Evidence map›Paper›PMID 38497678›Full record

ArticleCancer research communications2024

High-resolution Diffusion-weighted Imaging to Detect Changes in Tumor Size and ADC, and Predict Adverse Biopsy Histology during Prostate Cancer Active Surveillance.

Rola Saouaf, Yibin Xie, Sungjin Kim, Yaniv Raphael, Christopher Nguyen, Daniel Luthringer, Timothy J Daskivich, Eric Lo, Mourad Tighiouart, Debiao Li and 1 more

Open access · goldAbstract read
In one paragraph

Article in Cancer research communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 94% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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

11 authors at 2 institutions in 1 country.

Rola SaouafDepartment of Imaging, Cedars Sinai Medical Center, Los Angeles, California.ORCID 0000-0003-0464-3786
Yibin XieBiomedical Imaging Research Institute, Cedars Sinai Medical Center, Los Angeles, California.ORCID 0000-0002-0333-567X
Sungjin KimCedars Sinai Medical Center, Biostatistics and Bioinformatics Research Center, Los Angeles, California.ORCID 0000-0002-1150-703X
Yaniv RaphaelDepartment of Imaging, Cedars Sinai Medical Center, Los Angeles, California.ORCID 0009-0008-9426-829X
Christopher NguyenCardiovascular Innovation Research Center, Heart Vascular Thoracic Institute, Cleveland Clinic, Cleveland, Ohio.ORCID 0000-0003-1475-2329
Daniel LuthringerDepartment of Pathology Cedars Sinai Medical Center, Los Angeles, California.ORCID 0000-0001-8189-0540
Timothy J DaskivichDepartment of Urology, Cedars Sinai Medical Center, Los Angels, California.ORCID 0000-0002-7540-3272
Eric LoDepartment of Urology, Cedars Sinai Medical Center, Los Angels, California.ORCID 0000-0001-7118-0852
Mourad TighiouartCedars Sinai Medical Center, Biostatistics and Bioinformatics Research Center, Los Angeles, California.ORCID 0000-0001-8021-5690
Debiao Li *Biomedical Imaging Research Institute, Cedars Sinai Medical Center, Los Angeles, California.ORCID 0000-0001-8560-8231
Hyung L Kim *Department of Urology, Cedars Sinai Medical Center, Los Angels, California.ORCID 0000-0003-2085-6140
Cedars-Sinai Medical Center · USCleveland Clinic · US

Funding

High resolution volumetric MRI for prostate cancer active surveillanceR01CA217098 · NCI · CEDARS-SINAI MEDICAL CENTER · PI KIM, HYUNG L, LI, DEBIAO · 2017 to 2021
$1.9M
NCI NIH HHS R01 CA217098
6 · The paper itself

Abstract

purposeMajority of men with low-risk prostate cancer can be managed with active surveillance (AS). This study evaluates a high-resolution diffusion-weighted imaging (HR-DWI) technique to predict adverse biopsy histology (AH), defined as Gleason score ≥7 on any biopsy or ≥3 increase in number of positive biopsy cores on systematic biopsies. We test the hypothesis that high-grade disease and progressing disease undergo subtle changes during even short intervals that can be detected by HR-DWI. EXPERIMENTAL

designIn a prospective clinical trial, serial multiparametric MRIs, incorporating HR-DWI and standard DWI (S-DWI) were performed approximately 12 months apart prior to prostate biopsy (n = 59). HR-DWI, which uses reduced field-of-view and motion compensation techniques, was compared with S-DWI.

resultsHR-DWI had a 3-fold improvement in spacial resolution compared with S-DWI as confirmed using imaging phantoms. For detecting AH, multiparametric MRI using HR-DWI had a sensitivity of 75% and specificity of 83.9%, and MRI using S-DWI had a sensitivity of 71.4% and specificity of 54.8%. The AUC for HR-DWI was significantly higher (0.794 vs. 0.631, P = 0.014). Secondary analyses of univariable predictors of AH showed tumor size increase [OR 16.8; 95% confidence interval (CI): 4.06-69.48; P < 0.001] and apparent diffusion coefficient (ADC) decrease (OR 5.06; 95% CI: 1.39-18.38; P = 0.014) on HR-DWI were significant predictors of AH.

conclusionHR-DWI outperforms S-DWI in predicting AH. Patient with AH have tumors that change in size and ADC that could be detected using HR-DWI. Future studies with longer follow-up should assess HR-DWI for predicting disease progression during AS. SIGNIFICANCE: We report on a prospective clinical trial using a MRI that has three times the resolution of standard MRI. During AS for prostate cancer, two high-resolution MRIs performed approximately a year apart can detect tumor changes that predict the presence of aggressive cancers that should be considered for curative therapy such as prostatectomy or radiation.

Indexed as

Prostatic NeoplasmsWatchful WaitingBiopsyDiffusion Magnetic Resonance ImagingHumansMaleProspective Studies

Identifiers

PMID38497678
PMCPMC10967695
OpenAlexW4392911833

What OpenQuestion holds

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LicenceCC BY
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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.