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B. Turkbey

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Open access Sep 2026

Inter-reader variability for quality assessment of synthetically degraded prostate MR images aligned with PIQUAL version 2.

PURPOSE The purpose of this study was to evaluate expert radiologists' perceptions of the quality of magnetic resonance imaging (MRI) of the prostate for images that underwent controlled degradation. MATERIALS AND METHODS Seven-levels of controlled degradations were applied to 10 single-slice image sets, with T2-weig...

Kang-Lung Lee, Jobie Budd, Shu-Huei Shen et al. · 0 citations
Open access Aug 2026

Deep learning-based histologic classifiers enable molecular subtyping of metastatic prostate cancer.

Metastatic prostate cancer is a clinically and molecularly heterogeneous disease. Under the selective pressure of androgen receptor (AR)-directed therapies, resistant phenotypes frequently emerge, posing significant diagnostic and therapeutic challenges. Neuroendocrine prostate cancer (NEPC) is a clinically important p...

Zhi-Jun Chen, Erolcan Sayar, D. Guevara et al. · 0 citations
Sep 2026

Quantitative Thresholds on 18F-DCFPyL PET for Determining Malignancy in Prostate Cancer with Pathologic Correlation.

In this article, we assess histologically validated quantitative parameters on 18F-piflufolastat (18F-DCFPyL) prostate-specific membrane antigen (PSMA) PET and identify thresholds for differentiating malignant prostate cancer from benign tissue. Methods: In this multicenter retrospective study, men with prostate cancer...

L. Lindenberg, Erich P. Huang, G. Ulaner et al. · 0 citations
Review Open access Jul 2026

Uncertainty quantification for artificial intelligence in medical imaging: what every radiologist needs to know.

Although UQ has the potential to improve the safety and interpretability of AI-assisted screening, challenges remain, including calibration, threshold selection, computational cost, and the need for prospective clinical validation.

Fernando Vega Lara, Lisa Koopmans, Christian Roest et al. · 1 citation
Open access Aug 2026

Toward Autonomous Prostate Cancer Clinical Significance Determination from Spectral/Statistics Features in Bi-Parametric MRI

Simple Summary Deciding between active surveillance and treatment for prostate cancer patients often requires accurate assessment of prostate tumors. Independent fast quantitative analysis may provide a check and support for radiologists who conventionally visually inspect MRI and may follow protocols such as PI-RADS....

Rulon Mayer, Yuan Yuan, J. Udupa et al. · 0 citations
Review Open access Aug 2026

Autonomous AI in prostate cancer: the road ahead towards clinical implementation.

The findings highlight the need for evidence from large, multicenter, prospective trials and evaluation frameworks that reflect the consequences of clinical decision-making, as well as further exploration of safeguards to monitor and address mismatches between training data and incoming scans during deployment.

Lisa Koopmans, Fernando Vega Lara, Christian Roest et al. · 0 citations

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