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Novel clinical risk calculator for improving cancer predictability of mpMRI fusion biopsy in prostates

  • Urology - Original Paper
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Abstract

Purpose

Prostate Imaging-Reporting and Data System (PI-RADS) assists in evaluating lesions on multiparametric magnetic resonance imaging (mpMRI), but there are still ongoing efforts in improving the predictive value for the presence of clinically significant PCa (csPCa) with a Gleason grade group ≥ 2 on Fusion-Biopsy. This pilot study intends to propose an easily implementable method for augmenting predictability of csPCa for PI-RADS.

Methods

A cohort of 151 consecutive patients underwent mpMRI Fusion and random US Biopsy as a result of having at least one PI-RADS lesion grade 3–5 between January 1, 2019 and December 31, 2022. A single radiologist reads all films in this study applying PI-RADS V2.

Results

Of the 151 consecutive patients, 49 had a highest lesion of PI-RADS 3, 82 had a highest lesion of PI-RADS 4, and 20 had a highest lesion of PI-RADS 5. For each respective group, 12, 42, and 18 patients had proven csPCa. Two predictive models for csPCa were created by employing a logistical regression with parameters readily available to providers. The models had an AUC of 0.8133 and 0.8206, indicating promising effective models.

Conclusion

PI-RADS classification has relevant predictability problems for grades 3 and 4. By applying the presented risk calculators, patients with PI-RADS 3 and 4 are better stratified, and thus, a significant number of patients can be spared biopsies with potential complications, such as infection and bleeding. The presented predictive models may be a valuable diagnostic tool, adding additional information in the clinical decision-making process for biopsies.

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Availability of data and materials

The data that support the findings of this study are available on request.

Abbreviations

AUC:

Area under the curve

csPCa:

Clinically significant prostate cancer (Gleason Grade Group ≥ 2)

mpMRI:

Multiparametric MRI

PCa:

Prostate cancer

PI‐RADS:

Prostate imaging‐reporting and data system

PLUM:

Prospective Loyola University multiparametric MRI

PSAD:

PSA density

RC:

Risk calculator

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Acknowledgements

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Authors and Affiliations

  1. School of Medicine, Department of Urology, Texas Tech University Health Sciences Center, 3601-4 Street STOP 7260, Lubbock, TX, 79430-7260, USA

    Anthony Bruccoliere, Vivie Tran & Werner T. W. de Riese

  2. Department of Radiology, University Medical Center, Lubbock, TX, 79415, USA

    Naseem Helo

  3. School of Medicine, Texas Tech University Health Sciences Center, Clinical Research Institute, Lubbock, TX, 79415, USA

    Abdul Awal & Stephanie Stroever

Authors
  1. Anthony Bruccoliere
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  2. Vivie Tran
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  3. Naseem Helo
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  4. Abdul Awal
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  5. Stephanie Stroever
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  6. Werner T. W. de Riese
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Contributions

Anthony Bruccoliere and Werner de Riese contributed to the study conception and design. Material preparation and data collection were performed by Anthony Bruccoliere, Vivie Tran, and Nasseem Helo. Data analysis was performed by Abdul Awal and Stephanie Stroever. The first draft of the manuscript was written by Anthony Bruccoliere and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Werner T. W. de Riese.

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The authors have no relevant financial or non-financial interests to disclose.

Ethics approval and consent to participate

The Internal Review Board (IRB) of Texas Tech University HSC, Lubbock, Texas (#L20-147), waived the need to obtain informed consent due to meeting criteria in accordance with 45 CFR 46.104(d)(4)(iii) in which no identifying information was presented for the studied patients.

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Bruccoliere, A., Tran, V., Helo, N. et al. Novel clinical risk calculator for improving cancer predictability of mpMRI fusion biopsy in prostates. Int Urol Nephrol (2024). https://doi.org/10.1007/s11255-024-04037-1

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  • DOI: https://doi.org/10.1007/s11255-024-04037-1

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