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Machine Learning-Based Interpretation and Visualization of Nonlinear Interactions in Prostate Cancer Survival

Machine Learning-Based Interpretation and Visualization of Nonlinear Interactions in Prostate... PURPOSE: Shapley additive explanation (SHAP) values represent a unified approach to interpreting predictions made by complex machine learning (ML) models, with superior consistency and accuracy compared with prior methods. We describe a novel application of SHAP values to the prediction of mortality risk in prostate cancer. METHODS: Patients with nonmetastatic, node-negative prostate cancer, diagnosed between 2004 and 2015, were identified using the National Cancer Database. Model features were specified a priori: age, prostate-specific antigen (PSA), Gleason score, percent positive cores (PPC), comorbidity score, and clinical T stage. We trained a gradient-boosted tree model and applied SHAP values to model predictions. Open-source libraries in Python 3.7 were used for all analyses. RESULTS: We identified 372,808 patients meeting the inclusion criteria. When analyzing the interaction between PSA and Gleason score, we demonstrated consistency with the literature using the example of low-PSA, high-Gleason prostate cancer, recently identified as a unique entity with a poor prognosis. When analyzing the PPC-Gleason score interaction, we identified a novel finding of stronger interaction effects in patients with Gleason >= 8 disease compared with Gleason 6-7 disease, particularly with PPC >= 50%. Subsequent confirmatory linear analyses supported this finding: 5-year overall survival in Gleason >= 8 patients was 87.7% with PPC < 50% versus 77.2% with PPC >= 50% (P < .001), compared with 89.1% versus 86.0% in Gleason 7 patients (P < .001), with a significant interaction term between PPC >= 50% and Gleason >= 8 (P < .001). CONCLUSION: We describe a novel application of SHAP values for modeling and visualizing nonlinear interaction effects in prostate cancer. This ML-based approach is a promising technique with the potential to meaningfully improve risk stratification and staging systems. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png JCO Clinical Cancer Informatics Wolters Kluwer Health

Machine Learning-Based Interpretation and Visualization of Nonlinear Interactions in Prostate Cancer Survival

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References (24)

Publisher
Wolters Kluwer Health
Copyright
(C) 2020 American Society of Clinical Oncology
ISSN
2473-4276
DOI
10.1200/CCI.20.00002
Publisher site
See Article on Publisher Site

Abstract

PURPOSE: Shapley additive explanation (SHAP) values represent a unified approach to interpreting predictions made by complex machine learning (ML) models, with superior consistency and accuracy compared with prior methods. We describe a novel application of SHAP values to the prediction of mortality risk in prostate cancer. METHODS: Patients with nonmetastatic, node-negative prostate cancer, diagnosed between 2004 and 2015, were identified using the National Cancer Database. Model features were specified a priori: age, prostate-specific antigen (PSA), Gleason score, percent positive cores (PPC), comorbidity score, and clinical T stage. We trained a gradient-boosted tree model and applied SHAP values to model predictions. Open-source libraries in Python 3.7 were used for all analyses. RESULTS: We identified 372,808 patients meeting the inclusion criteria. When analyzing the interaction between PSA and Gleason score, we demonstrated consistency with the literature using the example of low-PSA, high-Gleason prostate cancer, recently identified as a unique entity with a poor prognosis. When analyzing the PPC-Gleason score interaction, we identified a novel finding of stronger interaction effects in patients with Gleason >= 8 disease compared with Gleason 6-7 disease, particularly with PPC >= 50%. Subsequent confirmatory linear analyses supported this finding: 5-year overall survival in Gleason >= 8 patients was 87.7% with PPC < 50% versus 77.2% with PPC >= 50% (P < .001), compared with 89.1% versus 86.0% in Gleason 7 patients (P < .001), with a significant interaction term between PPC >= 50% and Gleason >= 8 (P < .001). CONCLUSION: We describe a novel application of SHAP values for modeling and visualizing nonlinear interaction effects in prostate cancer. This ML-based approach is a promising technique with the potential to meaningfully improve risk stratification and staging systems.

Journal

JCO Clinical Cancer InformaticsWolters Kluwer Health

Published: Jul 16, 2020

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