Copyright
©The Author(s) 2023.
World J Radiol. Dec 28, 2023; 15(12): 359-369
Published online Dec 28, 2023. doi: 10.4329/wjr.v15.i12.359
Published online Dec 28, 2023. doi: 10.4329/wjr.v15.i12.359
Table 1 Clinical and protocol details of training and test cases
| Training cases (n = 58) | Test cases (n = 20) | |
| AJCC stage | ||
| Stage 1 | 0 | 5 |
| Stage 2 | 15 | 5 |
| Stage 3 | 14 | 7 |
| Stage 4 | 29 | 3 |
| T stage | ||
| T1 | 0 | 2 |
| T2 | 0 | 4 |
| T3 | 21 | 13 |
| T4 | 37 | 1 |
| Location | ||
| Right | 39 | 17 |
| Transverse | 3 | 2 |
| Left | 16 | 1 |
| CT slice thickness (mm) | ||
| 7 | 0 | 1 |
| 5 | 29 | 17 |
| 3-4 | 25 | 0 |
| 2 or less | 4 | 2 |
| Contrast | ||
| IV+PO | 27 | 18 |
| IV | 22 | 1 |
| PO | 4 | 1 |
| None | 5 | 0 |
- Citation: Grudza M, Salinel B, Zeien S, Murphy M, Adkins J, Jensen CT, Bay C, Kodibagkar V, Koo P, Dragovich T, Choti MA, Kundranda M, Syeda-Mahmood T, Wang HZ, Chang J. Methods for improving colorectal cancer annotation efficiency for artificial intelligence-observer training. World J Radiol 2023; 15(12): 359-369
- URL: https://www.wjgnet.com/1949-8470/full/v15/i12/359.htm
- DOI: https://dx.doi.org/10.4329/wjr.v15.i12.359