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©The Author(s) 2022.
World J Clin Cases. Jan 14, 2022; 10(2): 518-527
Published online Jan 14, 2022. doi: 10.12998/wjcc.v10.i2.518
Published online Jan 14, 2022. doi: 10.12998/wjcc.v10.i2.518
Table 3 BI-RADS classification distribution and risk prediction before and after adjustment
| Inspection method | Biopsy rate (%) | Malignancy risk (%) | Cancer detection rate (%) |
| BI-RADS classification before adjustment | 100 | 0 | 42.99 |
| 4A (n = 48) | |||
| 4B (n = 20) | |||
| 4C (n = 39) | |||
| Adjusted BI-RADS classification | 67.29 | 1.87 | 61.11 |
| 3 (n = 35) | |||
| 4A (n = 9) | |||
| 4B (n = 20) | |||
| 4C (n = 11) | |||
| 5 (n = 32) |
- Citation: Lyu SY, Zhang Y, Zhang MW, Zhang BS, Gao LB, Bai LT, Wang J. Diagnostic value of artificial intelligence automatic detection systems for breast BI-RADS 4 nodules. World J Clin Cases 2022; 10(2): 518-527
- URL: https://www.wjgnet.com/2307-8960/full/v10/i2/518.htm
- DOI: https://dx.doi.org/10.12998/wjcc.v10.i2.518