Research Papers:

Development and validation of a prognostic index to predict pulmonary metastasis of giant cell tumor of bone

Bo Wang, Wei Chen _, Xianbiao Xie, Jian Tu, Gang Huang, Changye Zou, Junqiang Yin, Lili Wen and Jingnan Shen

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Oncotarget. 2017; 8:108054-108063. https://doi.org/10.18632/oncotarget.22478

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Bo Wang1,*, Wei Chen1,*, Xianbiao Xie1, Jian Tu1, Gang Huang1, Changye Zou1, Junqiang Yin1, Lili Wen2 and Jingnan Shen1

1Musculoskeletal Oncology Center, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China

2Department of Anesthesiology, State Key Laboratory of Oncology in South China, Sun Yat-Sen University Cancer Center, Guangzhou, China

*These authors have contributed equally to this work

Correspondence to:

Jingnan Shen, email: [email protected]

Lili Wen, email: [email protected]

Keywords: giant cell tumor; pulmonary metastasis; risk factor; nomogram; predictive model

Received: July 26, 2017    Accepted: August 29, 2017    Published: November 08, 2017


Purpose: Giant cell tumor of bone (GCTB) is an intermittent tumor with a low probability of pulmonary metastasis. Our aim was to investigate the risk factors and establish a nomogram predictive model for GCTB pulmonary metastasis.

Methods: We retrospectively evaluated GCTB patients at our center from 1991 to 2014. The cohort was randomized into training and validation sets. Univariate and multivariate analyses were used to evaluate the risk factors of pulmonary metastasis. A nomogram was established. Internal validation was achieved based on ROC curve and C-index values in the validation set. Decision curve analysis was performed to assess the clinical performance of the nomogram.

Results: 417 patients were studied, including benign and malignant GCTBs. The average follow up was 79 months. Pulmonary metastases were observed in 27 cases. Four independent risk factors were identified: malignancy, tumor bearing time, times of recurrence and tumor size. A nomogram was developed to predict pulmonary metastasis with C-index values of 0.857 and 0.785 in the training and validation groups. In the decision curve analysis, patients could benefit from the nomogram, which differentiates patients at high risk for pulmonary metastasis and avoids unnecessary examination. According to the nomogram, patients with final risks of more than 0.06 should be scheduled for further chest scans.

Conclusion: Malignancy, tumor bearing time, times of recurrence and tumor size were independent risk factors for pulmonary metastasis in GCTB patients. The nomogram can accurately predict the risk of pulmonary metastasis and help doctors to make clinical decisions for further chest examinations.

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