Evidence map›Paper›PMID 15249663›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2004

A rare-cell detector for cancer.

Robert T Krivacic, Andras Ladanyi, Douglas N Curry, H B Hsieh, Peter Kuhn, Danielle E Bergsrud, Jane F Kepros, Todd Barbera, Michael Y Ho, Lan Bo Chen and 2 more

Open access · greenAbstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2004. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 138 papers.

0numbers the graph read from it
0cells of the map it votes in
138citing papers in PubMed
1.7field-weighted citation impact, top 17% 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

138 citing papers in PubMed, 340 citations in OpenAlex.

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  9. Recent Advances in Methods for Circulating Tumor Cell Detection.International journal of molecular sciences · 2023
    Review
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  13. Frontiers in immunology · 2022
    Review
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78 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

12 authors at 2 institutions in 1 country.

Robert T KrivacicScripps-PARC Institute for Advanced Biomedical Science, Palo Alto Research Center, 3333 Coyote Hill Road, Palo Alto, CA 94304, USA.
Andras Ladanyi
Douglas N Curry
H B Hsieh
Peter Kuhn
Danielle E Bergsrud
Jane F Kepros
Todd Barbera
Michael Y Ho
Lan Bo Chen
Richard A Lerner
Richard H Bruce
Scripps Research Institute · USPalo Alto Research Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Although a reliable method for detection of cancer cells in blood would be an important tool for diagnosis and monitoring of solid tumors in early stages, current technologies cannot reliably detect the extremely low concentrations of these rare cells. The preferred method of detection, automated digital microscopy (ADM), is too slow to scan the large substrate areas. Here we report an approach that uses fiber-optic array scanning technology (FAST), which applies laser-printing techniques to the rare-cell detection problem. With FAST cytometry, laser-printing optics are used to excite 300,000 cells per sec, and emission is collected in an extremely wide field of view, enabling a 500-fold speed-up over ADM with comparable sensitivity and superior specificity. The combination of FAST enrichment and ADM imaging has the performance required for reliable detection of early-stage cancer in blood.

Indexed as

Fiber Optic TechnologyBiomarkers, TumorCytophotometryHT29 CellsHumansMass ScreeningNeoplasmsNeoplastic Cells, CirculatingOptical FibersSensitivity and SpecificityBiomarkers, Tumor

Identifiers

PMID15249663
PMCPMC489966
OpenAlexW2057306577

What OpenQuestion holds

Textmetadata
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.