Evidence map›Paper›PMID 40445307›Full record

ArticleAbdominal radiology (New York)2025

Using AI to triage patients without clinically significant prostate cancer using biparametric MRI and PSA.

Emerson P Grabke, Carolina A M Heming, Amit Hadari, Antonio Finelli, Sangeet Ghai, Katherine Lajkosz, Babak Taati, Masoom A Haider

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Article in Abdominal radiology (New York), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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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

2 citing papers in PubMed.

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4 · The record

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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.

Emerson P GrabkeLunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Canada.
Carolina A M HemingLunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Canada.
Amit HadariLunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Canada.
Antonio FinelliDivision of Urology, Department of Surgical Oncology, Princess Margaret Cancer Center, Toronto, Canada.
Sangeet GhaiJoint Department of Medical Imaging, Sinai Health System, University Health Network, University of Toronto, Toronto, Canada.
Katherine LajkoszDepartment of Biostatistics, University Health Network, Toronto, Canada.
Babak TaatiKITE Research Institute, University Health Network, Toronto, Canada.
Masoom A HaiderLunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Canada. m.haider@utoronto.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo train and evaluate the performance of a machine learning triaging tool that identifies MRI negative for clinically significant prostate cancer and to compare this against non-MRI models.

methods2895 MRIs were collected from two sources (1630 internal, 1265 public) in this retrospective study. Risk models compared were: Prostate Cancer Prevention Trial Risk Calculator 2.0, Prostate Biopsy Collaborative Group Calculator, PSA density, U-Net segmentation, and U-Net combined with clinical parameters. The reference standard was histopathology or negative follow-up. Performance metrics were calculated by simulating a triaging workflow compared to radiologist interpreting all exams on a test set of 465 patients. Sensitivity and specificity differences were assessed using the McNemar test. Differences in PPV and NPV were assessed using the Leisenring, Alonzo and Pepe generalized score statistic. Equivalence test p-values were adjusted within each measure using Benjamini-Hochberg correction.

resultsTriaging using U-Net with clinical parameters reduced radiologist workload by 12.5% with sensitivity decrease from 93 to 90% (p = 0.023) and specificity increase from 39 to 47% (p < 0.001). This simulated workload reduction was greater than triaging with risk calculators (3.2% and 1.3%, p < 0.001), and comparable to PSA density (8.4%, p = 0.071) and U-Net alone (11.6%, p = 0.762). Both U-Net triaging strategies increased PPV (+ 2.8% p = 0.005 clinical, + 2.2% p = 0.020 nonclinical), unlike non-U-Net strategies (p > 0.05). NPV remained equivalent for all scenarios (p > 0.05). Clinically-informed U-Net triaging correctly ruled out 20 (13.4%) radiologist false positives (12 PI-RADS = 3, 8 PI-RADS = 4). Of the eight (3.6%) false negatives, two were misclassified by the radiologist. No misclassified case was interpreted as PI-RADS 5.

conclusionsProstate MRI triaging using machine learning could reduce radiologist workload by 12.5% with a 3% sensitivity decrease and 8% specificity increase, outperforming triaging using non-imaging-based risk models. Further prospective validation is required.

Indexed as

Artificial IntelligenceMagnetic Resonance ImagingProstate-Specific AntigenProstatic NeoplasmsAdultAgedAged, 80 and overHumansMachine LearningMaleMedical InformaticsMiddle AgedPrecision MedicinePredictive Learning ModelsRetrospective StudiesProstate-Specific AntigenArtificial intelligenceMagnetic resonance imagingProstate neoplasmsWorkflow triage

Identifiers

PMID40445307

What Socratic holds

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Registered trials

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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 Socratic graph.