Relapse after nonmetastatic rhabdomyosarcoma: Salvage rates and prognostic variables

Background Patients with relapsing rhabdomyosarcoma (RMS) pose a therapeutic challenge, and the survival rate is reportedly poor. We describe a retrospective series of relapsing RMS patients treated at a referral center for pediatric sarcoma, investigating the pattern of relapse, salvage rates, and factors correlating with final outcomes. Methods The analysis concerned 105 patients <21 years old treated from 1985 to 2020 with initially localized RMS at first relapse. For risk-adapted stratification purposes, patient outcomes were examined using univariable and multivariable analyses based on patients' clinical features at first diagnosis, first-line treatments, clinical findings at first relapse, and second-line treatments. Results First relapses occurred 0.08-4.8 years (median 1 year) following initial diagnosis and were local/locoregional in 59% of cases. Treatment at first relapse included chemotherapy in all but two cases, radiotherapy in 38, and surgery in 21. Median event-free survival (EFS) after first relapse was 4 months, while 5-year EFS was 16.3%; median overall survival (OS) was 9 months, while 5-year OS was 16.7%. Several variables influenced survival rates. Considering only clinical findings and treatment at relapse, Cox's multivariable analysis showed that OS correlated significantly with time to relapse, radiotherapy administered at relapse, response to chemotherapy, and whether a second remission was achieved. Conclusion Survival following first relapse of patients with localized RMS at initial diagnosis is poor. The variables found to influence survival can be utilized in a risk-adapted model to estimate the chances of salvage to guide decisions for second-line treatments.

Automated summary

This study retrospectively analyzed the treatment of relapsing rhabdomyosarcoma patients and found that the survival rate after the first relapse is poor. Several factors were found to influence survival rates, and these factors can be used in a risk-adapted model to guide decisions for second-line treatments.