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Meta-Analysis
. 2025 Jun 11:13:e19546.
doi: 10.7717/peerj.19546. eCollection 2025.

The value of nanocarbon contrast methylene blue based on dye-based tracer technology in sentinel lymph node biopsy for breast cancer: a systematic review and meta-analysis

Affiliations
Meta-Analysis

The value of nanocarbon contrast methylene blue based on dye-based tracer technology in sentinel lymph node biopsy for breast cancer: a systematic review and meta-analysis

Luhua Xia et al. PeerJ. .

Abstract

Objective: The purpose of this study was to compare the diagnostic value of nanocarbon suspensions and methylene blue injections in sentinel lymph node biopsies of patients with breast cancer based on the dye method.

Methods: A systematic search of the PubMed, Embase, Cochrane Library (Central) and Web of Science (SCI Expanded) databases was performed to determine the diagnostic value of carbon nanoparticles in Chinese databases (China National Knowledge Infrastructure (CNKI), VIP, Wanfang, Chinese Biomedical Literature Database (CBM)) for identifying methylene blue in sentinel lymph node biopsies of patients with breast cancer. The QUADAS2 quality evaluation tool of Review Manager 5.4 was used to evaluate the quality of the included studies. Meta-Disc 1.4 software was used to calculate the extracted valid data and perform a heterogeneity test. STATA 14.0 software was selected to conduct a sensitivity analysis, and Deek's publication bias test was used for the included studies.

Results: In 17 articles, the binding sensitivity of nanocarbons was 0.93 (0.90-0.95), the binding specificity was 0.98 (0.97-0.99), the binding sensitivity of methylene blue was 0.89 (0.85-0.92), and the binding specificity was 0.94 (0.92-0.95). The AUC value of the nanocarbon SROC was 0.9827 (SE = 0.0062), and the AUC value of methylene blue was 0.9495 (SE = 0.0139).

Conclusion: Nanocarbon versus methylene blue is a more satisfactory dye tracer for sentinel lymph node biopsy in breast cancer patients and should be considered a first-line diagnostic agent.

Keywords: Breast cancer; Diagnostic value; Methylene blue; Nanocarbon suspension.

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Conflict of interest statement

The authors declare there are no competing interests.

Figures

Figure 1
Figure 1. The study selection process.
Figure 2
Figure 2. Methodological quality graph.
Jie, Yan & Cao (2011), Yang & Li (2011), Zhou, Du & Chen (2012), Lin, Zhou & Li (2012), Sun, Ge & Cao (2013), Wu et al. (2015), Chen (2015), Wang, Chen & Zhao (2017), Zhu, Li & Zhang (2017), Zhang (2017), Li et al. (2018), Qi (2018), Wu & Chen (2019), Liu, Bao & Qiu (2019), Xia, Cao & Yang (2019), Jiang, Cao & Zhou (2021).
Figure 3
Figure 3. Methodological quality summary.
Figure 4
Figure 4. NCS pooled sensitivity.
Jie, Yan & Cao (2011), Yang & Li (2011), Zhou, Du & Chen (2012), Lin, Zhou & Li (2012), Sun, Ge & Cao (2013), Wu et al. (2015), Chen (2015), Wang, Chen & Zhao (2017), Zhu, Li & Zhang (2017), Zhang (2017), Li et al. (2018), Qi (2018), Wu & Chen (2019), Liu, Bao & Qiu (2019), Xia, Cao & Yang (2019), Jiang, Cao & Zhou (2021).
Figure 5
Figure 5. NCS pooled specificity.
Jie, Yan & Cao (2011), Yang & Li (2011), Zhou, Du & Chen (2012), Lin, Zhou & Li (2012), Sun, Ge & Cao (2013), Wu et al. (2015), Chen (2015), Wang, Chen & Zhao (2017), Zhu, Li & Zhang (2017), Zhang (2017), Li et al. (2018), Qi (2018), Wu & Chen (2019), Liu, Bao & Qiu (2019), Xia, Cao & Yang (2019), Jiang, Cao & Zhou (2021).
Figure 6
Figure 6. MBI pooled sensitivity.
Jie, Yan & Cao (2011), Yang & Li (2011), Zhou, Du & Chen (2012), Lin, Zhou & Li (2012), Sun, Ge & Cao (2013), Wu et al. (2015), Chen (2015), Wang, Chen & Zhao (2017), Zhu, Li & Zhang (2017), Zhang (2017), Li et al. (2018), Qi (2018), Wu & Chen (2019), Liu, Bao & Qiu (2019), Xia, Cao & Yang (2019), Jiang, Cao & Zhou (2021).
Figure 7
Figure 7. MBI pooled specificity.
Jie, Yan & Cao (2011), Yang & Li (2011), Zhou, Du & Chen (2012), Lin, Zhou & Li (2012), Sun, Ge & Cao (2013), Wu et al. (2015), Chen (2015), Wang, Chen & Zhao (2017), Zhu, Li & Zhang (2017), Zhang (2017), Li et al. (2018), Qi (2018), Wu & Chen (2019), Liu, Bao & Qiu (2019), Xia, Cao & Yang (2019), Jiang, Cao & Zhou (2021).
Figure 8
Figure 8. SROC curve of the NCS.
Figure 9
Figure 9. SROC curve of the BMI.
Figure 10
Figure 10. NCS sensitivity analysis.
Figure 11
Figure 11. MBI sensitivity analysis.
Figure 12
Figure 12. Publication bias test of the NCS.
Figure 13
Figure 13. Publication bias test of the MBI.
Figure 14
Figure 14. Probability of the Fagan likelihood nomograph and NCS in detecting lymph node metastasis by SLNB.
Figure 15
Figure 15. Probability of the Fagan likelihood nomograph and MBI in detecting lymph node metastasis by SLNB.

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