Evidence map›Paper›PMID 22180839›Full record

ArticleCancers2010

Exogenous Molecular Probes for Targeted Imaging in Cancer: Focus on Multi-modal Imaging.

Bishnu P Joshi, Thomas D Wang

Open access · goldAbstract read
In one paragraph

Article in Cancers, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed
5.6field-weighted citation impact, top 4% 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

33 citing papers in PubMed, 92 citations in OpenAlex.

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  3. Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A Review.IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society · 2025
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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 at 1 institution in 1 country.

Bishnu P JoshiDivision of Gastroenterology, Department of Internal Medicine, University of Michigan, School of Medicine, 109 Zina Pitcher Place, BSRB 1722, Ann Arbor, MI, 48109, USA.
Thomas D Wang
University of Michigan–Ann Arbor · US

Funding

MICHIGAN INSTITUTE FOR CLINICAL AND HEALTH RESEARCH (MICHR) (UL1): (BPCA)UL1RR024986 · NCRR · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI SHANLEY, THOMAS P. · 2007 to 2011
$51.2M
Task Specific Project 4U54CA136429 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI REHEMTULLA, ALNAWAZ · 2008 to 2012
$5.2M
NCI NIH HHS U54 CA136429NCRR NIH HHS UL1 RR024986
6 · The paper itself

Abstract

Cancer is one of the major causes of mortality and morbidity in our health care system. Molecular imaging is an emerging methodology for the early detection of cancer, and the development of exogenous molecular probes that can be labeled for multi-modality imaging is critical to this process. Today, molecular imaging is at crossroad, and new targeted imaging agents are expected to broadly expand our ability to detect pre-malignant lesions. This integrated imaging strategy will permit clinicians to not only localize lesions within the body, but also to visualize the expression and activity of specific molecules. This information is expected to have a major impact on diagnosis, therapy, drug development and understanding of basic cancer biology. At this time, a number of molecular probes have been developed by conjugating various labels to affinity ligands for targeting in different imaging modalities. This review will describe the current status of exogenous molecular probes for optical, nuclear and MRI imaging platforms. Furthermore, we will also shed light on how these techniques can be used synergistically in multi-modal platforms and how these techniques are being employed in current research.

Identifiers

PMID22180839
PMCPMC3237638
OpenAlexW2044161841

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.