Evidence map›Paper›PMID 30456585›Full record

ArticleEuropean radiology2019

Multi-parametric MRI zone-specific diagnostic model performance compared with experienced radiologists for detection of prostate cancer.

Nikolaos Dikaios, Francesco Giganti, Harbir S Sidhu, Edward W Johnston, Mrishta B Appayya, Lucy Simmons, Alex Freeman, Hashim U Ahmed, David Atkinson, Shonit Punwani

Open access · hybridAbstract readComparative StudyEvaluation Study

PubMed Full text at PMC Publisher

In one paragraph

Article in European radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it

0cells of the map it votes in

6citing papers in PubMed, 1 pooled it

1.1field-weighted citation impact, top 20% of its field

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

6 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.

Factors Influencing Variability in the Performance of Multiparametric Magnetic Resonance Imaging in Detecting Clinically Significant Prostate Cancer: A Systematic Literature Review.European urology oncology · 2020
Pooled it
MR Virtual Cytometry for Zone-specific Prostate Cancer Characterization with Histologic and Molecular Validation.Radiology. Imaging cancer · 2026
Article
External Validation of a Previously Developed Deep Learning-based Prostate Lesion Detection Algorithm on Paired External and In-House Biparametric MRI Scans.Radiology. Imaging cancer · 2024
Article
A radiomics based method for prediction of prostate cancer Gleason score using enlarged region of interest.BMC medical imaging · 2023
Article
Identification of prostate cancer using multiparametric MR imaging characteristics of prostate tissues referenced to whole mount histopathology.Magnetic resonance imaging · 2022
Article
Predicting clinically significant prostate cancer from quantitative image features including compressed sensing radial MRI of prostate perfusion using machine learning: comparison with PI-RADS v2 assessment scores.Quantitative imaging in medicine and surgery · 2020
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

10 authors at 4 institutions in 1 country.

Nikolaos DikaiosCentre for Medical Imaging, University College London, 2nd floor, Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK.

Francesco GigantiDepartments of Radiology, University College London Hospital, 235 Euston Road, London, NW1 2BU, UK.

Harbir S SidhuCentre for Medical Imaging, University College London, 2nd floor, Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK.

Edward W JohnstonCentre for Medical Imaging, University College London, 2nd floor, Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK.

Mrishta B AppayyaCentre for Medical Imaging, University College London, 2nd floor, Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK.

Lucy SimmonsResearch Department of Urology, Division of Surgery and Interventional Science, University College London, London, NW1 2PG, UK.

Alex FreemanDepartment of Histopathology, University College London Hospital, London, NW1 2PG, UK.

Hashim U AhmedDepartment of Surgery and Cancer, Imperial College London, London, UK.

David AtkinsonCentre for Medical Imaging, University College London, 2nd floor, Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK.

Shonit PunwaniCentre for Medical Imaging, University College London, 2nd floor, Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK. shonit.punwani@gmail.com.ORCID http://orcid.org/0000-0001-9865-0260

University College Hospital · GBUniversity College London · GBImperial College London · GBUniversity of Surrey · GB

Funding

Cancer Research UK 16463

6 · The paper itself

Abstract

objectivesCompare the performance of zone-specific multi-parametric-MRI (mp-MRI) diagnostic models in prostate cancer detection with experienced radiologists.

methodsA single-centre, IRB approved, prospective STARD compliant 3 T MRI test dataset of 203 patients was generated to test validity and generalisability of previously reported 1.5 T mp-MRI diagnostic models. All patients included within the test dataset underwent 3 T mp-MRI, comprising T2, diffusion-weighted and dynamic contrast-enhanced imaging followed by transperineal template ± targeted index lesion biopsy. Separate diagnostic models (transition zone (TZ) and peripheral zone (PZ)) were applied to respective zones. Sensitivity/specificity and the area under the receiver operating characteristic curve (ROC-AUC) were calculated for the two zone-specific models. Two radiologists (A and B) independently Likert scored test 3 T mp-MRI dataset, allowing ROC analysis for each radiologist for each prostate zone.

resultsDiagnostic models applied to the test dataset demonstrated a ROC-AUC = 0.74 (95% CI 0.67-0.81) in the PZ and 0.68 (95% CI 0.61-0.75) in the TZ. Radiologist A/B had a ROC-AUC = 0.78/0.74 in the PZ and 0.69/0.69 in the TZ. Radiologists A and B each scored 51 patients in the PZ and 41 and 45 patients respectively in the TZ as Likert 3. The PZ model demonstrated a ROC-AUC = 0.65/0.67 for the patients Likert scored as indeterminate by radiologist A/B respectively, whereas the TZ model demonstrated a ROC-AUC = 0.74/0.69.

conclusionZone-specific mp-MRI diagnostic models demonstrate generalisability between 1.5 and 3 T mp-MRI protocols and show similar classification performance to experienced radiologists for prostate cancer detection. Results also indicate the ability of diagnostic models to classify cases with an indeterminate radiologist score. KEY POINTS: • MRI diagnostic models had similar performance to experienced radiologists for classification of prostate cancer. • MRI diagnostic models may help radiologists classify tumour in patients with indeterminate Likert 3 scores.

Indexed as

AgedAged, 80 and overBiopsyClinical CompetenceHumansLiverMagnetic Resonance ImagingMaleMiddle AgedProspective StudiesProstatic NeoplasmsRadiologistsROC CurveSensitivity and SpecificityComputer-assistedDiagnosisLogistic modelsMagnetic resonance imagingProstatic neoplasms

Identifiers

PMID30456585

DOI10.1007/s00330-018-5799-y

PMCPMC6610264

OpenAlexW2901579968

What OpenQuestion holds

Textmetadata

LicenceCC BY

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.