Machine learning models for discriminating clinically significant from clinically insignificant prostate cancer using bi-parametric magnetic resonance imaging
This study demonstrates that machine learning models using radiomics features derived from bi-parametric MRI can discriminate csPCa from clinically insignificant PCa. These findings suggest that radiomics-based machine learning models have the potential to reduce the need for re-biopsy in cases of indeterminate pathology, assist in diagnosing pathology–radiology discordance, and support treatment decision-making in the management of PCa.
Authors
- Atadan Tunacı (ORCID: https://orcid.org/0000-0002-8796-8641)
- Şükrü Mehmet Ertürk (ORCID: https://orcid.org/0000-0003-4086-675X)
- Merve Gülbiz Kartal (ORCID: https://orcid.org/0000-0001-9018-4736)
- Okan İnce (ORCID: https://orcid.org/0000-0002-5083-5490)
- Esin Korkut (ORCID: https://orcid.org/0000-0002-5864-295X)
- Hakan Ayyıldız (ORCID: https://orcid.org/0000-0003-0244-7765)
Institutions
- Rush University Medical Center (US)
- University of Health Science (KH)
- Sivas State Hospital (TR)
- Sağlık Bilimleri Üniversitesi (TR)
- Istanbul University (TR)
- University of Health Sciences Antigua (AG)
Publication Details
- Journal
- Diagnostic and Interventional Radiology
- Published
- 2024-10-02
- DOI
- https://doi.org/10.4274/dir.2024.242856
- Citations
- 5
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.02