Lixia Zhang1, Caiyun Xu1, Xiaohui Zhang2,3,4, Jing Wang5,6,7, Han Jiang8,9,10, Jinyan Chen1, Hong Zhang11,12,13. 1. Department of Nuclear Medicine, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, 310006, Zhejiang, China. 2. Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China. zhanghui4127@zju.edu.cn. 3. Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, 310009, Zhejiang, China. zhanghui4127@zju.edu.cn. 4. Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, 310009, Zhejiang, China. zhanghui4127@zju.edu.cn. 5. Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China. wangjing5678@zju.edu.cn. 6. Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, 310009, Zhejiang, China. wangjing5678@zju.edu.cn. 7. Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, 310009, Zhejiang, China. wangjing5678@zju.edu.cn. 8. Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China. 9. Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, 310009, Zhejiang, China. 10. Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, 310009, Zhejiang, China. 11. Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China. hzhang21@zju.edu.cn. 12. Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, 310009, Zhejiang, China. hzhang21@zju.edu.cn. 13. Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, 310009, Zhejiang, China. hzhang21@zju.edu.cn.
Abstract
OBJECTIVES: To develop a novel analytical approach based on 18F-fluorodeoxyglucose ([18F]FDG) positron emission tomography (PET) metabolic parameters, serum inflammatory markers, and clinical variables to improve the outcome prediction in NSCLC. METHODS: A total of 190 newly diagnosed NSCLC patients who underwent pretreatment [18F]FDG PET/CT were retrospectively enrolled and divided into a training cohort (n = 127) and a test cohort (n = 63). Cox regression analysis was used to investigate the predictive values of PET metabolic parameters, inflammation markers, and clinical variables for progression-free survival (PFS) and overall survival (OS). Based on the results of multivariate analysis, PET-based, clinical, and combined models were constructed. The predictive performance of different models was evaluated using time-dependent ROC curve analysis, Harrell concordance index (C-index), calibration curve, and decision curve analysis. RESULTS: The combined models incorporating SULmax, MTV, NLR, and ECOG PS demonstrated significant prognostic superiority over PET-based models, clinical models, and TNM stage in terms of both PFS (C-index: 0.813 vs. 0.786 vs. 0.776 vs. 0.678, respectively) and OS (C-index: 0.856 vs. 0.792 vs. 0.781 vs. 0.674, respectively) in the training cohort. Similar results were observed in the test cohort for PFS (C-index: 0.808 vs. 0.764 vs. 0.748 vs. 0.679, respectively) and OS (C-index: 0.836 vs. 0.785 vs. 0.726 vs. 0.660, respectively) prediction. The combined model calibrated well in two cohorts. Decision curve analysis supported the clinical utility of the combined model. CONCLUSIONS: We reported a novel analytical approach combining PET metabolic information with inflammatory biomarker and clinical characteristics, which could significantly improve outcome prediction in newly diagnosed NSCLC. KEY POINTS: • The nomogram incorporating SULmax, MTV, NLR, and ECOG PS outperformed the TNM stage for outcome prediction in patients with newly diagnosed NSCLC. • The established nomogram could provide refined prognostic stratification.
OBJECTIVES: To develop a novel analytical approach based on 18F-fluorodeoxyglucose ([18F]FDG) positron emission tomography (PET) metabolic parameters, serum inflammatory markers, and clinical variables to improve the outcome prediction in NSCLC. METHODS: A total of 190 newly diagnosed NSCLC patients who underwent pretreatment [18F]FDG PET/CT were retrospectively enrolled and divided into a training cohort (n = 127) and a test cohort (n = 63). Cox regression analysis was used to investigate the predictive values of PET metabolic parameters, inflammation markers, and clinical variables for progression-free survival (PFS) and overall survival (OS). Based on the results of multivariate analysis, PET-based, clinical, and combined models were constructed. The predictive performance of different models was evaluated using time-dependent ROC curve analysis, Harrell concordance index (C-index), calibration curve, and decision curve analysis. RESULTS: The combined models incorporating SULmax, MTV, NLR, and ECOG PS demonstrated significant prognostic superiority over PET-based models, clinical models, and TNM stage in terms of both PFS (C-index: 0.813 vs. 0.786 vs. 0.776 vs. 0.678, respectively) and OS (C-index: 0.856 vs. 0.792 vs. 0.781 vs. 0.674, respectively) in the training cohort. Similar results were observed in the test cohort for PFS (C-index: 0.808 vs. 0.764 vs. 0.748 vs. 0.679, respectively) and OS (C-index: 0.836 vs. 0.785 vs. 0.726 vs. 0.660, respectively) prediction. The combined model calibrated well in two cohorts. Decision curve analysis supported the clinical utility of the combined model. CONCLUSIONS: We reported a novel analytical approach combining PET metabolic information with inflammatory biomarker and clinical characteristics, which could significantly improve outcome prediction in newly diagnosed NSCLC. KEY POINTS: • The nomogram incorporating SULmax, MTV, NLR, and ECOG PS outperformed the TNM stage for outcome prediction in patients with newly diagnosed NSCLC. • The established nomogram could provide refined prognostic stratification.
Authors: Seong Yong Park; Arthur Cho; Woo Sik Yu; Chang Young Lee; Jin Gu Lee; Dae Joon Kim; Kyung Young Chung Journal: J Nucl Med Date: 2014-12-18 Impact factor: 10.057
Authors: Willem Grootjans; Lioe-Fee de Geus-Oei; Esther G C Troost; Eric P Visser; Wim J G Oyen; Johan Bussink Journal: Nat Rev Clin Oncol Date: 2015-04-28 Impact factor: 66.675
Authors: Kari Chansky; Jean-Paul Sculier; John J Crowley; Dori Giroux; Jan Van Meerbeeck; Peter Goldstraw Journal: J Thorac Oncol Date: 2009-07 Impact factor: 15.609
Authors: Peter Goldstraw; Kari Chansky; John Crowley; Ramon Rami-Porta; Hisao Asamura; Wilfried E E Eberhardt; Andrew G Nicholson; Patti Groome; Alan Mitchell; Vanessa Bolejack Journal: J Thorac Oncol Date: 2016-01 Impact factor: 15.609
Authors: Jonathan Frederik Carlsen; Tina Nørgaard Munch; Adam Espe Hansen; Steen Gregers Hasselbalch; Alexander Malcolm Rykkje Journal: Neuroradiology Date: 2022-07-24 Impact factor: 2.995