Development of Artificial Intelligence-based Real-time Automatic Fusion of Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasonography of the Prostate - 28/03/25

Doi : 10.1016/j.urology.2025.03.004

Francesco Cianflone ^a,^{^{1
Francesco Cianflone and Bogdan Maris contributed equally.}}, Bogdan Maris ^b,^{^{1
Francesco Cianflone and Bogdan Maris contributed equally.}}, Riccardo Bertolo ^a,^⁎ , Alessandro Veccia ^a, Francesco Artoni ^a, Greta Pettenuzzo ^a, Francesca Montanaro ^a, Antonio Benito Porcaro ^a, Alberto Bianchi ^a, Sarah Malandra ^a, Francesco Ditonno ^a, Maria Angela Cerruto ^a, Giulia Zamboni ^c, Paolo Fiorini ^b, Alessandro Antonelli ^a
^a Department of Surgery, Dentistry, Pediatrics and Gynecology, Urology Unit, Azienda Ospedaliera Universitaria Integrata, University of Verona, Verona, Italy
^b Department of Engineering for Innovation Medicine, University of Verona, Verona, Italy
^c Department of Diagnostic and Public Health, Unit of Radiology, Azienda Ospedaliera Universitaria Integrata, University of Verona, Verona, Italy

^⁎Address correspondence to: Riccardo Bertolo, MD, PhD, Azienda Ospedaliera Universitaria Integrata Verona, AUOI Verona, Borgo Trento Hospital, University of Verona, Piazzale A. Stefani 1, 37126 Verona, Italy.Azienda Ospedaliera Universitaria Integrata Verona, AUOI Verona, Borgo Trento Hospital, University of VeronaPiazzale A. Stefani 1Verona37126Italy

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Résumé

OBJECTIVE

To report the development of artificial intelligence (AI)-based software to allow for the autonomous fusion of transrectal ultrasound and multiparametric magnetic resonance images of the prostate to be used during transperineal prostate biopsies.

MATERIALS AND METHODS

This study was approved by the Institutional Review Board (protocol ID 3167CESC). The automatic software development for fusion biopsy involved 3 steps: (1) developing an AI component to segment the prostate during ultrasound; (2) developing the component to segment anatomical structures in magnetic resonance images using labeled datasets from the Cancer Imaging Archive and in-house scans; (3) developing the fusion component to register segmented ultrasound and magnetic resonance images using a 3-step process: pre-alignment, rigid alignment, and elastic fusion, validated by measuring the lesion distance between modalities. Statistical analysis included descriptive statistics and the Mann-Whitney U test, evaluating outcomes with Dice scores and average precision metrics.

RESULTS

The ultrasound component showed a Dice score of 0.87 with a test set of 75,357 images and 28,946 annotations. The magnetic resonance component achieved a Dice score of 0.85 on a test set of 2494 images and annotations. It also demonstrated a mean average precision of 0.80 for bounding boxes and 0.88 for segmented objects, both measured at a 50% intersection over union threshold. The fusion AI component reduced the median magnetic resonance-ultrasound lesion distance from 8 mm (interquartile ranges 6-9) after rigid fusion to 4 mm (interquartile ranges 3-5) after elastic fusion (P <.001).

CONCLUSION

A data processing pipeline and AI were created to allow for the autonomous fusion of ultrasound and magnetic resonance images to be ideally used during transperineal prostate biopsies.

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