A Predictive Model for Distant Metastasis in Patients With Breast Cancer Based on Machine Learning
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Purpose: Breast cancer starts as a local disease, but can metastasize to distant organs. In this study, we described an easy-to-use tool for predicting distant metastases based on clinical characteristics and gene expression profiles. Methods: We performed a retrospective chart review of 326 patients with breast cancer who underwent surgery and CancerSCAN<sup>TM</sup> between January 2001 and December 2014 at the Samsung Medical Center. Additional retrospective data for 83 patients during 2015 were used for internal validation. CancerSCAN<sup>TM</sup>, a next-generation sequencing-based targeted deep sequencing analysis, was used for gene analysis, and Azure Machine Learning (ML) was used for the ML processes. Results: The no-distant metastasis group comprised 267 patients, while the distant metastasis group comprised 59. Using the Azure ML platform, a predictive model was developed with 326 cases. The area under the curve of the receiver operating characteristic curve for predictive value was 0.917. Based on the internal validation performed using 83 patients, the true-negative was 81 and the true-positive was two when a threshold value of 0.5 was applied. Conclusion: Patients with breast cancer are at risk of metastasis and experience fear throughout their lives. Our predictive model is a valuable and easy-to-access tool for identifying patients with distant metastasis and it presents a way for each institution to achieve optimal results using its variables. Further evaluation with a larger patient population will improve the reliability of this model.
Abstract
Purpose: Breast cancer starts as a local disease, but can metastasize to distant organs. In this study, we described an easy-to-use tool for predicting distant metastases based on clinical characteristics and gene expression profiles. Methods: We performed a retrospective chart review of 326 patients with breast cancer who underwent surgery and CancerSCAN<sup>TM</sup> between January 2001 and December 2014 at the Samsung Medical Center. Additional retrospective data for 83 patients during 2015 were used for internal validation. CancerSCAN<sup>TM</sup>, a next-generation sequencing-based targeted deep sequencing analysis, was used for gene analysis, and Azure Machine Learning (ML) was used for the ML processes. Results: The no-distant metastasis group comprised 267 patients, while the distant metastasis group comprised 59. Using the Azure ML platform, a predictive model was developed with 326 cases. The area under the curve of the receiver operating characteristic curve for predictive value was 0.917. Based on the internal validation performed using 83 patients, the true-negative was 81 and the true-positive was two when a threshold value of 0.5 was applied. Conclusion: Patients with breast cancer are at risk of metastasis and experience fear throughout their lives. Our predictive model is a valuable and easy-to-access tool for identifying patients with distant metastasis and it presents a way for each institution to achieve optimal results using its variables. Further evaluation with a larger patient population will improve the reliability of this model.
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