Evidence map›Paper›PMID 30311988›Full record

ReviewJournal of magnetic resonance imaging : JMRI2019

Diffusion MRI of cancer: From low to high b-values.

Lei Tang, Xiaohong Joe Zhou

2 registry-linked trialsAbstract readReview
In one paragraph

Review in Journal of magnetic resonance imaging : JMRI, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 121 papers.

0numbers the graph read from it
0cells of the map it votes in
121citing 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.

NCT05298722 narecruitingnot on this mapstarted 2022, after this paper: background citation

Prediction of Surgical Resectability After FOLFIRINOX Chemotherapy for Borderline Resectable and Locally Advanced Pancreatic Cancer: the Role of Diffusion Weighted Magnetic Resonance Imaging, Radiomics and Liquid Biopsy

TypeinterventionalSponsorUniversity Hospital, GhentRan2022 to 2028Enrolled45ConditionsPancreatic CancerArmsImaging analysis of CT-scan and MRI with radiomics and genetic analysis of peripheral blood samples and questionnaires.
NCT05804643 recruitingnot on this mapstarted 2023, after this paper: background citation

Development and Optimization of Novel MRI Techniques on Evaluation of Therapeutic Effects for Lymphedema Secondary to Breast Cancer Treatment: An Integrated Study of Pre-clinical Animal Models and Clinical Follow-up

TypeobservationalSponsorNational Taiwan University HospitalRan2023 to 2026Enrolled210ConditionsLymphedema of Upper LimbArmsDiffusion magnetic resonance imaging
3 · Its place in the literature

Who cites it

121 citing papers in PubMed.

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61 more citing papers are in PubMed but not listed here.

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

2 authors.

Lei TangDepartment of Radiology, Peking University Cancer Hospital & Institute, Key laboratory of Carcinogenesis and Translational Research, Beijing, China.ORCID 0000-0002-8406-9843
Xiaohong Joe ZhouCenter for MR Research and Departments of Radiology, Neurosurgery, and Bioengineering, University of Illinois at Chicago, Chicago, Illinois, USA.ORCID 0000-0003-0793-4925

Funding

A High-End 3 Tesla Human MRI Scanner Dedicated to ResearchS10RR028898 · NCRR · UNIVERSITY OF ILLINOIS AT CHICAGO · PI ZHOU, XIAOHONG JOE · 2010 to 2010
$2.9M
Probing Intra-voxel Tissue Heterogeneity Using MRIR01EB026716 · NIBIB · UNIVERSITY OF ILLINOIS AT CHICAGO · PI ZHOU, XIAOHONG JOE · 2018 to 2022
$1.7M
NCRR NIH HHS S10 RR028898NIBIB NIH HHS R01 EB026716
6 · The paper itself

Abstract

Following its success in early detection of cerebral ischemia, diffusion-weighted imaging (DWI) has been increasingly used in cancer diagnosis and treatment evaluation. These applications are propelled by the rapid development of novel diffusion models to extract biologically valuable information from diffusion-weighted MR signals, and significant advances in MR hardware that has enabled image acquisition with high b-values. This article reviews recent technical developments and clinical applications in cancer imaging using DWI, with a special emphasis on high b-value diffusion models. The article is organized in four sections. First, we provide an overview of diffusion models that are relevant to cancer imaging. The model parameters are discussed in relation to three tissue properties-cellularity, vascularity, and microstructures. An emphasis is placed on characterization of microstructural heterogeneity, given its novelty and close relevance to cancer. Second, we illustrate diffusion MR clinical applications in each of the following three categories: 1) cancer detection and diagnosis; 2) cancer grading, staging, and classification; and 3) cancer treatment response prediction and evaluation. Third, we discuss several practical issues, including selection of image acquisition parameters, reproducibility and reliability, motion management, image distortion, etc., that are commonly encountered when applying DWI to cancer in clinical settings. Lastly, we highlight a few ongoing challenges and provide some possible future directions, particularly in the area of establishing standards via well-organized multicenter clinical trials to accelerate clinical translation of advanced DWI techniques to improving cancer care on a large scale. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:23-40.

Indexed as

Diffusion Magnetic Resonance ImagingDiffusionEarly Detection of CancerHumansImage Interpretation, Computer-AssistedImage Processing, Computer-AssistedMotionNeoplasm MetastasisNeoplasmsNormal DistributionReproducibility of Results

Identifiers

PMID30311988
PMCPMC6298843

What OpenQuestion holds

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Read underepoch 390

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.