Visual Versus Quantitative Assessment of Intratumor ¹⁸F-FDG PET Uptake Heterogeneity: Prognostic Value in Non–Small Cell Lung Cancer

Abstract

The goal of this study was to compare visual assessment of intratumor 18 F-FDG PET uptake distribution with a textural-features (TF) automated quantification and to establish their respective prognostic value in non-small cell lung cancer (NSCLC). Methods: The study retrospectively included 102 consecutive patients. Only primary tumors were considered. Intratumor heterogeneity was visually scored (3-level scale [H visu ]) by 2 nuclear medicine physicians. Tumor volumes were automatically delineated, and heterogeneity was quantified with TF. Mean and maximum standardized uptake value were also included. Visual interobserver agreement and correlations with quantitative assessment were evaluated using the κ test and Spearman rank (ρ) coefficient, respectively. Association with overall survival and recurrencefree survival was investigated using the Kaplan-Meier method and Cox regression models. Results: Moderate correlations (0.4 , ρ , 0.6) between TF parameters and H visu were observed. Interobserver agreement for H visu was moderate (κ 5 0.64, discrepancies in 27% of the cases). High standardized uptake value, large metabolic volumes, and high heterogeneity according to TF were associated with poorer overall survival and recurrence-free survival and remained an independent prognostic factor of overall survival with respect to clinical variables. Conclusion: Quantification of 18 F-FDG uptake heterogeneity in NSCLC through TF was correlated with visual assessment by experts. However, TF also constitutes an objective heterogeneity quantification, with reduced interobserver variability, and independent prognostic value potentially useful for patient stratification and management. PET/ CT imaging with 18 F-FDG is today a well-established tool for diagnostic oncology applications (1). Its exploitation for tumor delineation in the planning of radiotherapy treatment (2) or monitoring of response to therapy (3,4) is increasing. For non-small cell lung cancer (NSCLC), 18 F-FDG PET/CT image quantification has been shown to provide prognostic information. PET image-derived features, including metabolically active tumor volume (MATV), mean standardized uptake value (SUV mean ), and total lesion glycolysis (TLG, defined as MATV · SUV mean ), have been shown to provide an accurate assessment of tumor burden with potentially higher prognostic value than standard maximum SUV (SUV max ), for both surgical and nonsurgical patients (5-9).Intratumor 18 F-FDG uptake heterogeneity has been associated with treatment failure (10), and its quantification has recently generated interest (11-14), including in lung cancer (15). Several methodologies have been proposed to assess intratumor heterogeneity, including visual evaluation (16), SUV coefficient of variation (SUV COV ) (17), area under the curve of the cumulative histogram (CH AUC ) (18), and textural-features (TF) analysis (17,19).TF analysis can generate many features quantifying heterogeneity within a delineated MATV. Recent studies have identified a few of these features that are ...

The Impact of Optimal Respiratory Gating and Image Noise on Evaluation of Intratumor Heterogeneity on 18F-FDG PET Imaging of Lung Cancer

“…Identification and validation of the image parameters associated with patient outcome and OS require a multicenter prospective study. Although these textural parameters were found to be associated with OS in a previous study (8), such was not our finding in the current patient cohort. This difference could be due to our relatively large number of patients with metastatic disease, which was an exclusion criterion in the previous study (8).…”

“…Although these textural parameters were found to be associated with OS in a previous study (8), such was not our finding in the current patient cohort. This difference could be due to our relatively large number of patients with metastatic disease, which was an exclusion criterion in the previous study (8). In patients with metastatic disease, the characteristics of the primary tumor might have limited predictive value regarding OS, whereas indices containing more information about disease load (such as summed TLG) might more accurately predict OS.…”

“…Particularly, there has been increasing recognition of the role of medical imaging in identifying specific tumor phenotypes (4), predicting treatment resistance (5-7), and projecting overall survival (OS) (8). This view fits our current knowledge of cancer, in which malignant lesions consist of heterogeneous cell populations with distinct molecular and microenvironmental differences (9).…”

“…The first two describe local heterogeneity (variations in intensity between each voxel and its immediate neighbors averaged over the entire volume), and the third and fourth describe regional heterogeneity (at the level of groups of voxels and areas of various sizes and intensities). These parameters have been found useful for predicting prognosis in patients with non-small cell lung cancer (8). The voxel intensities were grouped using 64 levels of gray, and the local-heterogeneity parameters were computed over 13 directions (18).…”

Impact of Image Reconstruction Settings on Texture Features in 18F-FDG PET

“…Subsequently, there are increasing numbers of publications related to the application of TFs in PET tumor imaging, mostly with 18 F-FDG (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21). These previous studies mainly focused on the predictive and prognostic value (5-10,13-25) of TF and also the potential use in radiotherapy planning (20,21).…”