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Updated: Jun 7, 2025
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
Published on: August 16, 2020
Risk factor analysis and predictive model construction for bone metastasis in newly diagnosed malignant tumor
Chengru Hu1, Jing Wu1, Zhipei Duan1
1Department of Oncology, Suzhou Ninth People's Hospital Suzhou 215200, Jiangsu, China.
Objective:
To identify risk factors for bone metastasis in patients with newly diagnosed malignant tumor and to develop a prediction model.
Methods:
Clinical data from 232 patients with newly diagnosed malignant tumors were analyzed to screen for risk factors associated with bone metastasis. A nomogram prediction model was constructed using R software. The model's performance was evaluated using Receiver Operating Characteristic (ROC) analysis, Bootstrap sampling, and Decision Curve Analysis (DCA).
Results:
The incidence of bone metastasis in the 232 cases with newly diagnosed malignant tumors was 21.98% (51/232). Multivariate logistic regression analysis revealed that tumor staging III-IV, lymph node metastasis, high Eastern Cancer Collaboration Group Physical Status (ECOG-PS) score, high alkaline phosphatase (ALP) expression, and high SII index were risk factors for bone metastasis at initial diagnosis (all P<0.05). The area under the curve (AUC) of the nomogram model was 0.893. Bootstrap sampling validation showed a small error of 0.017 between predicted and actual probabilities. DCA supported the utility of the model in clinical practice.
Conclusion:
Bone metastasis in newly diagnosed malignant tumors is associated with advanced tumor staging, lymph node metastasis, high ECOG-PS score, elevated ALP expression, and a high SII index. A nomogram model based on these factors can effectively predict the risk of bone metastasis in these patients.
