Evidence map›Paper›PMID 36057766›Full record

ReviewCancer imaging : the official publication of the International Cancer Imaging Society2022

The use of advanced imaging in guiding the further investigation and treatment of primary prostate cancer.

Heying Duan, Andrei Iagaru

Abstract readReview
In one paragraph

Review in Cancer imaging : the official publication of the International Cancer Imaging Society, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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.

Heying DuanDepartment of Radiology, Division of Nuclear Medicine and Molecular Imaging, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0002-2834-5524
Andrei IagaruDepartment of Radiology, Division of Nuclear Medicine and Molecular Imaging, Stanford University, Stanford, CA, USA. aiagaru@stanford.edu.ORCID http://orcid.org/0000-0003-0839-5329

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the era of precision medicine, oncological imaging techniques are advancing at a rapid pace, particularly molecular imaging with promising new targets for prostate cancer (PC) such as gastrin releasing peptide receptors (GRPR) along the established and indispensable prostate specific membrane antigen (PSMA). As PC is characterized by heterogenous tumor biology ranging from indolent to aggressive disease, distinguishing clinically significant tumors from indolent disease is critical. Multiparametric MRI- and PET-targeted prostate biopsies mitigate the shortcomings and risks of standard systematic template biopsy by identifying more significant cancers.Focal treatment for localized disease is a minimally invasive approach that targets the index tumor - the lesion of the highest grade - while sparing the surrounding healthy tissue. Real-time MRI-guidance and thermal control with MR-thermometry, improves treatment accuracy and results in lower rates of functional side effects. PET imaging could be an useful tool to assess response to treatment compared to invasive prostate biopsies.In this comprehensive review, we focus on the image-guided detection and treatment of localized primary prostate cancer, its current status and future perspectives.

Indexed as

Multiparametric Magnetic Resonance ImagingProstatic NeoplasmsGallium RadioisotopesHumansMagnetic Resonance ImagingMalePositron-Emission TomographyPositron Emission Tomography Computed TomographyProstateGallium Radioisotopes68 Ga-PSMA68 Ga-RM2Image-guidedmpMRIPETProstate Cancer

Identifiers

PMID36057766
PMCPMC9441085

What OpenQuestion holds

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LicenceCC BY
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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.