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Prospective comparison of restriction spectrum imaging and non-invasive biomarkers to predict upgrading on active surveillance prostate biopsy

Prostate Cancer and Prostatic Diseases volume 27, pages 65–72 (2024)Cite this article

Subjects

Abstract

Background

Protocol-based active surveillance (AS) biopsies have led to poor compliance. To move to risk-based protocols, more accurate imaging biomarkers are needed to predict upgrading on AS prostate biopsy. We compared restriction spectrum imaging (RSI-MRI) generated signal maps as a biomarker to other available non-invasive biomarkers to predict upgrading or reclassification on an AS biopsy.

Methods

We prospectively enrolled men on prostate cancer AS undergoing repeat biopsy from January 2016 to June 2019 to obtain an MRI and biomarkers to predict upgrading. Subjects underwent a prostate multiparametric MRI and a short duration, diffusion-weighted enhanced MRI called RSI to generate a restricted signal map along with evaluation of 30 biomarkers (14 clinico-epidemiologic features, 9 molecular biomarkers, and 7 radiologic-associated features). Our primary outcome was upgrading or reclassification on subsequent AS prostate biopsy. Statistical analysis included operating characteristic improvement using AUROC and AUPRC.

Results

The individual biomarker with the highest area under the receiver operator characteristic curve (AUC) was RSI-MRI (AUC = 0.84; 95% CI: 0.71–0.96). The best non-imaging biomarker was prostate volume-corrected Prostate Health Index density (PHI, AUC = 0.68; 95% CI: 0.53–0.82). Non-imaging biomarkers had a negligible effect on predicting upgrading at the next biopsy but did improve predictions of overall time to progression in AS.

Conclusions

RSI-MRI, PIRADS, and PHI could improve the predictive ability to detect upgrading in AS. The strongest predictor of clinically significant prostate cancer on AS biopsy was RSI-MRI signal output.

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Fig. 1: Restriction spectrum imaging generated a restricted signal map.
Fig. 2
Fig. 3: Correlation heatmap demonstrating all prostate cancer biomarkers tested.
Fig. 4: Receiver operating curves for combinations of biomarkers.
Fig. 5: Metrics for the sequential models grouped by test methodology.

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Data availability

The datasets generated during and/or analyzed during the current study are available as an R package at https://github.com/uclahs-cds/public-R-ASBiomarkerStratification. The datasets generated during and/or analyzed during the current study are also available from the corresponding author upon reasonable request.

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Acknowledgements

First, thank you to all the patients who altruistically participated in this research and to all the coordinators and lab personnel who are vital to move research but are not named on the manuscripts.

Funding

MAL is supported through the DOD Prostate Cancer Research Program (PCRP) Physician Research Training Award. This work was supported by the Office of the Assistant Secretary of Defense for Health Affairs through the Prostate Cancer Research Program under Award No. W81XWH-15-1-0441. Opinions, interpretations, conclusions, and recommendations are those of the authors and are not necessarily endorsed by the Department of Defense. The work was also funded by an NCI Early Detection Research Network (EDRN) Associate Member Grant (U24CA086368) and by EDRN Biomarker Development Laboratories (U01CA214194, U24CA115102, and U01CA214170), by the ITCR (U24CA249265) and by NCI CCSG grants to UCLA (P30CA016042) and UTHSC (P30CA054174). Beckman-Coulter Life Sciences supplied reagents for the PHI test. JJT’s research is funded in part by the Prostate Cancer Foundation Young Investigator Award and a SPORE Career Enhancement Program (CA186786). The funder played no role in the experimentation or authorship of the manuscript.

Author information

Authors and Affiliations

  1. Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, CA, USA

    Stefan E. Eng, Alfonso Lam & Paul C. Boutros

  2. Institute for Precision Health, UCLA, Los Angeles, CA, USA

    Stefan E. Eng, Alfonso Lam & Paul C. Boutros

  3. Department of Urology, UCLA, Los Angeles, CA, USA

    Stefan E. Eng, Alfonso Lam & Paul C. Boutros

  4. Department of Urology, University of Texas Health San Antonio, San Antonio, TX, USA

    Benjamin Basasie, Teresa L. Johnson-Pais & Michael A. Liss

  5. Department of Microbiology and Molecular Cell Biology, Eastern Virginia Medical School, Norfolk, VA, USA

    O. John Semmes

  6. Department of Pathology, Eastern Virginia Medical School, Norfolk, VA, USA

    Dean A. Troyer

  7. Research Imaging Institute, University of Texas Health San Antonio, San Antonio, TX, USA

    Geoffrey D. Clarke & Michael A. Liss

  8. Department of Radiology, University of Texas Health San Antonio, San Antonio, TX, USA

    Geoffrey D. Clarke & Abhijit G. Sunnapwar

  9. Department of Cell Systems and Anatomy, University of Texas Health San Antonio, San Antonio, TX, USA

    Robin J. Leach

  10. Department of Pathology, Division of Clinical Chemistry, Johns Hopkins University, Baltimore, MD, USA

    Lori J. Sokoll & Daniel W. Chan

  11. Department of Urlogy, Vanderbilt University, Nashville, TN, USA

    Jeffrey J. Tosoian

  12. Department of Pathology, University of Michigan, Ann Arbor, MI, USA

    Javed Siddiqui & Arul M. Chinnaiyan

  13. CHRISTUS Santa Rosa Health System, San Antonio, TX, USA

    Ian M. Thompson Jr.

  14. Department of Human Genetics, UCLA, Los Angeles, CA, USA

    Paul C. Boutros

  15. Broad Stem Cell Research Center, UCLA, Los Angeles, CA, USA

    Paul C. Boutros

  16. Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada

    Paul C. Boutros

  17. College of Pharmacy, University of Texas Austin, Austin, TX, USA

    Michael A. Liss

  18. Department of Urology, South Texas Veterans Healthcare System, San Antonio, TX, USA

    Michael A. Liss

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Contributions

Conceptualization: MAL, IMT. Data curation: MAL, BB, RJL, TLJ-P, DAT, AGS, JJT, JS. Formal analysis: SEE, AL. Funding acquisition: MAL, RJL, OJS, AMC, DWC, PCB. Investigation: MAL, OJS, DAT. Methodology: MAL, PCB. Project administration: MAL, BB. Resources: RJL, JJT, LJS, MAL, PCB. Software: MAL, GDC, PCB. Supervision: MAL, IMT, RJL, PCB. Validation: MAL, PCB. Visualization: MAL, SEE, AL, PCB. Writing—original draft: MAL, SEE, BB. Writing—review and editing: SEE, BB, AL, OJS, DAT, GDC, AGS, RGL, TLJ-P, LJS, DWC, JJT, JS, AMC, IMT, MAL, PCB.

Corresponding authors

Correspondence to Paul C. Boutros or Michael A. Liss.

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Competing interests

JJT and AMC are co-founders and have equity in Lynx Dx, which has licensed the urine biomarkers mentioned in this study from Hologic and the University of Michigan. JJT has leadership roles in Lynx Dx. The University of Michigan has been issued a patent on ETS gene fusions in prostate cancer on which AMC is a co-inventor.

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Eng, S.E., Basasie, B., Lam, A. et al. Prospective comparison of restriction spectrum imaging and non-invasive biomarkers to predict upgrading on active surveillance prostate biopsy. Prostate Cancer Prostatic Dis 27, 65–72 (2024). https://doi.org/10.1038/s41391-022-00591-w

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