Evidence map›Paper›PMID 40473563›Full record

ArticleAcademic radiology2025

Impact of AI-Generated ADC Maps on Computer-Aided Diagnosis of Prostate Cancer: A Feasibility Study.

Kutsev B Ozyoruk, Stephanie A Harmon, Enis C Yilmaz, David G Gelikman, Ulas Bagci, Benjamin D Simon, Maria J Merino, Rosina Lis, Sandeep Gurram, Bradford J Wood and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Academic radiology, 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

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

2 citing papers in PubMed.

  1. Toward Precision Imaging: Commentary on the Predictive Value of T2WI and ADC in Prostate Cancer.International braz j urol : official journal of the Brazilian Society of Urology
    Article
  2. First Report on the use of Loop-X Image Acquisition Technology for Sacral Neuromodulation.International braz j urol : official journal of the Brazilian Society of Urology
    Article
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

13 authors.

Kutsev B OzyorukArtificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, Bethesda, Maryland (K.B.O., S.A.H., D.G.G., B.D.S., R.L., P.L.C., B.T.); Molecular Imaging Branch, National Cancer Institute, Bethesda, Maryland (K.B.O., S.A.H., D.G.G., B.D.S., R.L., P.L.C., B.T.).
Stephanie A HarmonArtificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, Bethesda, Maryland (K.B.O., S.A.H., D.G.G., B.D.S., R.L., P.L.C., B.T.); Molecular Imaging Branch, National Cancer Institute, Bethesda, Maryland (K.B.O., S.A.H., D.G.G., B.D.S., R.L., P.L.C., B.T.).
Enis C YilmazTransitional Program, Sinai-Grace Hospital, Detroit Medical Center, Detroit, Michigan (E.C.Y.).
David G GelikmanArtificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, Bethesda, Maryland (K.B.O., S.A.H., D.G.G., B.D.S., R.L., P.L.C., B.T.); Molecular Imaging Branch, National Cancer Institute, Bethesda, Maryland (K.B.O., S.A.H., D.G.G., B.D.S., R.L., P.L.C., B.T.).
Ulas BagciRadiology and Biomedical Engineering Department, Northwestern University Feinberg School of Medicine, Chicago, IL 60611 (U.B.).
Benjamin D SimonArtificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, Bethesda, Maryland (K.B.O., S.A.H., D.G.G., B.D.S., R.L., P.L.C., B.T.); Molecular Imaging Branch, National Cancer Institute, Bethesda, Maryland (K.B.O., S.A.H., D.G.G., B.D.S., R.L., P.L.C., B.T.).
Maria J MerinoLaboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland (M.J.M.).
Rosina LisArtificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, Bethesda, Maryland (K.B.O., S.A.H., D.G.G., B.D.S., R.L., P.L.C., B.T.); Molecular Imaging Branch, National Cancer Institute, Bethesda, Maryland (K.B.O., S.A.H., D.G.G., B.D.S., R.L., P.L.C., B.T.).
Sandeep GurramUrologic Oncology Branch, National Cancer Institute, NIH, Bethesda, Maryland (S.G., P.A.P.).
Bradford J WoodCenter for Interventional Oncology, NIH Clinical Center, National Institutes of Health, Bethesda, Maryland (B.J.W.); Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, Maryland (B.J.W.).
Peter A PintoUrologic Oncology Branch, National Cancer Institute, NIH, Bethesda, Maryland (S.G., P.A.P.).
Peter L ChoykeArtificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, Bethesda, Maryland (K.B.O., S.A.H., D.G.G., B.D.S., R.L., P.L.C., B.T.); Molecular Imaging Branch, National Cancer Institute, Bethesda, Maryland (K.B.O., S.A.H., D.G.G., B.D.S., R.L., P.L.C., B.T.).
Baris TurkbeyArtificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, Bethesda, Maryland (K.B.O., S.A.H., D.G.G., B.D.S., R.L., P.L.C., B.T.); Molecular Imaging Branch, National Cancer Institute, Bethesda, Maryland (K.B.O., S.A.H., D.G.G., B.D.S., R.L., P.L.C., B.T.). Electronic address: turkbeyi@mail.nih.gov.

Funding

CCR Artificial Intelligence ResourceZIHBC012032 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI TÜRKBEY, BARIS · 2021 to 2025
$10.1M
Hybrid Intelligence for Trustable Diagnosis And Patient Management of Prostate Cancer (HIT-PIRADS)U01CA268808 · NCI · NORTHWESTERN UNIVERSITY AT CHICAGO · PI Ulas Bagci · 2023 to 2026
$1.5M
Imaging, image guided biopsy and treatment of prostate cancerZIABC012062 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI TÜRKBEY, BARIS · 2021 to 2025
$1.3M
Intramural NIH HHS ZIA BC012062Intramural NIH HHS ZIH BC012032NCI NIH HHS U01 CA268808
6 · The paper itself

Abstract

RATIONALE AND

objectivesTo evaluate the impact of AI-generated apparent diffusion coefficient (ADC) maps on diagnostic performance of a 3D U-Net AI model for prostate cancer (PCa) detection and segmentation at biparametric MRI (bpMRI). MATERIAL AND

methodsThe study population was retrospectively collected and consisted of 178 patients, including 119 cases and 59 controls. Cases had a mean age of 62.1 years (SD=7.4) and a median prostate-specific antigen (PSA) level of 7.27ng/mL (IQR=5.43-10.55), while controls had a mean age of 63.4 years (SD=7.5) and a median PSA of 6.66ng/mL (IQR=4.29-11.30). All participants underwent 3.0 T T2-weighted turbo spin-echo MRI and high b-value echo-planar diffusion-weighted imaging (bpMRI), followed by either prostate biopsy or radical prostatectomy between January 2013 and December 2022. We compared the lesion detection and segmentation performance of a pretrained 3D U-Net AI model using conventional ADC maps versus AI-generated ADC maps. The Wilcoxon signed-rank test was used for statistical comparison, with 95% confidence intervals (CI) estimated via bootstrapping. A p-value <0.05 was considered significant.

resultsAI-ADC maps increased the accuracy of the lesion detection AI model, from 0.70 to 0.78 (p<0.01). Specificity increased from 0.22 to 0.47 (p<0.001), while maintaining high sensitivity, which was 0.94 with conventional ADC maps and 0.93 with AI-ADC maps (p>0.05). Mean dice similarity coefficients (DSC) for conventional ADC maps was 0.276, while AI-ADC maps showed a mean DSC of 0.225 (p<0.05). In the subset of patients with ISUP≥2, standard ADC maps demonstrated a mean DSC of 0.282 compared to 0.230 for AI-ADC maps (p<0.05).

conclusionAI-generated ADC maps can improve performance of computer-aided diagnosis of prostate cancer.

Indexed as

Artificial IntelligenceDiagnosis, Computer-AssistedDiffusion Magnetic Resonance ImagingImage Interpretation, Computer-AssistedProstatic NeoplasmsAgedFeasibility StudiesHumansMaleMiddle AgedRetrospective StudiesSensitivity and SpecificityAI-ADC mapsArtificial intelligenceComputer-aided diagnosisProstate cancer

Identifiers

PMID40473563
PMCPMC12279456

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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 OpenQuestion graph.