Effect evaluation of denosumab combined with curettage and bone cement reconstruction in the treatment of recurrent giant cell tumor of bone around the knee joint

D.-K. Duan, G.-C. Zhang, B.-J. Sun, T.-X. Ma, M. Zhao

Department of Musculoskeletal Tumor, The Third Hospital of Hebei Medical University, Shijiazhuang, China Shijiazhuang, China. hbsygzlhy@163.com

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• Oncology
• Orthopedics

OBJECTIVE: Giant cell tumor of bone (GCTB) is a common primary bone tumor with latent malignant tendency. GCTB is prone to occur around the knee joint, and surgery is the major treatment method. There are relatively few reports on denosumab in the treatment of recurrent GCTB around the knee joint and postoperative function evaluation of patients. This research aimed to explore the appropriate surgical options for the treatment of recurrent GCTB around the knee joint.

PATIENTS AND METHODS: 19 patients with recurrent GCTB around the knee joint, who were admitted to Hospital for 3 months following denosumab treatment from January 2016 to December 2019, were included as the research subjects. The prognosis was compared between patients treated with curettage combined with polymethylmethacrylate (PMMA) and those with extensive-resection replacement of tumor prosthesis (RTP). A deep learning model of Inception-v3 combined with a Faster region-based convolutional neural network (Faster-RCNN) was constructed to classify and identify X-ray images of patients. The Musculoskeletal Tumor Society (MSTS) score, short form-36 (SF-36) score, recurrence, and the rate of complications were also analyzed during the follow-up period.

RESULTS: The results showed that the Inception-v3 model trained on the low-rank sparse loss function was obviously the best for X-ray image classification, and the classification and identification effect of the Faster-RCNN model was significantly better than that of the convolutional neural network (CNN), U-Net, and Fast region-based convolutional neural network (Fast-RCNN) models. During the follow-up period, the MSTS score in the PMMA group was significantly higher than that in the RTP group (p<0.05), while there was no significant difference in the SF-36 score, recurrence, and the rate of complications (p>0.05).

CONCLUSIONS: The deep learning model could improve the classification and identification of the lesion location in the X-ray images of GCTB patients. Denosumab was an effective adjuvant for recurrent GCTB, and widely extensive-resection RTP could reduce the risk of local recurrence after denosumab treatment for recurrent GCTB.

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