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Deriving global quantitative tumor response parameters from 18F-FDG PET-CT scans in patients with non-Hodgkin’s lymphoma

Sampedro, Frederic; Domenech, Anna; Escalera, Sergio; Carrió, Ignasi

Nuclear Medicine Communications: April 2015 - Volume 36 - Issue 4 - p 328–333
doi: 10.1097/MNM.0000000000000256

Objectives The aim of the study was to address the need for quantifying the global cancer time evolution magnitude from a pair of time-consecutive positron emission tomography-computed tomography (PET-CT) scans. In particular, we focus on the computation of indicators using image-processing techniques that seek to model non-Hodgkin’s lymphoma (NHL) progression or response severity.

Materials and methods A total of 89 pairs of time-consecutive PET-CT scans from NHL patients were stored in a nuclear medicine station for subsequent analysis. These were classified by a consensus of nuclear medicine physicians into progressions, partial responses, mixed responses, complete responses, and relapses. The cases of each group were ordered by magnitude following visual analysis. Thereafter, a set of quantitative indicators designed to model the cancer evolution magnitude within each group were computed using semiautomatic and automatic image-processing techniques. Performance evaluation of the proposed indicators was measured by a correlation analysis with the expert-based visual analysis.

Results The set of proposed indicators achieved Pearson’s correlation results in each group with respect to the expert-based visual analysis: 80.2% in progressions, 77.1% in partial response, 68.3% in mixed response, 88.5% in complete response, and 100% in relapse. In the progression and mixed response groups, the proposed indicators outperformed the common indicators used in clinical practice [changes in metabolic tumor volume, mean, maximum, peak standardized uptake value (SUVmean, SUVmax, SUVpeak), and total lesion glycolysis] by more than 40%.

Conclusion Computing global indicators of NHL response using PET-CT imaging techniques offers a strong correlation with the associated expert-based visual analysis, motivating the future incorporation of such quantitative and highly observer-independent indicators in oncological decision making or treatment response evaluation scenarios.

aDepartment of Radiology, Faculty of Medicine

bComputer Vision Center, Building O, Autonomous University of Barcelona

cDepartment of Nuclear Medicine, Hospital de Sant Pau

dDepartment of Applied Mathematics, Faculty of Mathematics, University of Barcelona, Barcelona, Spain

Correspondence to Frederic Sampedro, MSc, Faculty of Medicine, Autonomous University of Barcelona, 08193 Barcelona, Spain Tel: +34 699 805 231; e-mail:

Received September 5, 2014

Received in revised form October 19, 2014

Accepted November 26, 2014

Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved.