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Review
. 2024 Dec;34(12):7698-7704.
doi: 10.1007/s00330-024-10869-3. Epub 2024 Jun 27.

Prostate MRI and artificial intelligence during active surveillance: should we jump on the bandwagon?

Vilma Bozgo  1   2 , Christian Roest  3 , Inge van Oort  4 , Derya Yakar  3   5 , Henkjan Huisman  1   6 , Maarten de Rooij  7
Affiliations
Review

Prostate MRI and artificial intelligence during active surveillance: should we jump on the bandwagon?

Vilma Bozgo et al. Eur Radiol. 2024 Dec.

Abstract

Objective: To review the components of past and present active surveillance (AS) protocols, provide an overview of the current studies employing artificial intelligence (AI) in AS of prostate cancer, discuss the current challenges of AI in AS, and offer recommendations for future research.

Methods: Research studies on the topic of MRI-based AI were reviewed to summarize current possibilities and diagnostic accuracies for AI methods in the context of AS. Established guidelines were used to identify possibilities for future refinement using AI.

Results: Preliminary results show the role of AI in a range of diagnostic tasks in AS populations, including the localization, follow-up, and prognostication of prostate cancer. Current evidence is insufficient to support a shift to AI-based AS, with studies being limited by small dataset sizes, heterogeneous inclusion and outcome definitions, or lacking appropriate benchmarks.

Conclusion: The AI-based integration of prostate MRI is a direction that promises substantial benefits for AS in the future, but evidence is currently insufficient to support implementation. Studies with standardized inclusion criteria and standardized progression definitions are needed to support this. The increasing inclusion of patients in AS protocols and the incorporation of MRI as a scheduled examination in AS protocols may help to alleviate these challenges in future studies.

Clinical relevance statement: This manuscript provides an overview of available evidence for the integration of prostate MRI and AI in active surveillance, addressing its potential for clinical optimizations in the context of established guidelines, while highlighting the main challenges for implementation.

Key points: Active surveillance is currently based on diagnostic tests such as PSA, biopsy, and imaging. Prostate MRI and AI demonstrate promising diagnostic accuracy across a variety of tasks, including the localization, follow-up and risk estimation in active surveillance cohorts. A transition to AI-based active surveillance is not currently realistic; larger studies using standardized inclusion criteria and outcomes are necessary to improve and validate existing evidence.

Keywords: Artificial intelligence; Magnetic resonance imaging; Prostatic neoplasms.

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Conflict of interest statement

Compliance with ethical standards Guarantor The scientific guarantor of this publication is Dr. Maarten de Rooij. Conflict of interest V.B., C.R., D.Y., and H.H. have received grants from Siemens Healthineers on the topic of AI in active surveillance. D.Y. is a member of the Scientific Editorial Board for European Radiology (Imaging Informatics and Artificial Intelligence), they have not participated in the selection nor review processes for this article. The remaining authors declare no relationships with any companies, whose products or services may be related to the subject matter of the article. Statistics and biometry No complex statistical methods were necessary for this paper. Informed consent Written informed consent was not required for this study because this is an invited editorial and not the subject of a research article. Ethical approval Institutional Review Board approval was not required because this is an invited editorial and not the subject of a research article. Study subjects or cohorts overlap Not relevant. Methodology Special Report

References

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