Machine Learning-Based Prognostic Prediction Models of Non-Metastatic Colon Cancer: Analyses Based on Surveillance, Epidemiology and End Results Database and a Chinese Cohort Article Swipe
Mo Tang,1 Lihao Gao,2 Bin He,1 Yufei Yang1 1Oncology Department, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, People’s Republic of China; 2Smart City Business Unit, Baidu Inc., Beijing, People’s Republic of ChinaCorrespondence: Yufei YangXiyuan Hospital of China Academy of Chinese Medical Sciences, No. 1 Xiyuan Caochang Road, Haidian District, Beijing, 100091, People’s Republic of ChinaEmail [email protected] GaoSmart City Business Unit, Baidu Inc., No. 51 Dezhen Road, Haidian District, Beijing, 100091, People’s Republic of ChinaEmail [email protected]: The present study aimed to develop prognostic prediction models based on machine learning (ML) for non-metastatic colon cancer (CRC), which can provide a precise quantitative risk assessment and serve as an assistive method for treatment strategy development. The possibility of improving prediction accuracy using nonlinear methods compared to linear methods was investigated.Patients and Methods: A cancer-specific survival (CSS) model constructed using logistic regression, extreme gradient boosting (XGBoost), and random forest algorithms was trained on the Surveillance, Epidemiology, and End Results datasets for 15,254 patients with non-metastatic CRC (split into training [70%] and internal validation [30%] datasets) and externally validated with an outpatient cohort of 311 cases from Xiyuan Hospital in China. A Chinese cohort was also used to develop recurrence and metastasis (R&M) models for CRC patients. The experiments for each model were performed 100 times to obtain average scores and 95% confidence intervals. The model performance was evaluated using the area under the receiver operating characteristic curve (AUC) values.Results: The XGBoost approach showed the highest AUC values of 0.86 (0.84– 0.88), 0.82 (0.81– 0.83), and 0.81 (0.79– 0.82) for one-, three-, and five-year CSS cohorts, respectively, along with a relatively high generalization ability. The XGBoost approach also performed best for the R&M model, with the AUC values of 0.71 (0.64– 0.79), 0.79 (0.74– 0.86), and 0.89 (0.82– 0.95) for one-, three-, and five-year R&M cohorts, respectively. The rankings of predictor importance for the CSS and R&M models were different, and the higher model accuracy was associated with more prognostic predictors.Conclusion: Three different ML algorithms for developing prognostic prediction models for non-metastatic CRC were compared. The predictive performance results showed that the nonlinear XGBoost approach performed best, suggesting that it can be used for quantifying the prognostic risk. It was also demonstrated that the model performance can be improved when more prognostic predictors are considered.Keywords: colon cancer, machine learning, extreme gradient boosting, prognostic prediction models
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doaj.org/article/584a0ba45bdc4cb0921abbdcae01a905
- OA Status
- green
- Cited By
- 6
- References
- 44
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4225360073
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4225360073Canonical identifier for this work in OpenAlex
- Title
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Machine Learning-Based Prognostic Prediction Models of Non-Metastatic Colon Cancer: Analyses Based on Surveillance, Epidemiology and End Results Database and a Chinese CohortWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
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2022-01-01Full publication date if available
- Authors
-
Mo Tang, Lihao Gao, Bin He, Yufei YangList of authors in order
- Landing page
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https://doaj.org/article/584a0ba45bdc4cb0921abbdcae01a905Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://doaj.org/article/584a0ba45bdc4cb0921abbdcae01a905Direct OA link when available
- Concepts
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Medicine, Cohort, Logistic regression, Random forest, Receiver operating characteristic, Colorectal cancer, Epidemiology, Confidence interval, Machine learning, Artificial intelligence, Internal medicine, Oncology, Cancer, Statistics, Database, Computer science, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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6Total citation count in OpenAlex
- Citations by year (recent)
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2024: 2, 2023: 2, 2022: 2Per-year citation counts (last 5 years)
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44Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| publication_date | 2022-01-01 |
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| abstract_inverted_index.0.95) | 296 |
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