Evidence map›Paper›PMID 39545376›Full record

ArticleCancer control : journal of the Moffitt Cancer Center

Quantitative DCE Dynamics on Transformed MR Imaging Discriminates Clinically Significant Prostate Cancer.

Zhouping Wei, Malinda Iluppangama, Jin Qi, Jung W Choi, Alice Yu, Kenneth Gage, Vaibhav Chumbalkar, Jasreman Dhilon, K C Balaji, Satish Venkataperumal and 7 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancer control : journal of the Moffitt Cancer Center. 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. Data Driven Stochastic Model for Detecting Patients with Alzheimer's Disease.medRxiv : the preprint server for health sciences · 2026
    Article
  2. 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

17 authors.

Zhouping WeiDepartment of Machine Learning, Moffitt Cancer Center, Tampa, FL, USA.
Malinda IluppangamaDepartment of Machine Learning, Moffitt Cancer Center, Tampa, FL, USA.
Jin QiDepartment of Cancer Physiology, Moffitt Cancer Center, Tampa, FL, USA.
Jung W ChoiDepartment of Diagnostic & Interventional Radiology, Moffitt Cancer Center, Tampa, FL, USA.
Alice YuDepartment of Genitourinary Cancer, Moffitt Cancer Center, Tampa, FL, USA.ORCID 0000-0001-9815-3619
Kenneth GageDepartment of Diagnostic & Interventional Radiology, Moffitt Cancer Center, Tampa, FL, USA.
Vaibhav ChumbalkarDepartment of Pathology, Moffitt Cancer Center, Tampa, FL, USA.
Jasreman DhilonDepartment of Pathology, Moffitt Cancer Center, Tampa, FL, USA.
K C BalajiDepartment of Urology, University of Florida, Jacksonville, FL, USA.
Satish VenkataperumalDepartment of Radiology, James A Haley Veterans Hospital, Tampa, FL, USA.
David J HernandezDepartment of Urology, University of South Florida Health, Tampa, FL, USA.
Jong ParkDepartment of Population Sciences, Moffitt Cancer Center, Tampa, FL, USA.
Clement YedjouDepartment of Biology, College of Science and Technology, Florida A&M University, Tallahassee, FL, USA.
Richard AloDepartment of Biology, College of Science and Technology, Florida A&M University, Tallahassee, FL, USA.
Robert A GatenbyDepartment of Diagnostic & Interventional Radiology, Moffitt Cancer Center, Tampa, FL, USA.
Julio Pow-SangDepartment of Genitourinary Cancer, Moffitt Cancer Center, Tampa, FL, USA.ORCID 0000-0003-1827-6585
Yoganand BalagurunanthanDepartment of Machine Learning, Moffitt Cancer Center, Tampa, FL, USA.ORCID 0000-0002-5598-4727

Funding

Quantitative Imaging Clinical Validation Center at Moffitt Cancer CenterU01CA200464 · NCI · H. LEE MOFFITT CANCER CTR & RES INST · PI JOHN J HEINE, Matthew B. Schabath · 2016 to 2026
$9.2M
Using Radiogenomics to Noninvasively Predict the Malignant Potential of Intraductal Papillary Mucinous Neoplasms of the Pancreas and Uncover Hidden BiologyR37CA229810 · NCI · H. LEE MOFFITT CANCER CTR & RES INST · PI JEONG, DANIEL, PERMUTH, JENNIFER B · 2019 to 2025
$5.0M
(PQC4) Habitats in Prostate CancerR01CA190105 · NCI · H. LEE MOFFITT CANCER CTR & RES INST · PI BALAGURUNATHAN, YOGANAND, GILLIES, ROBERT J. · 2014 to 2017
$2.8M
NCI NIH HHS R01 CA190105NCI NIH HHS R37 CA229810NCI NIH HHS U01 CA200464
6 · The paper itself

Abstract

Dynamic contrast enhancement (DCE) imaging is a valuable sequence of multiparametric magnetic resonance imaging (mpMRI). A DCE sequence enhances the vasculature and complements T2-weighted (T2W) and Diffusion-weighted imaging (DWI), allowing early detection of prostate cancer. However, DCE assessment has remained primarily qualitative. The study proposes quantifying DCE characteristics (T1W sequences) using six time-dependent metrics computed on feature transformations (306 radiomic features) of abnormal image regions observed over time. We applied our methodology to prostate cancer patients with the DCE MRI images (n = 25) who underwent prostatectomy with confirmed pathological assessment of the disease using Gleason Score. Regions of abnormality were assessed on the T2W MRI, guided using the whole mount pathology. Preliminary analysis finds over six temporal DCE imaging features obtained on different transformations on the imaging regions showed significant differences compared to the indolent counterpart (

Indexed as

Prostatic NeoplasmsAgedContrast MediaHumansMagnetic Resonance ImagingMaleMiddle AgedMultiparametric Magnetic Resonance ImagingNeoplasm GradingProstatectomyContrast MediaDCEhabitatsmachine learningMRIprostate cancerradiomics

Identifiers

PMID39545376
PMCPMC11565616

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.