Cancer Res Treat.  2022 Apr;54(2):517-524. 10.4143/crt.2021.206.

Machine Learning Model for Predicting Postoperative Survival of Patients with Colorectal Cancer

• Osman MH
• Mohamed RH
• Sarhan HM
• Park EJ
• Baik SH
• Lee KY
• Kang J

• KMID: 2528221
• DOI: http://doi.org/10.4143/crt.2021.206

Abstract

Purpose
Machine learning (ML) is a strong candidate for making accurate predictions, as we can use large amount of data with powerful computational algorithms. We developed a ML based model to predict survival of patients with colorectal cancer (CRC) using data from two independent datasets.
Materials and Methods
A total of 364,316 and 1,572 CRC patients were included from the Surveillance, Epidemiology, and End Results (SEER) and a Korean dataset, respectively. As SEER combines data from 18 cancer registries, internal validation was done using 18-Fold-Cross-Validation then external validation was performed by testing the trained model on the Korean dataset. Performance was evaluated using area under the receiver operating characteristic curve (AUROC), sensitivity and positive predictive values.
Results
Clinicopathological characteristics were significantly different between the two datasets and the SEER showed a significant lower 5-year survival rate compared to the Korean dataset (60.1% vs. 75.3%, p < 0.001). The ML-based model using the Light gradient boosting algorithm achieved a better performance in predicting 5-year-survival compared to American Joint Committee on Cancer stage (AUROC, 0.804 vs. 0.736; p < 0.001). The most important features which influenced model performance were age, number of examined lymph nodes, and tumor size. Sensitivity and positive predictive values of predicting 5-year-survival for classes including dead or alive were reported as 68.14%, 77.51% and 49.88%, 88.1% respectively in the validation set. Survival probability can be checked using the web-based survival predictor (http://colorectalcancer.pythonanywhere.com).
Conclusion
ML-based model achieved a much better performance compared to staging in individualized estimation of survival of patients with CRC.

Keyword

Machine learning; LightGBM; Colorectal neoplasms; Area under the curve; Mortality; SEER

Figure

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  Fig. 1 Comparison of 5-year overall survival between Surveillance, Epidemiology, and End Results (SEER) dataset and Korean dataset.

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  Fig. 2 Comparison of receiver operating characteristic curve using 5-year survival in the training, internal validation and external validation. AUC, area under the curve.

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  Fig. 3 Feature importance selection in respective survival time periods. CEA, carcinoembryonic antigen; LN, lymph node.

Reference

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