Literature DB >> 14516540

Tumor Treatment Response Based on Visual and Quantitative Changes in Global Tumor Glycolysis Using PET-FDG Imaging. The Visual Response Score and the Change in Total Lesion Glycolysis.

Steven M. Larson1, Yusuf Erdi, Timothy Akhurst, Madhu Mazumdar, Homer A. Macapinlac, Ronald D. Finn, Cecille Casilla, Melissa Fazzari, Neil Srivastava, Henry W.D. Yeung, John L. Humm, Jose Guillem, Robert Downey, Martin Karpeh, Alfred E. Cohen, Robert Ginsberg.   

Abstract

"Functional" tumor treatment response parameters have been developed to measure treatment induced biochemical changes in the entire tumor mass, using positron emission tomography (PET) and [F-18] fludeoxyglucose (FDG). These new parameters are intended to measure global changes in tumor glycolysis. The response parameters are determined by comparing the pre- and posttreatment PET-FDG images either visually from the change in image appearance in the region of the tumor, or quantitatively based on features of the calibrated digital PET image. The visually assessed parameters are expressed as a visual response score (VRS), or visual response index (VRI), as the estimated percent response of the tumor. Visual Response Score (VRS) is recorded on a 5 point response scale (0-4): 0: no response or progression; 1: 1-33%; 2: >33%-66%; 3: >66%-99%; and 4: >99%, estimated response, respectively. The quantitative changes are expressed as total lesion glycolysis TLG or as the change in TLG during treatment, also called deltaTLG or Larson-Ginsberg Index (LGI), expressed as percent response. The volume of the lesion is determined from the PET-FDG images by an adaptive thresholding technique. This response index is computed as, deltaTLG (LGI) = {[(SUV(ave))(1) * (Vol)(1) - (SUV(ave))(2) * (Vol)(2)]/[(SUV(ave))(1) * (Vol)(1)]} * 100. Where "1" and "2" denote the pre- and posttreatment PET-FDG, scans respectively. Pre- and posttreatment PET-FDG scans were performed on a group of 41 locally advanced lung (2), rectal (17), esophageal (16) and gastric (6) cancers. These patients were treated before surgery with neoadjuvant chemo-radiation. Four experienced PET readers determined individual VRS and VRI blinded to each other as well as to the clinical history. Consensus VRS was obtained based on a discussion. The interobserver variability captured by intraclass correlation coefficient was 89.7%. In addition, reader reliability was assessed for the categorized VRS using Kendall's coefficient of concordance for ordinal data and was found to be equal to 85% This provided assurance that these response parameters were highly reproducible. The correlation of deltaTLG with % change in SUV(ave) and % change in SUV(max), as widely used parameters of response, were 0.73 and 0.78 (P <.0001) respectively. The corresponding correlation of VRI were 0.63 and 0.64 (P <.0001) respectively. Both deltaTLG and VRI showed greater mean changes than SUV maximum or average (59.7% and 76% vs. 46.9% and 46.8%). We conclude that VRS and deltaTLG are substantially correlated with other response parameters and are highly reproducible. As global measures of metabolic response, VRS, VRI and deltaTLG (LGI) should provide complementary information to more commonly used PET response parameters like the metabolic rate of FDG (MRFDG), or the standardized uptake value (SUV), that are calculated as normalized per gram of tumor. These findings set the stage for validation studies of the VRS and deltaTLG as objective measures of clinical treatment response, through comparison to the appropriate gold standards of posttreatment histopathology, recurrence free survival, and disease specific survival in well characterized populations of patients with locally advanced cancers.

Entities:  

Year:  1999        PMID: 14516540     DOI: 10.1016/s1095-0397(99)00016-3

Source DB:  PubMed          Journal:  Clin Positron Imaging        ISSN: 1095-0397


  191 in total

1.  Is quantitation necessary for oncological PET studies? For.

Authors:  R Edward Coleman
Journal:  Eur J Nucl Med Mol Imaging       Date:  2001-11-14       Impact factor: 9.236

2.  Impact of partial-volume effect correction on the predictive and prognostic value of baseline 18F-FDG PET images in esophageal cancer.

