©The Author(s) 2024.
World J Gastrointest Oncol. Oct 15, 2024; 16(10): 4146-4156
Published online Oct 15, 2024. doi: 10.4251/wjgo.v16.i10.4146
Published online Oct 15, 2024. doi: 10.4251/wjgo.v16.i10.4146
Table 2 R-value of the neural network prediction model
| Dose | Training | Verification | Test | All |
| Dn0 | 0.8792 | 0.6458 | 0.6207 | 0.7513 |
| Dn10 | 0.9209 | 0.8115 | 0.8126 | 0.8813 |
| Dn20 | 0.9101 | 0.8509 | 0.8720 | 0.8918 |
| Dn30 | 0.9261 | 0.8510 | 0.9561 | 0.9139 |
| Dn40 | 0.9674 | 0.8915 | 0.8697 | 0.9274 |
| Dn50 | 0.8319 | 0.8146 | 0.8629 | 0.8263 |
| Dn60 | 0.9252 | 0.8278 | 0.8537 | 0.8858 |
| Dn70 | 0.9162 | 0.8332 | 0.8924 | 0.9010 |
| Dn80 | 0.8397 | 0.7873 | 0.7825 | 0.8199 |
| Dn90 | 0.8599 | 0.8138 | 0.8079 | 0.8391 |
| Dn100 | 0.9715 | 0.7300 | 0.8336 | 0.8606 |
| Dnmean | 0.9088 | 0.9498 | 0.7943 | 0.9006 |
- Citation: Zhang HW, Wang YH, Hu B, Pang HW. Uninvolved liver dose prediction in stereotactic body radiation therapy for liver cancer based on the neural network method. World J Gastrointest Oncol 2024; 16(10): 4146-4156
- URL: https://www.wjgnet.com/1948-5204/full/v16/i10/4146.htm
- DOI: https://dx.doi.org/10.4251/wjgo.v16.i10.4146