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J Gastrointest Surg. 2019 Aug 29. doi: 10.1007/s11605-019-04373-z. [Epub ahead of print]

Predicting Overall Survival in Patients with Metastatic Rectal Cancer: a Machine Learning Approach.

Author information

1
Department of Surgery, University of California San Diego, San Diego, CA, USA. beiqunmzhao@gmail.com.
2
, San Diego, USA. beiqunmzhao@gmail.com.
3
Department of Anesthesiology, University of California San Diego, San Diego, CA, USA.
4
Department of Family Medicine and Public Health, University of California San Diego, San Diego, CA, USA.
5
Department of Surgery, University of California San Diego, San Diego, CA, USA.

Abstract

BACKGROUND:

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.

METHODS:

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.

RESULTS:

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.

CONCLUSIONS:

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.

KEYWORDS:

Lasso; Machine learning; NCDB; Nomograms; Rectal cancer

PMID:
31468331
DOI:
10.1007/s11605-019-04373-z

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