Machine learning and explainable artificial intelligence to predict pathologic stage in men with localized prostate cancer
Our ML models better predicted pathologic stage relative to existing nomograms at predicting pathologic stage. Accurate prediction of pathologic stage can help oncologists and patients determine optimal definitive treatment options for patients with prostate cancer.
Authors
- J. Tze‐Fei Wong (ORCID: https://orcid.org/0000-0001-8539-8537)
- Osama Mohamad (ORCID: https://orcid.org/0000-0003-2972-657X)
- Hemal Semwal
- Colton Ladbury (ORCID: https://orcid.org/0000-0002-2668-3415)
- Derya Tilki (ORCID: https://orcid.org/0000-0001-7033-1380)
- Savita V. Dandapani (ORCID: https://orcid.org/0000-0003-2840-2995)
- Arya Amini
- Bertram Yuh
- Scott Glaser (ORCID: https://orcid.org/0000-0002-0756-9449)
- Ali Sabbagh (ORCID: https://orcid.org/0000-0001-6826-959X)
Institutions
- City Of Hope National Medical Center (US)
- The University of Texas MD Anderson Cancer Center (US)
- Koç University (TR)
- Universität Hamburg (DE)
- University of California, Los Angeles (US)
- University of California, San Francisco (US)
- University Medical Center Hamburg-Eppendorf (DE)
- Martini-Klinik (DE)
Publication Details
- Journal
- The Prostate
- Published
- 2024-10-13
- DOI
- https://doi.org/10.1002/pros.24793
- Citations
- 3
- Primary Topic
- Prostate Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.26