahmim; Martin A Lodge; Nicolas A Karakatsanis; Vladimir Y Panin; Yun Zhou; Alan McMillan; Steve Cho; Habib Zaidi; Michael E Casey; Richard L Wahl
Journal:  Eur J Nucl Med Mol Imaging       Date:  2018-09-29       Impact factor: 9.236

4.  Impact of third-line treatment with irinotecan plus cetuximab on non-tumor standardized uptake values in patients with metastatic colorectal cancer.

Authors:  Kim Francis Andersen; Kristin Skougaard; Anne Lerberg Nielsen; Helle Westergren Hendel
Journal:  Oncol Lett       Date:  2012-04-18       Impact factor: 2.967

5.  Population Pharmacokinetics of Tracers: A New Tool for Medical Imaging?

Authors:  Peggy Gandia; Cyril Jaudet; Etienne Chatelut; Didier Concordet
Journal:  Clin Pharmacokinet       Date:  2017-02       Impact factor: 6.447

6.  Design considerations for using PET as a response measure in single site and multicenter clinical trials.

Authors:  Robert K Doot; Brenda F Kurland; Paul E Kinahan; David A Mankoff
Journal:  Acad Radiol       Date:  2011-11-21       Impact factor: 3.173

Review 7.  (18)F-FDG PET/CT quantification in head and neck squamous cell cancer: principles, technical issues and clinical applications.

Authors:  Gianpiero Manca; Eleonora Vanzi; Domenico Rubello; Francesco Giammarile; Gaia Grassetto; Ka Kit Wong; Alan C Perkins; Patrick M Colletti; Duccio Volterrani
Journal:  Eur J Nucl Med Mol Imaging       Date:  2016-01-19       Impact factor: 9.236

8.  How Long of a Dynamic 3'-Deoxy-3'-[18F]fluorothymidine ([18F]FLT) PET Acquisition Is Needed for Robust Kinetic Analysis in Breast Cancer?

Authors:  Jun Zhang; Xiaoli Liu; Michelle I Knopp; Bhuvaneswari Ramaswamy; Michael V Knopp
Journal:  Mol Imaging Biol       Date:  2019-04       Impact factor: 3.488

9.  PET/CT Assessment of Response to Therapy: Tumor Change Measurement, Truth Data, and Error.

Authors:  Paul E Kinahan; Robert K Doot; Michelle Wanner-Roybal; Luc M Bidaut; Samuel G Armato; Charles R Meyer; Geoffrey McLennan
Journal:  Transl Oncol       Date:  2009-12       Impact factor: 4.243

10.  Quantitative accuracy of PET/CT for image-based kinetic analysis.

Authors:  Youngho Seo; Boon-Keng Teo; Mohiuddin Hadi; Carole Schreck; Stephen L Bacharach; Bruce H Hasegawa
Journal:  Med Phys       Date:  2008-07       Impact factor: 4.071

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