Development of a quantitative multiparametric ultrasound and deep learning classifier for the detection of prostate cancer
Our proposed deep learning-based classifier using quantitative 3D mpUS features accurately detects csPCa, as validated on the largest mpUS prostate dataset to date. This opens the door to ultrasound as an accurate, cost-effective method for csPCa detection.
Authors
- J.O.R.G. Oddens
- Wim Zwart
- Florian Delberghe (ORCID: https://orcid.org/0009-0000-2937-9796)
- Arnoud W. Postema
- Massimo Mischi
- Auke Jager
- Hessel Wijkstra
- Daniel L. van den Kroonenberg
- Simona Turco
- Giuseppe Valvano
- Xueting Li
Institutions
- Dutch Cancer Society (NL)
- Leiden University Medical Center (NL)
- Angiogenesis Foundation (US)
- Amsterdam Neuroscience (NL)
- Eindhoven University of Technology (NL)
Publication Details
- Journal
- European Radiology
- Published
- 2026-01-30
- DOI
- https://doi.org/10.1007/s00330-026-12323-y
- Primary Topic
- Prostate Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Institutes of Health
- National Cancer Institute