| Autor: |
Ross, Ashley E., Zhang, Jingbin, Huang, Huei-Chung, Yamashita, Rikiya, Keim-Malpass, Jessica, Simko, Jeffry P., DeVries, Sandy, Morgan, Todd M., Souhami, Luis, Dobelbower, Michael C., McGinnis, L. Scott, Jones, Christopher U., Dess, Robert T., Zeitzer, Kenneth L., Choi, Kwang, Hartford, Alan C., Michalski, Jeff M., Raben, Adam, Gomella, Leonard G., Sartor, A. Oliver, Rosenthal, Seth A., Sandler, Howard M., Spratt, Daniel E., Pugh, Stephanie L., Mohamad, Osama, Esteva, Andre, Chen, Emmalyn, Schaeffer, Edward M., Tran, Phuoc T., Feng, Felix Y. |
| Zdroj: |
European Urology Oncology; October 2024, Vol. 7 Issue: 5 p1024-1033, 10p |
| Abstrakt: |
Multimodal artificial intelligence models using digital histopathology slides outperform clinical and pathological variables for prognostic prediction of distant metastasis and prostate cancer–specific mortality, and can be incorporated in clinical practice for personalized risk stratification. |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
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