Machine Learning–based Identification of Prognostic Factors for Surgical Management in Patients With NOS Sarcoma
Considering the comparatively small cohort with which the machine learning algorithm was trained, this study underscores the importance of considering age, tumor size, location, body mass index, and tumor grading in the management of NOS sarcomas, shedding light on factors that may impact clinical outcomes and guide personalized treatment strategies.
Authors
- Felix Reinkemeier (ORCID: https://orcid.org/0000-0003-2265-725X)
- P.S. Harenberg (ORCID: https://orcid.org/0000-0002-2069-2855)
- Sonja Verena Schmidt (ORCID: https://orcid.org/0009-0000-3841-9067)
- Alexander Sogorski (ORCID: https://orcid.org/0000-0001-6619-9145)
- Marcus Lehnhardt
- Flemming Puscz (ORCID: https://orcid.org/0000-0002-3599-9243)
- Ingo Stricker
- Maxi von Glinski (ORCID: https://orcid.org/0000-0002-3923-896X)
- Mustafa Becerikli (ORCID: https://orcid.org/0000-0002-2695-5273)
- Christoph Wallner (ORCID: https://orcid.org/0000-0002-4707-5056)
- Mehran Dadras (ORCID: https://orcid.org/0000-0003-1283-2982)
- Marius Drysch
Institutions
- BG University Hospital Bergmannsheil Bochum (DE)
- Agaplesion Diakonieklinikum Rotenburg (DE)
- Ruhr University Bochum (DE)
Publication Details
- Journal
- Plastic & Reconstructive Surgery Global Open
- Published
- 2025-04-01
- DOI
- https://doi.org/10.1097/gox.0000000000006653
- Citations
- 1
- Primary Topic
- Sarcoma Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.52