Heterogeneity in [¹⁸F]Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography of Non–Small Cell Lung Carcinoma and Its Relationship to Metabolic Parameters and Pathologic Staging

Abstract

To investigate the relationships between tumor heterogeneity, assessed by texture analysis of [18F]fluorodeoxyglucose-positron emission tomography (FDG-PET) images, metabolic parameters, and pathologic staging in patients with non-small cell lung carcinoma (NSCLC). A retrospective analysis of 38 patients with histologically confirmed NSCLC who underwent staging FDG-PET/computed tomography was performed. Tumor images were segmented using a standardized uptake value (SUV) cutoff of 2.5. Five textural features, related to the heterogeneity of gray-level distribution, were computed (energy, entropy, contrast, homogeneity, and correlation). Additionally, metabolic parameters such as SUVmax, SUVmean, metabolic tumor volume (MTV), and total lesion glycolysis (TLG), as well as pathologic staging, histologic subtype, and tumor diameter, were obtained. Finally, a correlation analysis was carried out. Of 38 tumors, 63.2% were epidermoid and 36.8% were adenocarcinomas. The mean ± standard deviation values of MTV and TLG were 30.47 ± 25.17 mL and 197.81 ± 251.11 g, respectively. There was a positive relationship of all metabolic parameters (SUVmax, SUVmean, MTV, and TLG) with entropy, correlation, and homogeneity and a negative relationship with energy and contrast. The T component of the pathologic TNM staging (pT) was similarly correlated with these textural parameters. Textural features associated with tumor heterogeneity were shown to be related to global metabolic parameters and pathologic staging.

Repeatability of 18F‐FDG PET radiomic features: A phantom study to explore sensitivity to image reconstruction settings, noise, and delineation method

“…This is in line with previous studies showing that, for example, coarseness contains valuable information about survival for NSCLC patients or response to therapy for esophageal cancer patients . While sum and difference entropy (GLCM) showed to have prognostic value for NSCLC tumors …”

“…36,37 While sum and difference entropy (GLCM) showed to have prognostic value for NSCLC tumors. [38][39][40] A drawback of our study is that only phantom data were included, although we attempted to make the study as clinically relevant as possible by using 3D printed phantom inserts reflecting heterogeneous tumor uptake pattern. It should be noted that our study was designed to explore the technical performance of radiomic features under controlled experimental conditions, thereby avoiding biological uncertainties or variations in imaging procedures.…”

Application of texture analysis based on apparent diffusion coefficient maps in discriminating different stages of rectal cancer

“…Similar results have been previously reported wherein [18F]fluorodeoxyglucose-positron emission tomography-based texture features, including skewness, entropy, and contrast, correlated with the overall staging of nonsmall cell lung carcinoma and cervical cancer. 26,32 In our study, entropy and contrast showed a better correlation with overall staging as compared to that afforded by the traditional mean ADC value. This is consistent with a recent report by Vignati et al, in which the authors analyzed the correlation between ADC texture features and grading of prostate cancer.…”

“…; in which Range represents the number of discrete values (16,32,64,128), I represents the intensity of the original image, and H refers to the set of pixels within the delineated area. No statistically significant difference was found between the four range numbers (16,32,64,128), and the number of 32 was chosen in this study.…”

Stage III Non–Small Cell Lung Cancer: Prognostic Value of FDG PET Quantitative Imaging Features Combined with Clinical Prognostic Factors

“…Recently, studies have suggested that the examination of spatial heterogeneity with computed tomography (CT) and the use of SUVs within solid tumors (particularly NSCLC) may provide prognostic information (8)(9)(10)(11)(12). Although these studies have generated compelling early evidence that heterogeneous tumors may lead to inferior outcomes, they have lacked adjustment for known prognostic factors and the use of proper validation techniques.…”