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Angiosarcoma: clinical outcomes and prognostic factors, a single-center analysis

  • Siyer Roohani,
  • Titus Rotermund,
  • Felix Ehret,
  • Tomasz Dziodzio,
  • Armin Jarosch,
  • Frederik Maximilian Schäfer,
  • Anne Flörcken,
  • Silvan Wittenberg,
  • Daniel Zips,
  • David Kaul

摘要

Purpose

This study sought to investigate oncological outcomes and prognostic factors for patients with angiosarcomas (AS).

Methods

This single-center, retrospective cohort study, analyzed histopathologically confirmed AS cases. Primarily diagnosed, locally recurrent and metastatic AS were included. Overall survival (OS), local control (LC) and local progression-free survival (LPFS) were assessed by Kaplan-Meier estimator. Multivariable Cox regression analysis was performed to detect factors associated with OS and LPFS.

Results

In total, 118 patients with a median follow-up of 6.6 months were included. The majority presented with localized disease (62.7%), followed by metastatic (31.4%) and locally recurrent (5.9%) disease. Seventy-four patients (62.7%) received surgery, of which 29 (39.2%) were treated with surgery only, 38 (51.4%) with surgery and perioperative radiotherapy or chemotherapy, and 7 (9.4%) with surgery, perioperative radiotherapy and chemotherapy. Multivariable Cox regression of OS showed a significant association with age per year (hazard ratio (HR): 1.03, p = 0.044) and metastatic disease at presentation (hazard ratio: 3.24, p = 0.015). For LPFS, age per year (HR: 1.04, p = 0.008), locally recurrent disease at presentation (HR: 5.32, p = 0.013), and metastatic disease at presentation (HR: 4.06, p = 0.009) had significant associations. Tumor size, epithelioid components, margin status, and perioperative RT and/or CTX were not significantly associated with OS or LPFS.

Conclusion

Older age and metastatic disease at initial presentation status were negatively associated with OS and LPFS. Innovative and collaborative effort is warranted to overcome the epidemiologic challenges of AS by collecting multi-institutional datasets, characterizing AS molecularly and identifying new perioperative therapies to improve patient outcomes.