Systematic effects of patient factors and scanner/protocol factors on a Restriction Spectrum Imaging (RSI) quantitative MRI biomarker for prostate cancer
While multiparametric MRI has revolutionized clinically significant prostate cancer (csPCa) detection, conventional apparent diffusion coefficient (ADC) maps suffer from a monoexponential model that conflate the various forms of diffusion in the tissue, leading to poor microstructural specificity and substantial inter-scanner variability. Restriction Spectrum Imaging (RSI) addresses these limitations with a multi-compartment biophysical model that separates the diffusion signal into four compartments, providing a more specific quantitative imaging biomarker, the Restriction Spectrum Imaging restriction score (RSIrs). Although RSIrs has demonstrated promising detection performance for csPCa, patient- and acquisition-related factors may influence its measured values and performance. Our retrospective analysis included 1,890 men (median age 70) from seven centers between 2018 and 2024. Our primary objective was to characterize the quantitative technical robustness of RSIrs using linear modeling. We assessed how age, race, ethnicity, prostate volume, medication use, and MRI acquisition parameters influenced prostate maximum RSIrs (RSIrsmax) values. Secondary, we conducted a functional validation of RSIrs’ csPCa detection performance when accounting for any significant effects (if any). Non-csPCa patients in this analysis were (1) with confirmed benign or Grade Group 1 findings on biopsy or (2) with a non-suspicious mpMRI (PI-RADS 1 or 2) and a prostate-specific antigen density (PSAD) ≤ 0.15. Results revealed modest effects from age (1.8/year increase), prostate volume (-0.83/mL decrease), and acquisition methods (varying between − 63.14 to + 56.23). While these acquisition- and patient-level variations partially overlapped with change in RSIrsmax due to Grade Group 2 csPCa, these variations were secondary to the primary signal variation due to csPCa. Adjusting for them did not improve csPCa detection performance (p > 0.05). AUC of RSIrsmax for patient-level csPCa detection was 0.77 [95% CI: 0.75–0.79] compared to 0.74 [0.72–0.76] after. RSIrs demonstrates robust csPCa detection performance regardless of modest signal variations from patient and acquisition factors, supporting its potential as a generalizable quantitative imaging biomarker for csPCa.
Authors
- Courtney Ollison
- Michael E. Hahn (ORCID: https://orcid.org/0000-0002-8323-8174)
- Dimitri A. Kessler (ORCID: https://orcid.org/0000-0002-1813-1039)
- Joshua Kuperman (ORCID: https://orcid.org/0000-0002-7890-4785)
- Natasha Wehrli (ORCID: https://orcid.org/0000-0002-6962-9005)
- Christopher J. Kane (ORCID: https://orcid.org/0000-0002-8769-2112)
- Tristan Barrett (ORCID: https://orcid.org/0000-0002-1180-1474)
- Sean Woolen (ORCID: https://orcid.org/0000-0003-4951-2173)
- Isabella R. Pompa
- Michael A. Liss (ORCID: https://orcid.org/0000-0001-6978-1026)
- P. Murphy (ORCID: https://orcid.org/0000-0003-2563-3552)
- Mariluz Rojo Domingo (ORCID: https://orcid.org/0000-0003-1386-4636)
- Shaun Trecarten (ORCID: https://orcid.org/0009-0002-0968-6339)
- Allison Y. Zhong (ORCID: https://orcid.org/0000-0001-5732-1380)
- Ian Matthews (ORCID: https://orcid.org/0000-0003-0773-4923)
- Karoline Kallis (ORCID: https://orcid.org/0000-0002-0889-004X)
- Michael A. Ohliger (ORCID: https://orcid.org/0000-0001-6878-8189)
- Clare M. Tempany (ORCID: https://orcid.org/0000-0002-1642-9720)
- Eric Weinberg (ORCID: https://orcid.org/0000-0003-4951-7142)
- Anthony J. Pamatmat (ORCID: https://orcid.org/0000-0003-4652-8444)
- Ahmed Shabaik (ORCID: https://orcid.org/0000-0003-1987-3453)
- Nabih Nakrour (ORCID: https://orcid.org/0009-0000-6402-7585)
- Jacob Roberts (ORCID: https://orcid.org/0009-0008-7746-7633)
- Karan Santhosh
- Daniel J. A. Margolis
- Gary Hollenberg
- Felix Feng
- George Xu
- Rebecca Rakow-Penner
- Matthew Cooperberg
- Kang-Lung Lee
- Aditya Bagrodia
- Yuze Song
- Sophia C. Kamran
- Thomas Osinski
- Jonathan Levine
- Anders M. Dale
- Tyler M. Seibert
- Juan Javier-Desloges
- Christopher C. Conlin
- Mukesh Harisinghani
- David Song
- Madison T. Baxter
- Deondre D. Do
Institutions
- The University of Texas at San Antonio Health Science Center (US)
- University of California, San Francisco (US)
- Cornell University (US)
- University of Cambridge (GB)
- UC San Diego Health System (US)
- University of California System (US)
- University of Rochester Medical Center (US)
- University of California San Diego (US)
- Massachusetts General Hospital (US)
- La Jolla Bioengineering Institute (US)
Publication Details
- Journal
- Cancer Imaging
- Published
- 2026-04-16
- DOI
- https://doi.org/10.1186/s40644-026-01032-w
- Primary Topic
- Prostate Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- U.S. Department of Defense
- Prostate Cancer Foundation
- National Institutes of Health