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Role of 18F-FDG PET/CT and sarcopenia in untreated non-small cell lung cancer with advanced stage

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Abstract

Background

Sarcopenia is essential in managing advanced stage (III–IV) non-small cell lung cancer (NSCLC) but is laborious to diagnose using currently available method. This study aimed to establish a simple approach to predict sarcopenia using 18F-FDG PET/CT parameters and clinical characteristics and determine their roles in prognostication in advanced stage NSCLC.

Methods

Untreated 202 NSCLC patients with stage III–IV were retrospectively reviewed. Sarcopenia was defined using the skeletal muscle index (SMI) measured at the third lumbar vertebra (L3). 18F-FDG PET/CT metabolic parameters of maximum standard uptake value, metabolic tumor volume, and total lesion glycolysis of the primary tumor (SUVmax_T, MTV_T, and TLG_T) and of whole-body lesions (MTV_WB and TLG_WB) were measured. Besides, SUVmax of the psoas major muscle (SUVmax_Muscle) was measured at the L3 level. The diagnostic endpoint was the probability of sarcopenia, and the survival endpoints included progression-free survival (PFS) and overall survival (OS).

Results

Among the enrolled 202 patients, 82 (40.6%) were diagnosed with sarcopenia. Higher age, male, lower BMI, and lower SUVmax_Muscle were correlated with a higher incidence of sarcopenia (P < 0.05), while age, sex, BMI, and SUVmax_Muscle were independently predictive of sarcopenia, and thus were utilized to construct a nomogram model. Multivariate Cox regression analysis revealed that sarcopenia score derived from the nomogram model, sarcopenia, stage, and TLG_WB were independently predictive of both PFS and OS.

Conclusion

The incidence of sarcopenia increased with declining SUVmax_Muscle in advanced stage NSCLC. Our model using age, sex, BMI, and SUVmax_Muscle might be substituted for the complicated measurement of SMI. After adjustment by stage and TLG_WB, both sarcopenia score and sarcopenia were found to be independently predictive of PFS and OS.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work was supported by the fund from the National Natural Science Foundation of China (81971645), Guangdong Provincial People's Hospital (KY0120211130), and Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application (No. 2022B1212010011).

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Author notes

  1. Hui Yuan and Xiaoyue Tan equally contributed to this work.

Authors and Affiliations

  1. PET Center, Department of Nuclear Medicine, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China

    Hui Yuan, Xiaoyue Tan, Xiaolin Sun, Li He, Dongjiang Li & Lei Jiang

  2. Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China

    Lei Jiang

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  1. Hui Yuan
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11604_2022_1369_MOESM1_ESM.tif

Supplementary file1 Figure S1. The receiver operating characteristic (ROC) curve plotting the diagnostic performance of sarcopenia score to predict sarcopenia, and the area under the curve (AUC) was 0.851 (TIF 4028 KB)

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Yuan, H., Tan, X., Sun, X. et al. Role of 18F-FDG PET/CT and sarcopenia in untreated non-small cell lung cancer with advanced stage. Jpn J Radiol 41, 521–530 (2023). https://doi.org/10.1007/s11604-022-01369-9

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