Evidence map›Paper›PMID 30730533›Full record

ArticleJAMA facial plastic surgery2019

Comparison of Micro-Computed Tomography and Clinical Computed Tomography Protocols for Visualization of Nasal Cartilage Before Surgical Planning for Rhinoplasty.

Rajeev C Saxena, Seth Friedman, Randall A Bly, Jeffrey Otjen, Adam M Alessio, Yangming Li, Blake Hannaford, Mark Whipple, Kris S Moe

Open access · greenAbstract readComparative Study
In one paragraph

Article in JAMA facial plastic surgery, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.2field-weighted citation impact, top 13% 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

7 citing papers in PubMed, 17 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Deep causal learning for robotic intelligence.Frontiers in neurorobotics · 2023
    Review
  6. [Analysis of clinical effect of nasal endoscope-assisted nasal columella approach for simultaneous correction of nasal septum deviation and crooked nose deformity].Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery · 2021
    Article
  7. 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

9 authors at 2 institutions in 1 country.

Rajeev C SaxenaDepartment of Otolaryngology-Head and Neck Surgery, University of Washington, Seattle.
Seth FriedmanDepartment of Radiology, Seattle Children's Hospital, Seattle, Washington.
Randall A BlyDepartment of Otolaryngology-Head and Neck Surgery, Seattle Children's Hospital, Seattle, Washington.
Jeffrey OtjenDepartment of Radiology, Seattle Children's Hospital, Seattle, Washington.
Adam M AlessioDepartment of Radiology, Seattle Children's Hospital, Seattle, Washington.
Yangming LiDepartment of Electrical Engineering, University of Washington, Seattle.
Blake HannafordDepartment of Electrical Engineering, University of Washington, Seattle.
Mark WhippleDepartment of Otolaryngology-Head and Neck Surgery, University of Washington, Seattle.
Kris S MoeDepartment of Otolaryngology-Head and Neck Surgery, University of Washington, Seattle.
University of Washington · USSeattle Children's Hospital · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

importanceThere is no imaging standard to model nasal cartilage for the planning of rhinoplasty procedures. Preoperative visualization of cartilage may improve objective evaluation of nasal deformities, surgical planning, and surgical reconstruction.

objectivesTo evaluate the feasibility of visualizing nasal cartilage using high resolution micro-computed tomography (CT) compared with the criterion standard of pathologic findings in a cadaveric specimen and to evaluate its accuracy compared with various clinical CT protocols. DESIGN, SETTING, AND

participantsAnatomic study at the University of Washington using single human cadaveric nasal specimens performed from July 10, 2017, to March 30, 2018.

interventionsA micro-CT acquisition with 60-micron resolution was obtained of a nasal specimen. The specimen was then scanned with 5 different clinical CT protocols to span both clinical care and machine limits. The specimen was then sectioned in 5-mm axial slices for pathologic analysis. MAIN OUTCOMES AND MEASURES: Micro-CT images were registered to pathologic specimen cross-sections using a graphite fiducial system. Cartilage substructures were manually segmented and analyzed. A library of matched images across the micro-CT and various clinical CT protocols was then developed. Region of interest analysis was performed for each of the cartilage structures and their boundaries on clinical CT protocols and micro-CT, with the outcome of mean (SD) density using Hounsfield units.

resultsA single human cadaveric nasal specimen was used to obtain the following results. Lower lateral cartilage, upper lateral cartilage, and septal cartilage were accurately delineated on the micro-CT images compared with pathologic findings. The mean absolute deviation from pathologic findings was 0.30 mm for septal cartilage thickness, 0.98 mm for maximal upper lateral cartilage length, and 1.40 mm for maximal lower lateral cartilage length. On clinical CT protocols, only septal cartilage was well discriminated from boundary. Higher radiation dose resulted in more accurate density measurements of cartilage, but it did not ultimately improve ability to discriminate cartilage. CONCLUSIONS AND RELEVANCE: The results of this anatomic study may represent a notable step toward advancing knowledge of the capabilities and pitfalls of nasal cartilage visualization on CT. Nasal cartilage visualization was feasible on the micro-CT compared with pathologic findings. Future research may further examine the barriers to accurately visualizing upper lateral cartilage and lower lateral cartilage, a prerequisite for clinical application. LEVEL OF EVIDENCE: NA.

Indexed as

RhinoplastyCadaverFeasibility StudiesHumansNasal CartilagesTomography, X-Ray ComputedX-Ray Microtomography

Identifiers

PMID30730533
PMCPMC6537836
OpenAlexW2914900956

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Registered trials

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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.