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©The Author(s) 2025.
World J Gastrointest Oncol. May 15, 2025; 17(5): 103804
Published online May 15, 2025. doi: 10.4251/wjgo.v17.i5.103804
Published online May 15, 2025. doi: 10.4251/wjgo.v17.i5.103804
Table 5 Predictive variables for survival types extracted from the articles
| Selected features | Number (n) | Percentage (%) |
| Age | 7 | 87.5 |
| Stage | 7 | 87.5 |
| Grade | 6 | 75.0 |
| Treatment modality | 6 | 75.0 |
| Primary tumor site | 5 | 62.5 |
| Sex | 4 | 50.0 |
| Tumor size | 4 | 50.0 |
| Race | 3 | 37.5 |
| Histopathology type | 3 | 37.5 |
| Marital status | 3 | 37.5 |
| Positive lymph node numbers | 2 | 25.0 |
| Lymph node metastasis | 2 | 25.0 |
| Metastasis status | 2 | 25.0 |
| Regional nodes examined | 1 | 12.5 |
| Lymph node dissection | 1 | 12.5 |
| ASA grade | 1 | 12.5 |
| History of other cancers | 1 | 12.5 |
| Blood markers | 1 | 12.5 |
| Lauren type | 1 | 12.5 |
| Lymphovascular invasion | 1 | 12.5 |
| Months from diagnosis to treatment | 1 | 12.5 |
| Body weight | 1 | 12.5 |
- Citation: Wang HN, An JH, Wang FQ, Hu WQ, Zong L. Predicting gastric cancer survival using machine learning: A systematic review. World J Gastrointest Oncol 2025; 17(5): 103804
- URL: https://www.wjgnet.com/1948-5204/full/v17/i5/103804.htm
- DOI: https://dx.doi.org/10.4251/wjgo.v17.i5.103804