Assessment of the Impact of Anti-Hormonal Treatment on Bone Health in Patients With Breast Cancer Using Machine-Learning Analysis
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Purpose: This study analyzed the effects of anti-hormonal treatment (HTx) on bone health using real-world evidence and machine-learning analysis.Methods: We extracted 20 clinical variables and patient history of HTx by reviewing the records of 244 patients treated for breast cancer between January 2014 and June 2018 at Pusan National University Hospital. Baseline and first follow-up dual-energy absorptiometry were analyzed. To identify which of the 20 clinical variables were highly associated with the patients’ bone mineral density and trabecular bone score (TBS), we applied partial least squares discriminant analysis (PLS-DA) and MetaboAnalyst. A self-organizing map (SOM) was used to sort the patient groups based on the selected variables.Results: The patients were classified as ‘no change’ (n=161, 70.6%), ‘deteriorated’ (n=43, 18.9%), or ‘improved’ (n=24, 10.5%) according to the change in TBS during the follow-up period. The baseline TBS value was significantly lower in the improved group. The top five variables (age, HTx, duration of vitamin D and/or calcium intake, cancer stage, and body mass index) were selected using PLS-DA, which generated variable importance value (VIP) scores for all variables and high VIP scores contributed greatly to patient classification. To identify the patients’ clinical patterns using the top five selected variables, a 3×4 grid structure SOM was generated. Clusters were selected to represent the most improved, no change, and most deteriorated groups.Conclusion: This study evaluated the clinical association between HTx and bone health in patients with breast cancer under various clinical conditions and found that the characteristics of patients included in the study were too heterogeneous to be classified in clusters. Therefore, additional data should be collected for future research.
Abstract
Purpose: This study analyzed the effects of anti-hormonal treatment (HTx) on bone health using real-world evidence and machine-learning analysis.Methods: We extracted 20 clinical variables and patient history of HTx by reviewing the records of 244 patients treated for breast cancer between January 2014 and June 2018 at Pusan National University Hospital. Baseline and first follow-up dual-energy absorptiometry were analyzed. To identify which of the 20 clinical variables were highly associated with the patients’ bone mineral density and trabecular bone score (TBS), we applied partial least squares discriminant analysis (PLS-DA) and MetaboAnalyst. A self-organizing map (SOM) was used to sort the patient groups based on the selected variables.Results: The patients were classified as ‘no change’ (n=161, 70.6%), ‘deteriorated’ (n=43, 18.9%), or ‘improved’ (n=24, 10.5%) according to the change in TBS during the follow-up period. The baseline TBS value was significantly lower in the improved group. The top five variables (age, HTx, duration of vitamin D and/or calcium intake, cancer stage, and body mass index) were selected using PLS-DA, which generated variable importance value (VIP) scores for all variables and high VIP scores contributed greatly to patient classification. To identify the patients’ clinical patterns using the top five selected variables, a 3×4 grid structure SOM was generated. Clusters were selected to represent the most improved, no change, and most deteriorated groups.Conclusion: This study evaluated the clinical association between HTx and bone health in patients with breast cancer under various clinical conditions and found that the characteristics of patients included in the study were too heterogeneous to be classified in clusters. Therefore, additional data should be collected for future research.
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