Equivalent Long-term Survival with Neoadjuvant FLOT versus SOX in Locally Advanced Gastric Cancer: 5-Year Follow-up of the Dragon III Trial
Sah, B. K.; Li, C.; Zhu, Z.
Show abstract
BackgroundBoth FLOT and SOX neoadjuvant regimens are widely used for locally advanced gastric cancer; however, direct head-to-head survival data to guide optimal treatment selection are lacking. MethodsWe conducted an open-label, randomized, phase 2 trial (NCT03636893) at a single center in China. Patients with locally advanced gastric cancer (cT3-4b, cN1-3, cM0) were randomized to receive neoadjuvant FLOT (four cycles) or SOX (three cycles) before D2 gastrectomy. We report the long-term survival outcomes with 5-year follow-up in 74 randomized patients through May 2025. ResultsThe first prospective comparison demonstrated remarkable outcomes for both regimens. With median follow-up of 65.7 months, median overall survival exceeded 5 years in both groups: 61.5 months (95% CI: not reached) for FLOT versus 67.8 months (95% CI: 25.7-109.9) for SOX, with no significant difference (HR 1.101, 95% CI: 0.595-2.036, p=0.759). Disease-free survival was equivalent (23.0 vs 25.5 months, HR 1.060, p=0.842). Clinicopathological factors proved to be more prognostic than regimen choice: complete/subtotal tumor regression achieved 80.5-month survival versus 47.6 months for partial response (p=0.017), whereas gastrectomy type emerged as the strongest independent predictor (HR 3.619 for total vs. partial gastrectomy, p=0.010). Both regimens demonstrated favorable safety profiles with manageable toxicity. ConclusionsThis study establishes equivalent long-term survival between the FLOT and SOX regimens. With both achieving 5-year survival, treatment selection should prioritize patient factors, institutional experience, and practical considerations, rather than expected survival differences. Optimizing the pathological response and surgical approach appears more critical than specific regimen choice. FundingThis research received no specific grant funding.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Clinical activity of Mitogen-Activated Protein Kinase (MAPK) inhibitors in patients with MAP2K1 (MEK1)-mutated metastatic cancers 93%
- An Evidenced-Based Prior for Estimating the Treatment Effect of Phase III Randomized Trials in Oncology 93%
- Clinical activity of MAPK targeted therapies in patients with non-V600 BRAF mutant tumors 93%
Similar papers in this journal
- COVID-19 Outcomes in Patients with Cancer: Findings from the University of California Health System Database 92%
- Added-value of whole exome and RNA Sequencing in advanced and refractory cancer patients with no molecular-based treatment recommendation based on a 90-gene panel 91%
- Circulating serum miRNAs predict response to platinum chemotherapy in high-grade serous ovarian cancer 91%
Similar papers in this journal
- Efficacy of PD-(L)1 blockade monotherapy compared to PD-(L)1 blockade plus chemotherapy in first-line PD-L1-positive advanced lung adenocarcinomas: A cohort study 93%
- Nicotinamide combined with gemcitabine is an immunomodulatory therapy that restrains pancreatic cancer in mice 92%
- Tumor-agnostic transcriptome-based classifier identifies spatial infiltration patterns of CD8+ T cells in the tumor microenvironment and predicts clinical outcome in early- and late-phase clinical trials 91%
Similar papers in this journal
Similar papers in this journal
- Phase 1b dose expansion and translational analyses of olaparib in combination with the oral AKT inhibitor capivasertib in recurrent endometrial, triple negative breast, and ovarian, primary peritoneal, or fallopian tube cancer 92%
- Tumor-specific activity of precision medicines in the NCI-MATCH trial 90%
- Leveraging Longitudinal Patient-Reported Outcomes Trajectories to Predict Survival in Non-Small-Cell Lung Cancer 90%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.