Nomogram model based analysis on factors affecting the safety of gold fiducial markers in prostate cancer(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2025年第2期
- Page:
- 154-159
- Research Field:
- 医学放射物理
- Publishing date:
Info
- Title:
- Nomogram model based analysis on factors affecting the safety of gold fiducial markers in prostate cancer
- Author(s):
- XU Fei; TIAN Long
- Radiotherapy Room, Department of Oncology, Shengjing Hospital of China Medical University, Shenyang 110022, China
- Keywords:
- Keywords: prostate cancer nomogram gold fiducial marker safety influence factor
- PACS:
- R737.25；R811.1
- DOI:
- DOI:10.3969/j.issn.1005-202X.2025.02.003
- Abstract:
- Abstract: Objective To analyze the factors affecting the safety after gold fiducial markers (GFM) insertion for prostate cancer radiotherapy based on the nomogram model, and to evaluate the clinical application potential of the proposed model. Methods A total of 600 prostate cancer patients who underwent GFM insertion were randomly assigned to training set and test set. The inter-marker distance was set at a queue value of 1 mm, and the patients in training set were further divided into safety and risk subgroups after GFM insertion. Multivariate Logistic regression was used to analyze the influence factors for the safety after GFM insertion in training set. A nomogram prediction model was constructed using the obtained results and R language 4.0 "rms" software package. Internal validation was completed with calibration curves and clinical decision curves, while external validation was completed by plotting receiver operating characteristic curves and calculating area under the curve (AUC) in training and test sets. Results Multivariate Logistic regression analysis identified prostate volume <25 mL, insertion at the bottom, insertion of 4 markers, and average axial-to-surface ratio of 1-2 or 2-3 as the independent risk factors for the safety after GFM insertion (P<0.05) to construct a nomogram prediction model. The internal validation results showed that the model had good consistency and could provide clinical net benefits. External validation results revealed that ROC curves for predicting the safety after GFM insertion in training set and test set were well fitted ([χ2]=3.224, P=0.254). The AUC were 0.876 (95%CI: 0.724-0.903) and 0.865 (95%CI: 0.702-0.897), respectively, without statistically significant differences (P=0.341). Conclusion The nomogram model for the safety after GFM insertion exhibits satisfactory prediction efficiency, and it serving as a new evaluation tool can further improve the clinical application value of GFM.
Last Update: 2025-01-22