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

© 2015 Wolters Kluwer Health, Inc. Objectives The aim of the study was to address the need for quantifying the global cancer time evolutionmagnitude froma pair of time-consecutive positron emission tomographycomputed 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 timeconsecutive 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 expertbased 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.