Predicting Overall Survival in Patients with Metastatic Rectal Cancer: a Machine Learning Approach
A significant proportion of patients with rectal cancer will present with synchronous metastasis at the time of diagnosis. Overall survival (OS) for these patients are highly variable and previous attempts to build predictive models often have low predictive power, with concordance indexes (c-index) less than 0.70.
Using the National Cancer Database (2010–2014), we identified patients with synchronous metastatic rectal cancer. The data was split into a training dataset (diagnosis years 2010–2012), which was used to build the machine learning model, and a testing dataset (diagnosis years 2013–2014), which was used to externally validate the model. A nomogram predicting 3-year OS was created using Cox proportional hazard regression with lasso penalization. Predictors were selected based on clinical significance and availability in NCDB. Performance of the machine learning model was assessed by c-index.
A total of 4098 and 3107 patients were used to construct and validate the nomogram, respectively. Internally validated c-indexes at 1, 2, and 3 years were 0.816 (95% CI 0.813–0.818), 0.789 (95% CI 0.786–0.790), and 0.778 (95% CI 0.775–0.780), respectively. External validated c-indexes at 1, 2, and 3 years were 0.811, 0.779, and 0.778, respectively.
There is wide variability in the OS for patients with metastatic rectal cancer, making accurate predictions difficult. However, using machine learning techniques, more accurate models can be built. This will aid patients and clinicians in setting expectations and making clinical decisions in this group of challenging patients.
KeywordsRectal cancer Machine learning Nomograms Lasso NCDB
Dr. Zhao is supported by the National Library of Medicine Training Grant (NIH Grant: T15LM011271).
Author Contribution Statement
All authors met the standards set by the International Committee of Medical Journal Editors to be listed as an author for this manuscript.
Compliance with Ethical Standards
Conflicts of Interest
The authors declare that they have no conflict of interest.
The funding source had no role in the design and/or general conduct of this study; had no access to the data or role in data collection, management, analysis, or interpretation; had no role in the preparation, review, or approval of the manuscript; and had no role in the decision to submit the manuscript for publication.
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