Machine learning and logistic regression in estimating survival in patients with high-malignant deep-seated soft tissue sarcomas: development and analysis based on a population-based retrospective cohort

Although the developed random forest ML model performed well during training, logistic regression outperformed it after internal validation. Soft tissue sarcomas located in the trunk, grade 3 tumors, and chemotherapy within 3 months of surgery demonstrated the highest negative effect on survival, consistent with current treatment protocols in which patients with high-risk disease are managed with more aggressive multimodal therapy. Further external validation and assessment of clinical utility are required before potential clinical implementation.