A deep learning derived prostate zonal volume‐based biomarker from T2‐weighted MRI to distinguish between prostate cancer and benign prostatic hyperplasia
We demonstrated that the prostate zonal volume ratio computed with our ProZonaNet can be used to differentiate benign prostatic hyperplasia from prostate cancer on MRI. These results demonstrate the feasibility of non-invasive diagnosis of BPH-PCa, potentially aiding in the ability to distinguish PCa from benign cancer confounders such as BPH-Only.
Authors
- Anant Madabhushi (ORCID: https://orcid.org/0000-0002-5741-0399)
- Rakesh Shiradkar (ORCID: https://orcid.org/0000-0003-2852-3869)
- Tuomas Mirtti (ORCID: https://orcid.org/0000-0003-0455-9891)
- Jun Xu (ORCID: https://orcid.org/0000-0001-5315-8811)
- Kai Xuan (ORCID: https://orcid.org/0000-0001-6619-8206)
- Zelin Zhang (ORCID: https://orcid.org/0000-0001-9624-7590)
- Qingsong Yang (ORCID: https://orcid.org/0009-0007-5415-9593)
- Sena Azamat
Institutions
- Georgia Institute of Technology (US)
- United States Department of Veterans Affairs (US)
- Second Military Medical University (CN)
- Nanjing University of Information Science and Technology (CN)
- The Wallace H. Coulter Department of Biomedical Engineering (US)
- Atlanta Medical Center (US)
- Nanjing University of Science and Technology (CN)
- Changhai Hospital (CN)
- Jiangsu Industry Technology Research Institute (CN)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-08-01
- DOI
- https://doi.org/10.1002/mp.18053
- Citations
- 1
- Primary Topic
- Prostate Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.52