Denosumab regimens in the treatment of giant cell tumor of bone: A systematic review with meta-analysis

Table 1 Database search strategy

Databases	Strategy
MEDLINE via PubMed	1 "Giant Cell Tumor of Bone"[Mesh] OR osteoclastoma; 2 "Denosumab"[Mesh] OR (Xgeva) OR (AMG 162) OR (Prolia); 3 1 AND 2
SciELO	1 "Giant Cell Tumor of Bone" OR "Tumor Óseo de Células Gigantes" OR "Tumor de Células Gigantes do Osso" OR Osteoblastoma; 2 Denosumab OR Xgeva OR AMG 162 OR Prolia OR Denosumabe; 3 1 AND 2
EMBASE	1 'osteoclastoma'/exp OR (giant AND cell AND tumor AND of AND bone) OR (giant AND cell AND tumor, AND bone) OR (giant AND cell AND tumour AND of AND bone) OR (giant AND cell AND tumour, AND bone) OR (polyostotic AND osteoclastoma); 2 'denosumab'/exp OR (amg AND 162) OR amg162 OR amgiva OR prolia OR xgeva; 3 1 AND 2
ScienceDirect	1 ("Giant Cell Tumor of Bone" OR osteoclastoma) AND (Denosumab OR Xgeva OR AMG 162 OR Prolia)
Cochrane Library	1 MeSH descriptor: [Giant Cell Tumor of Bone] explode all trees; 2 osteoclastoma; 3 1 OR 2; 4 MeSH descriptor: [Denosumab] explode all trees; 5 Xgeva; 6 AMG 162; 7 Prolia; 8-4 OR 5 OR 6 OR 7; 6-3 AND 8
Clinicaltrials.gov	1 (Giant Cell Tumor of Bone OR osteoclastoma) AND (Denosumab OR Xgeva OR AMG 162 OR Prolia)
Registro Brasileiro de Ensaios Clínicos	1 (Giant Cell Tumor of Bone OR osteoclastoma) AND (Denosumab OR Xgeva OR AMG 162 OR Prolia)
Open Access Theses and Dissertations	1 ("Giant Cell Tumor of Bone" OR osteoclastoma) AND (Denosumab OR Xgeva OR AMG 162 OR Prolia)
Biblioteca Digital Brasileira de Teses e Dissertações	1 ("Giant Cell Tumor of Bone" OR osteoclastoma) AND (Denosumab OR Xgeva OR AMG 162 OR Prolia)

Table 2 Demographic characteristics of the included studies

Ref.	Year	Sample	Males	Females	Mean age	Min age	Max age
Agarwal et al[39]	2018	54	25	29	32	17	67
Borkowska et al[40]	2016	35	14	21	39	19	74
Boye et al[33]	2017	18	13	5	39	16	63
Campanacci et al[34]	2019	36	16	20	36	18	64
Chawla et al[27]	2019a	267	113	154	34	-	-
Chawla et al[27]	2019b	253	111	142	34	-	-
Chawla et al[27]	2019c	12	7	5	34	-	-
Chinder et al[28]	2019	123	87	36	29	-	-
Deveci et al[35]	2017	13	5	8	38	26	51
Goldschlager et al[50]	2015	5	0	5	36	22	58
Hindiskere et al[28]	2020	84	43	41	30	-	-
Latorre et al[41]	2021	14	6	8	35	23	46
Lee et al[26]	2018	12	-	-	40	21	66
Lim et al[30]	2020	68	31	37	34	-	-
Li et al[53]	2019	125	49	76	34	-	-
Müller et al[49]	2016	25	13	12	35	15	72
Murphy et al[48]	2020	154	85	69	35,8	21	51
Puri et al[42]	2019	44	26	18	27	13	47
Rutkowski et al[17]	2018	138	55	83	39	14	81
Sahito et al[32]	2021	70	38	32	30	17	47
Sambri et al[36]	2020	26	13	13	47	20	76
Scoccianti et al[47]	2018	21	11	10	42	17	66
Treffel et al[37]	2020	35	20	15	36	16	72
Ueda et al[46]	2015	17	8	9	36	18	66
Yang et al[38]	2018	16	6	10	33	16	63
Yi et al[45]	2018	8	3	5	37	18	45
Zhang et al[43]	2019	11	1	10	38	19	67
Zou et al[44]	2019	58	35	23	33	15	67

Table 3 Overall demographic characteristics

Total participants included across all studies	n = 1742
Total males included across all studies	834 (48.2%)
Total females included across all studies	896 (51.8%)
Mean age of participants	35 years
Minimum age of participants	13 years
Maximum age of participants	81 years