Ming Ni1,2, Shicun Wang2, Xin Liu2, Qin Shi2, Xingxing Zhu2, Yifan Zhang2, Qiang Xie2, Weifu Lv3,4. 1. Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China. 2. Department of Nuclear Medicine, Division of Life Sciences and Medicine, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, 230001, Anhui, China. 3. Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China. lwf09@163.com. 4. Department of Radiology, Division of Life Sciences and Medicine, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, 230001, Anhui, China. lwf09@163.com.
Abstract
PURPOSE: This study aimed to investigate the value of metabolic and heterogeneity parameters of 2-deoxy-2[18F]fluoro-D-glucose ([18F]FDG) positron emission tomography/computed tomography (PET/CT) in predicting epidermal growth factor receptor (EGFR) mutations in patients with lung adenocarcinoma (ADC). MATERIALS AND METHODS: A retrospective analysis was performed on 157 patients with lung ADC between September 2015 and June 2021, who had undergone both EGFR mutation testing and [18F]FDG PET/CT examination. Metabolic and heterogeneity parameters were measured and calculated, including maximum diameter (Dmax), maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and heterogeneity factor (HF). Relationships between PET/CT parameters and EGFR mutation status were evaluated and a multivariate logistic regression analysis was analyzed to establish a combined prediction model. RESULTS: 108 (68.8%) patients exhibited EGFR mutations. EGFR mutations were more likely to occur in females (51.9% vs. 48.1%, P = 0.007), non-smokers (83.3% vs. 16.7%, P < 0.001) and right lobes (55.6% vs. 44.4%, P = 0.017). High Dmax, MTV and HF and low SUVmean were significantly correlated with EGFR mutations, and the areas under the ROC curve (AUCs) measuring 0.647, 0.701, 0.757, and 0.661, respectively. Multivariate logistic regression analysis suggested that non-smokers (OR = 0.30, P = 0.034), low SUVmean (≤ 7.75, OR = 0.63, P < 0.001) and high HF (≥ 4.21, OR = 1.80, P = 0.027) were independent predictors of EGFR mutations. The AUC of the combined prediction model measured up to 0.863, significantly higher than that of a single parameter. CONCLUSIONS: EGFR mutant in lung ADC patients showed more intratumor heterogeneity (HF) than EGFR wild type, which was combined clinical feature (non-smokers), and metabolic parameter (SUVmean) may be helpful in predicting EGFR mutation status, thus playing a guiding role in EGFR-tyrosine kinase inhibitors (EGFR-TKIs) targeted therapies.
PURPOSE: This study aimed to investigate the value of metabolic and heterogeneity parameters of 2-deoxy-2[18F]fluoro-D-glucose ([18F]FDG) positron emission tomography/computed tomography (PET/CT) in predicting epidermal growth factor receptor (EGFR) mutations in patients with lung adenocarcinoma (ADC). MATERIALS AND METHODS: A retrospective analysis was performed on 157 patients with lung ADC between September 2015 and June 2021, who had undergone both EGFR mutation testing and [18F]FDG PET/CT examination. Metabolic and heterogeneity parameters were measured and calculated, including maximum diameter (Dmax), maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and heterogeneity factor (HF). Relationships between PET/CT parameters and EGFR mutation status were evaluated and a multivariate logistic regression analysis was analyzed to establish a combined prediction model. RESULTS: 108 (68.8%) patients exhibited EGFR mutations. EGFR mutations were more likely to occur in females (51.9% vs. 48.1%, P = 0.007), non-smokers (83.3% vs. 16.7%, P < 0.001) and right lobes (55.6% vs. 44.4%, P = 0.017). High Dmax, MTV and HF and low SUVmean were significantly correlated with EGFR mutations, and the areas under the ROC curve (AUCs) measuring 0.647, 0.701, 0.757, and 0.661, respectively. Multivariate logistic regression analysis suggested that non-smokers (OR = 0.30, P = 0.034), low SUVmean (≤ 7.75, OR = 0.63, P < 0.001) and high HF (≥ 4.21, OR = 1.80, P = 0.027) were independent predictors of EGFR mutations. The AUC of the combined prediction model measured up to 0.863, significantly higher than that of a single parameter. CONCLUSIONS: EGFR mutant in lung ADC patients showed more intratumor heterogeneity (HF) than EGFR wild type, which was combined clinical feature (non-smokers), and metabolic parameter (SUVmean) may be helpful in predicting EGFR mutation status, thus playing a guiding role in EGFR-tyrosine kinase inhibitors (EGFR-TKIs) targeted therapies.
Authors: David S Ettinger; Douglas E Wood; Dara L Aisner; Wallace Akerley; Jessica R Bauman; Ankit Bharat; Debora S Bruno; Joe Y Chang; Lucian R Chirieac; Thomas A D'Amico; Thomas J Dilling; Jonathan Dowell; Scott Gettinger; Matthew A Gubens; Aparna Hegde; Mark Hennon; Rudy P Lackner; Michael Lanuti; Ticiana A Leal; Jules Lin; Billy W Loo; Christine M Lovly; Renato G Martins; Erminia Massarelli; Daniel Morgensztern; Thomas Ng; Gregory A Otterson; Sandip P Patel; Gregory J Riely; Steven E Schild; Theresa A Shapiro; Aditi P Singh; James Stevenson; Alda Tam; Jane Yanagawa; Stephen C Yang; Kristina M Gregory; Miranda Hughes Journal: J Natl Compr Canc Netw Date: 2021-03-02 Impact factor: 11.908
Authors: David Chardin; Marie Paquet; Renaud Schiappa; Jacques Darcourt; Caroline Bailleux; Michel Poudenx; Aurélie Sciazza; Marius Ilie; Jonathan Benzaquen; Nicolas Martin; Josiane Otto; Olivier Humbert Journal: J Immunother Cancer Date: 2020-07 Impact factor: 13.751