Evidence map›Paper›PMID 42400610›Full record

ReviewAbdominal radiology (New York)2026

Artificial intelligence-enabled MRI surveillance of prostate cancer: integrating PRECISE v2, PI-QUAL, and longitudinal biomarkers-a narrative review.

Motohiro Fujiwara, Jina Pakpoor, Serdar Aslan, Merve Nur Tasdemir, Christian Roest, Yuki Arita, Andrei Purysko, Francesco Giganti

Abstract readReview
PubMed Publisher
In one paragraph

Review in Abdominal radiology (New York), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Motohiro FujiwaraDepartment of Pathology, Memorial Sloan Kettering Cancer Center, New York, USA. motohiro.fujiwara@gmail.com.
Jina PakpoorDivision of Surgical and Interventional Science, University College London, London, UK.
Serdar AslanDepartment of Radiology, Giresun University, Giresun, Turkey.
Merve Nur TasdemirDepartment of Radiology, Giresun University, Giresun, Turkey.
Christian RoestDepartment of Radiology, University Medical Center Groningen, Groningen, Netherlands.
Yuki AritaGraduate School of Medical and Dental Sciences, Institute of Science Tokyo, Tokyo, Japan. yukiarita1113@gmail.com.
Andrei PuryskoImaging Institute, Abdominal Imaging Section and Department of Nuclear Medicine, Diagnostics Institute, Cleveland Clinic, Cleveland, USA.
Francesco GigantiDivision of Surgical and Interventional Science, University College London, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Active surveillance (AS) is widely used for men with low-risk and selected favorable intermediate-risk prostate cancer, but pathways remain heterogeneous and often rely on serial biopsy despite sampling limitations and morbidity. Multiparametric MRI (mpMRI) improves enrollment and longitudinal monitoring, yet reproducibility depends on image quality, reader expertise, measurement consistency, biopsy quality, and clear definitions of progression. The PRECISE recommendations were developed to standardize serial MRI reporting, and PRECISE version 2 (PRECISE v2) introduces key refinements, including minimum quality thresholds (PRECISE-X), comparison with both baseline and prior scans, and subdivision of stable examinations into visible and non-visible disease (3-V and 3-NonV). In parallel, artificial intelligence (AI) for prostate MRI is advancing in quality control, detection, segmentation, and longitudinal analysis. Near-term opportunities in AS include automated quality checks, reduction of measurement variability through segmentation and lesion tracking, and integration of imaging change with clinical and biomarker trajectories such as PSA density and kinetics. However, key barriers include dataset shift, sample-size imbalance, imperfect progression labels, limited external validation, radiomic feature instability, calibration, missing data, uncertainty quantification, and the need for transparent human-AI interaction. This narrative review summarizes PRECISE v2, its relationship with PI-RADS and PI-QUAL, and emerging strategies to combine PRECISE-aligned imaging change with longitudinal clinical and biomarker information for risk-adapted decision support. Serial mpMRI can support AS when image quality and serial reporting are standardized; however, MRI should complement rather than replace biopsy, and AI-assisted pathways require AS-specific external validation, calibration, harmonization, and prospective evaluation before they can be used to modify biopsy timing.

Indexed as

Active surveillanceArtificial intelligenceMagnetic resonance imagingProstatic neoplasmsRadiomicsRisk assessment

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.