Integrating biparametric MRI radiomics with clinical variables improves pre-treatment prediction of prostate cancer recurrence - Takeaways - MDSpire

Integrating biparametric MRI radiomics with clinical variables improves pre-treatment prediction of prostate cancer recurrence

  • By

  • Selma Bozorgpana

  • Indri Desiati

  • Mohammed R. S. Sunoqrot

  • Petter Davik

  • Guro F. Giskeødegård

  • Gabriel Addio Nketiah

  • Mattijs Elschot

  • May-Britt Tessem

  • Tone F. Bathen

  • July 15, 2026

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  • 1

    The study evaluated the integration of radiomic features from biparametric MRI with clinical variables for predicting prostate cancer recurrence.

  • 2

    A total of 395 men who underwent pre-operative bpMRI before radical prostatectomy were included in this retrospective study.

  • 3

    The combined model achieved an AUC of 0.85, outperforming radiomics-only and clinical-only models with AUCs of 0.78 and 0.72, respectively.

  • 4

    The most influential predictors in the combined model were Gleason Grade Group and PSA, along with radiomic first-order and texture features.

  • 5

    The results indicate that integrating radiomic features enhances prediction of biochemical recurrence, requiring external validation for clinical implementation.

Original Source(s)

Integrating biparametric MRI radiomics with clinical variables improves pre-treatment prediction of prostate cancer recurrence

  • by Selma Bozorgpana, Indri Desiati, Mohammed R. S. Sunoqrot, Petter Davik, Guro F. Giskeødegård, Gabriel Addio Nketiah, Mattijs Elschot, May-Britt Tessem, Tone F. Bathen

  • July 15, 2026

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