Authors:  Mathieu Hatt; Adrien Le Pogam; Dimitris Visvikis; Olivier Pradier; Catherine Cheze Le Rest
Journal:  J Nucl Med       Date:  2012-01       Impact factor: 10.057

3.  Dose-response relationship in cyclophosphamide-treated B-cell lymphoma xenografts monitored with [18F]FDG PET.

Authors:  Lieselot Brepoels; Marijke De Saint-Hubert; Sigrid Stroobants; Gregor Verhoef; Jan Balzarini; Luc Mortelmans; Felix M Mottaghy
Journal:  Eur J Nucl Med Mol Imaging       Date:  2010-05-12       Impact factor: 9.236

4.  Comment on: "FDG PET and PET/CT: EANM procedure guidelines for tumour PET imaging, version 1.0".

Authors:  Babak Saboury; Drew A Torigian; Abass Alavi
Journal:  Eur J Nucl Med Mol Imaging       Date:  2010-05-26       Impact factor: 9.236

5.  Instrumentation factors affecting variance and bias of quantifying tracer uptake with PET/CT.

Authors:  R K Doot; J S Scheuermann; P E Christian; J S Karp; P E Kinahan
Journal:  Med Phys       Date:  2010-11       Impact factor: 4.071

6.  Prediction of tumour necrosis fractions using metabolic and volumetric 18F-FDG PET/CT indices, after one course and at the completion of neoadjuvant chemotherapy, in children and young adults with osteosarcoma.

Authors:  Hyung Jun Im; Tae Sung Kim; Seog-Yun Park; Hye Sook Min; June Hyuk Kim; Hyun Guy Kang; Seung Eun Park; Mi Mi Kwon; Jong Hyung Yoon; Hyeon Jin Park; Seok-ki Kim; Byung-Kiu Park
Journal:  Eur J Nucl Med Mol Imaging       Date:  2011-09-28       Impact factor: 9.236

7.  Independent prognostic value of whole-body metabolic tumor burden from FDG-PET in non-small cell lung cancer.

Authors:  Hao Zhang; Kristen Wroblewski; Daniel Appelbaum; Yonglin Pu
Journal:  Int J Comput Assist Radiol Surg       Date:  2012-05-30       Impact factor: 2.924

8.  Combined PET/CT-perfusion in patients with head and neck cancers.

Authors:  Patrick Veit-Haibach; Daniel Schmid; Klaus Strobel; Jan D Soyka; Niklaus G Schaefer; Stephan K Haerle; Gerhard Huber; Gabriele Studer; Burkhardt Seifert; Thomas F Hany
Journal:  Eur Radiol       Date:  2012-07-08       Impact factor: 5.315

9.  A Virtual Clinical Trial of FDG-PET Imaging of Breast Cancer: Effect of Variability on Response Assessment.

Authors:  Robert L Harrison; Brian F Elston; Robert K Doot; Thomas K Lewellen; David A Mankoff; Paul E Kinahan
Journal:  Transl Oncol       Date:  2014-02-01       Impact factor: 4.243

10.  Assessment of Total Lesion Glycolysis by 18F FDG PET/CT Significantly Improves Prognostic Value of GEP and ISS in Myeloma.

Authors:  James E McDonald; Marcus M Kessler; Michael W Gardner; Amy F Buros; James A Ntambi; Sarah Waheed; Frits van Rhee; Maurizio Zangari; Christoph J Heuck; Nathan Petty; Carolina Schinke; Sharmilan Thanendrarajan; Alan Mitchell; Antje Hoering; Bart Barlogie; Gareth J Morgan; Faith E Davies
Journal:  Clin Cancer Res       Date:  2016-10-03       Impact factor: 12.531

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.