A prediction model for urological tumor metastasis using liquid biopsy-derived biomarkers.
A machine-learning model using blood biomarkers accurately predicts which urological cancer patients will develop metastasis.
OBJECTIVE: To construct and validate a prediction model for tumor metastasis in patients with urological tumors based on liquid biopsy biomarkers and clinical characteristics, to facilitate early clinical identification ... The AUC of the random forest model (0.891) was significantly higher than that of the support vector machine (0.885) and the gradient boosting model (0.739), making it the optimal model.
What the study was
- Study design
- Retrospective study
- Population
- patients
- Sample size
- 252
- Category
- Early Detection
- Maturity
- Exploratory
- Journal
- Frontiers in medicine
Why it surfaced
Score 6/10 [EARLY_CANCER_DETECTION]: Retrospective study (Journal Article) matched 'Early cancer detection (liquid biopsy, c'. Components — novelty:1/3, relevance:2/3, design:1/2, population:2/2. Confidence: high. Conservative scoring applied per v1.3 rubric.
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