Metabolic phenotypes, serum tumor markers, and histopathological subtypes in predicting bone metastasis: analysis of 695 patients with lung cancer in China

Quantitative Imaging in Medicine and Surgery

article

Lijuan Ma¹  Weiqi Mei²  Jingfeng Zhang¹  Jianjun Zheng¹  Maoqing Jiang¹  Ping Chen³  Xiaohui Zhang¹  Xiuyu Guo¹  Qiaoling Gao¹ 

[1] Department of Radiology and PET/CT Center, Hwa Mei Hospital , University of Chinese Academy of Sciences;Department of Nuclear Medicine, Hwa Mei Hospital , University of Chinese Academy of Sciences;Department of Nephrology, Hwa Mei Hospital , University of Chinese Academy of Sciences

关键词: 18F-2-fluoro-2-deoxyglucose (FDG);    histopathological subtypes;    lung cancer;    metabolic phenotype;    serum tumor markers;   

DOI  :  10.21037/qims-22-741

学科分类：外科医学

来源: AME Publications

【 摘 要 】

Background: Patients with lung cancer who develop bone metastasis (BM) generally have an adverse prognosis. Although several clinical models have been used to predict BM in patients with lung cancer, the results are unsatisfactory. In this retrospective study, we investigated the role of 18F-2-fluoro-2-deoxyglucose (FDG) metabolic activity, serum tumor markers, and histopathological subtypes in predicting BM in patients with lung cancer. Methods: This study included 695 consecutive patients with lung cancer who underwent 18F-FDG positron emission tomography/computed tomography (PET/CT) and in whom serum tumor markers were detected prior to treatment. The maximum standardized uptake value of primary tumors (pSUVmax), metastatic lymph nodes (nSUVmax) and distant metastases (mSUVmax), 8 serum tumor markers [carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), squamous cell carcinoma-related antigen (SCCA), cytokeratin 19 fragment (CYFRA21-1), carbohydrate antigen (CA) 125, CA50, CA72-4, and ferritin], and histopathological subtypes were compared between patients with and without BM. Receiver operating characteristic (ROC) curve and multiple logistic regression analyses were performed to identify predictors of BM in patients with lung cancer. Results: BM was identified in 133 (19.1%) patients and not in 562 (80.9%). Patients with BM had significantly higher pSUVmax, nSUVmax, and mSUVmax than did those without BM. High concentrations of 6 serum tumor markers (i.e., CEA, ferritin, NSE, CA50, CA125, and CYFRA21-1) were significantly associated with BM. There were significant differences in the proportion of histopathological subtypes between patients with and without BM (χ2=32.35; P<0.001). The area under ROC-derived curve based on metabolic parameters was 0.737 (95% CI: 0.644–0.829) and 0.884 (95% CI: 0.825–0.943) when combined with the 6 serum tumor markers and histopathological subtypes, respectively. Conclusions: High pSUVmax, nSUVmax, and mSUVmax favor the presence of BM in patients with lung cancer, and serum tumor markers and histopathological subtypes are important factors for predicting BM in these patients.

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