ArticleTomography (Ann Arbor, Mich.)2019
Habitats in DCE-MRI to Predict Clinically Significant Prostate Cancers.
Article in Tomography (Ann Arbor, Mich.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Radiomics in prostate cancer imaging for a personalized treatment approach - current aspects of methodology and a systematic review on validated studies.Theranostics · 2021Pooled it
- Predictions of Oncotype DXBioengineering (Basel, Switzerland) · 2026Article
- Correlation between contrast-enhanced T2 fluid-attenuated inversion recovery enhancement and dynamic contrast-enhanced magnetic resonance imaging permeability in brain metastases.Quantitative imaging in medicine and surgery · 2026Article
- MRI-based Habitat Imaging for Noninvasive Prediction of High-Grade Prostate Cancer.Radiology. Imaging cancer · 2025Article
- The value of habitat analysis based onBMC medical imaging · 2025Article
- Automatic Characterization of Prostate Suspect Lesions on T2-Weighted Image Acquisitions Using Texture Features and Machine-Learning Methods: A Pilot Study.Diagnostics (Basel, Switzerland) · 2025Article
- Article
- Radiomic Pipelines for Prostate Cancer in External Beam Radiation Therapy: A Review of Methods and Future Directions.Journal of clinical medicine · 2024Review
- MRI-derived radiomics models for diagnosis, aggressiveness, and prognosis evaluation in prostate cancer.Journal of Zhejiang University. Science. B · 2023Review
- Multi-view radiomics and deep learning modeling for prostate cancer detection based on multi-parametric MRI.Frontiers in oncology · 2023Article
- More than Meets the Eye: Using Textural Analysis and Artificial Intelligence as Decision Support Tools in Prostate Cancer Diagnosis-A Systematic Review.Journal of personalized medicine · 2022Review
- Habitat radiomics analysis of pet/ct imaging in high-grade serous ovarian cancer: Application to Ki-67 status and progression-free survival.Frontiers in physiology · 2022Article
- Enhancement degree of brain metastases: correlation analysis between enhanced T2 FLAIR and vascular permeability parameters of dynamic contrast-enhanced MRI.European radiology · 2021Article
- Radiomics Improves Cancer Screening and Early Detection.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2020Review
- Article
- Multisite Technical and Clinical Performance Evaluation of Quantitative Imaging Biomarkers from 3D FDG PET Segmentations of Head and Neck Cancer Images.Tomography (Ann Arbor, Mich.) · 2020Article
- Quantitative Imaging Enters the Clinical Arena: A Personal Viewpoint.Tomography (Ann Arbor, Mich.) · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Prostate cancer identification and assessment of clinical significance continues to be a challenge. Routine multiparametric magnetic resonance imaging has shown to be useful in assessing disease progression. Although dynamic contrast-enhanced imaging (DCE) has the ability to characterize perfusion across time and has shown enormous utility, radiological assessment (Prostate Imaging-Reporting and Data System or PIRADS version 2) has limited its use owing to lack of consistency and nonquantitative nature. In our work, we propose a systematic methodology to quantify perfusion dynamics for the DCE imaging. Using these metrics, 7 different subregions or
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Registered trials
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.