Evidence map›Paper›PMID 31288949›Full record

ArticleGynecologic oncology2019

Low-cost, high-resolution imaging for detecting cervical precancer in medically-underserved areas of Texas.

Sonia G Parra, Ana M Rodriguez, Katelin D Cherry, Richard A Schwarz, Rose M Gowen, Laura B Guerra, Andrea M Milbourne, Paul A Toscano, Susan P Fisher-Hoch, Kathleen M Schmeler and 1 more

Abstract read
In one paragraph

Article in Gynecologic oncology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
–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

12 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Review
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  6. Review
  7. PARP1: A Potential Molecular Marker to Identify Cancer During Colposcopy Procedures.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2021
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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

11 authors.

Sonia G ParraRice University, Department of Bioengineering, Houston, TX 77005, USA.
Ana M RodriguezThe University of Texas Medical Branch, Department of Obstetrics & Gynecology, Galveston, TX 77555, USA.
Katelin D CherryRice University, Department of Bioengineering, Houston, TX 77005, USA.
Richard A SchwarzRice University, Department of Bioengineering, Houston, TX 77005, USA.
Rose M GowenSu Clinica Brownsville, Brownsville, TX 78526, USA.
Laura B GuerraSu Clinica Brownsville, Brownsville, TX 78526, USA.
Andrea M MilbourneThe University of Texas MD Anderson Cancer Center, Department of Gynecologic Oncology and Reproductive Medicine, Houston, TX 77057, USA.
Paul A ToscanoThe University of Texas Health Science Center at Houston, School of Public Health, Brownsville, TX 78520, USA.
Susan P Fisher-HochThe University of Texas Health Science Center at Houston, School of Public Health, Brownsville, TX 78520, USA.
Kathleen M SchmelerThe University of Texas MD Anderson Cancer Center, Department of Gynecologic Oncology and Reproductive Medicine, Houston, TX 77057, USA.
Rebecca R Richards-KortumRice University, Department of Bioengineering, Houston, TX 77005, USA. Electronic address: rkortum@rice.edu.

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Fabien Delerue · 1985 to 2026
$290.8M
High Resolution Imaging & HPV Oncoprotein Detection for Global Prevention of Cervical CancerR01CA186132 · NCI · RICE UNIVERSITY · PI RICHARDS-KORTUM, REBECCA R. · 2014 to 2018
$3.1M
NCI NIH HHS P30 CA016672NCI NIH HHS R01 CA186132
6 · The paper itself

Abstract

objectiveCervical cancer rates in the United States have declined since the 1940's, however, cervical cancer incidence remains elevated in medically-underserved areas, especially in the Rio Grande Valley (RGV) along the Texas-Mexico border. High-resolution microendoscopy (HRME) is a low-cost, in vivo imaging technique that can identify high-grade precancerous cervical lesions (CIN2+) at the point-of-care. The goal of this study was to evaluate the performance of HRME in medically-underserved areas in Texas, comparing results to a tertiary academic medical center.

methodsHRME was evaluated in five different outpatient clinical settings, two in Houston and three in the RGV, with medical providers of varying skill and training. Colposcopy, followed by HRME imaging, was performed on eligible women. The sensitivity and specificity of traditional colposcopy and colposcopy followed by HRME to detect CIN2+ were compared and HRME image quality was evaluated.

results174 women (227 cervical sites) were included in the final analysis, with 12% (11% of cervical sites) diagnosed with CIN2+ on histopathology. On a per-site basis, a colposcopic impression of low-grade precancer or greater had a sensitivity of 84% and a specificity of 45% to detect CIN2+. While there was no significant difference in sensitivity (76%, p = 0.62), the specificity when using HRME was significantly higher than that of traditional colposcopy (56%, p = 0.01). There was no significant difference in HRME image quality between clinical sites (p = 0.77) or medical providers (p = 0.33).

conclusionsHRME imaging increased the specificity for detecting CIN2+ when compared to traditional colposcopy. HRME image quality remained consistent across different clinical settings.

Indexed as

Medically Underserved AreaColonoscopyFemaleHumansImage Processing, Computer-AssistedNeoplasm GradingPrecancerous ConditionsProspective StudiesSensitivity and SpecificityTexasUnited StatesUterine Cervical DysplasiaUterine Cervical NeoplasmsCervical cancerCervical cancer preventionCervical dysplasiaColposcopyHigh-resolution cervical imaging

Identifiers

PMID31288949
PMCPMC6941476

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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.