A Bayesian network meta-analysis: evaluating the efficacy and safety of targeted therapies in metastatic or advanced radioiodine-refractory differentiated thyroid cancer

Read full text
Authors: Pin Wang, Ling Li, Ying Liu, Yushuya Shi, Xiangyu Zhang, Jian Wu
Cited by: 0
References: 34

Abstract

Background: Approximately 5%-10% of patients with differentiated thyroid cancer (DTC) develop resistance to radioactive iodine (RAI), leading to unsatisfactory survival rates. The optimal medication for advanced or metastatic RAI-resistant differentiated thyroid cancer (RAIR-DTC) remains unclear. Methods: We conducted a Bayesian network meta-analysis based on a systematic search of six electronic databases. The primary outcome was progression-free survival (PFS); secondary outcomes included overall survival (OS), objective response rate (ORR), and grade ≥3 adverse events (AEs). Hazard ratios (HRs) with 95% credible intervals (CrIs) were used for time-to-event outcomes, while odds ratios (ORs) with 95% CrIs were used for binary outcomes. A separate Bayesian network meta-analysis was performed for each endpoint. Results: Our study included 9 RCTs involving 1,760 patients with RAIR-DTC. Lenvatinib, anlotinib, apatinib, and cabozantinib all significantly improved PFS versus placebo (HRs: 3.85-5.36), with lenvatinib ranking first overall (SUCRA: 81.97%) and showing sustained benefit up to 24 months. Apatinib provided early PFS advantage but waning efficacy beyond 6-9 months. No treatment significantly improved OS, though apatinib consistently ranked highest for OS. Lenvatinib achieved the highest objective response rate (OR = 143.18; SUCRA: 82.09%). For grade ≥3 adverse events, no treatment differed significantly from placebo; however, apatinib ranked highest in safety (SUCRA = 93.16%). Conclusion: Lenvatinib demonstrates the greatest benefit in both PFS and ORR among the evaluated TKIs for RAIR-DTC, suggesting it as a potential preferred first-line option. The time-dependent efficacy patterns of other TKIs warrant further investigation. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD420251089713, identifier CRD420251089713.

Fields of Study & Subjects

Thyroid Cancer Diagnosis and Treatment Thyroid and Parathyroid Surgery Thyroid Disorders and Treatments

Publication details

Journal / source
Frontiers in Oncology
Volume
16
Pages
1720670–1720670
Publication date
27 February 2026
Language
EN