Access the full text.
Sign up today, get DeepDyve free for 14 days.
K. Bilimoria, A. Stewart, D. Winchester, C. Ko (2008)
The National Cancer Data Base: A Powerful Initiative to Improve Cancer Care in the United StatesAnnals of Surgical Oncology, 15
Lundberg SM, Allen PG, Lee S-I
A Unified Approach to Interpreting Model Predictions
Sanda MG, Cadeddu JA, Kirkby E
Clinically localized prostate cancer: AUA/ASTRO/SUO guideline
Kan Ren (2005)
Introduction to Survival AnalysisSurvival Analysis
A. Stojić, Nenad Stanić, G. Vuǩović, S. Stanisic, M. Perišić, Andrej Šoštarić, L. Lazić (2019)
Explainable extreme gradient boosting tree-based prediction of toluene, ethylbenzene and xylene wet deposition.The Science of the total environment, 653
D. Altman, B. Stavola, S. Love, K. Stepniewska (1995)
Review of survival analyses published in cancer journals.British Journal of Cancer, 72
American College of Surgeons
National Cancer Data Base - Data Dictionary PUF 2016
Scott Lundberg, B. Nair, M. Vavilala, M. Horibe, Michael Eisses, Trevor Adams, D. Liston, Daniel Low, Shu-Fang Newman, Jerry Kim, Su-In Lee (2018)
Explainable machine-learning predictions for the prevention of hypoxaemia during surgeryNature biomedical engineering, 2
Du M, Liu N, Hu X
Techniques for interpretable machine learning
Albanes S, Vamossy DF
Predicting Consumer Default: A Deep Learning Approach
B. Mahal, David Yang, Natalie Wang, M. Alshalalfa, E. Davicioni, V. Choeurng, E. Schaeffer, A. Ross, D. Spratt, R. Den, N. Martin, K. Mouw, P. Orio, T. Choueiri, M. Taplin, Q. Trinh, F. Feng, P. Nguyen (2018)
Clinical and Genomic Characterization of Low-Prostate-specific Antigen, High-grade Prostate Cancer.European urology, 74 2
John Kang, R. Schwartz, J. Flickinger, S. Beriwal (2015)
Machine Learning Approaches for Predicting Radiation Therapy Outcomes: A Clinician's Perspective.International journal of radiation oncology, biology, physics, 93 5
Cox DR
Regression models and life-tables
C. Bellera, G. MacGrogan, M. Debled, C. Lara, V. Brouste, S. Mathoulin-Pélissier (2010)
Variables with time-varying effects and the Cox model: Some statistical concepts illustrated with a prognostic factor study in breast cancerBMC Medical Research Methodology, 10
National Comprehensive Cancer Network
NCCN Guidelines: Prostate Cancer
M. Sanda, J. Cadeddu, Erin Kirkby, Ronald Chen, Tony Crispino, J. Fontanarosa, S. Freedland, Kirsten Greene, L. Klotz, D. Makarov, J. Nelson, G. Rodrigues, H. Sandler, M. Taplin, J. Treadwell (2017)
Clinically Localized Prostate Cancer: AUA/ASTRO/SUO Guideline. Part I: Risk Stratification, Shared Decision Making, and Care OptionsThe Journal of Urology, 199
Konstantina Kourou, T. Exarchos, K. Exarchos, M. Karamouzis, D. Fotiadis (2014)
Machine learning applications in cancer prognosis and predictionComputational and Structural Biotechnology Journal, 13
R. Merkow, A. Rademaker, K. Bilimoria (2018)
Practical Guide to Surgical Data Sets: National Cancer Database (NCDB)JAMA Surgery, 153
Wei Zhao, Jiancheng Yang, Yingli Sun, Cheng Li, Wei-lan Wu, L. Jin, Zhiming Yang, Bingbing Ni, P. Gao, Peijun Wang, Y. Hua, Ming Li (2018)
3D Deep Learning from CT Scans Predicts Tumor Invasiveness of Subcentimeter Pulmonary Adenocarcinomas.Cancer research, 78 24
Molnar C
Interpretable Machine Learning: A Guide for Making Black Box Models Explainable
Machine learning-based interpretation and visualization of nonlinear interactions in prostate cancer survival
Machine learning-based interpretation and visualization of nonlinear interactions in prostate cancer survival
C. Rudin (2018)
Stop explaining black box machine learning models for high stakes decisions and use interpretable models insteadNature Machine Intelligence, 1
International Classification of Diseases for Oncology
ICD-O-3 online
Kleinbaum DG, Klein M
Introduction to survival analysis, in Gail M, Samet JM (eds): Survival Analysis
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.
JCO Clinical Cancer Informatics – Wolters Kluwer Health
Published: Jul 16, 2020
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.