Evidence map›Paper›PMID 30995626›Full record

ArticlePhysics in medicine and biology2019

Localization of liver lesions in abdominal CT imaging: II. Mathematical model observer performance correlates with human observer performance for localization of liver lesions in abdominal CT imaging.

Samantha K N Dilger, Shuai Leng, Baiyu Chen, Rickey E Carter, Chris P Favazza, Joel G Fletcher, Cynthia H McCollough, Lifeng Yu

Abstract read
In one paragraph

Article in Physics in medicine and biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.0field-weighted citation impact, top 23% 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

8 citing papers in PubMed, 11 citations in OpenAlex.

  1. Article
  2. Article
  3. Automated Web-based Software for CT Quality Control Testing of Low-contrast Detectability using Model Observers.Proceedings of SPIE--the International Society for Optical Engineering · 2024
    Article
  4. Article
  5. Article
  6. Article
  7. A Web-Based Software Platform for Efficient and Quantitative CT Image Quality Assessment and Protocol Optimization.Proceedings of SPIE--the International Society for Optical Engineering · 2021
    Article
  8. Article
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

8 authors at 3 institutions in 1 country.

Samantha K N DilgerDepartment of Radiology, Mayo Clinic, Rochester, MN, United States of America.ORCID 0000-0002-3524-1723
Shuai LengDepartment of Radiology, Mayo Clinic, Rochester, MN, United States of America.
Baiyu ChenDepartment of Radiology, Mayo Clinic, Rochester, MN, United States of America.
Rickey E CarterDepartment of Biostatistics, Mayo Clinic, Rochester, MN, United States of America.
Chris P FavazzaDepartment of Radiology, Mayo Clinic, Rochester, MN, United States of America.
Joel G FletcherDepartment of Radiology, Mayo Clinic, Rochester, MN, United States of America.
Cynthia H McColloughDepartment of Radiology, Mayo Clinic, Rochester, MN, United States of America.
Lifeng YuDepartment of Radiology, Mayo Clinic, Rochester, MN, United States of America.
Mayo Clinic in Florida · USMayo Clinic · USNYU Langone Health · US

Funding

Trade-offs in human observer performance, image quality metrics, and patient doseR01EB017095 · NIBIB · MAYO CLINIC ROCHESTER · PI Hao Gong, Cynthia H McCollough · 2012 to 2026
$7.0M
Critical resources to evaluate CT scan techniques and dose reduction approachesU01EB017185 · NIBIB · MAYO CLINIC ROCHESTER · PI MCCOLLOUGH, CYNTHIA H · 2013 to 2016
$3.6M
NIBIB NIH HHS R01 EB017095NIBIB NIH HHS U01 EB017185
6 · The paper itself

Abstract

Determination of the effect of protocol modifications on diagnostic performance in CT with human observers is extremely time-consuming, limiting the applicability of such methods in routine clinical practice. In this work, we sought to determine whether a channelized Hotelling observer (CHO) could predict human observer performance for the task of liver lesion localization as background, reconstruction algorithm, dose, and lesion size were varied. Liver lesions (5 mm, 7 mm, and 9 mm) were digitally inserted into the CT projection data of patients with normal livers and water phantoms. The projection data were reconstructed with filtered back projection (FBP) and iterative reconstruction (IR) algorithms for three dose levels: full dose (liver CTDIvol  =  10.5  ±  8.5 mGy, water phantom CTDIvol  =  9.6  ±  0.1 mGy) and simulated half and quarter doses. For each of 36 datasets (3 dose levels  ×  2 reconstruction algorithms  ×  2 backgrounds  ×  3 sizes), 66 signal-present and 34 signal-absent 2D images were extracted from the reconstructed volumes. Three medical physicists independently reviewed each dataset and noted the lesion location and a confidence score for each image. A CHO with Gabor channels was calculated to estimate the performance for each of the 36 localization tasks. The CHO performances, quantified using localization receiver operating characteristic (LROC) analysis, were compared to the human observer performances. Performance values between human and model observers were highly correlated for equivalent parameters (same lesion size, dose, background, and reconstruction), with a Spearman's correlation coefficient of 0.93 (95% CI: 0.82-0.98). CHO performance values for the uniform background were strongly correlated (ρ  =  0.94, CI: 0.80-1.0) with the human observer performance values for the liver background. Performance values between human observers and CHO were highly correlated as dose, reconstruction type and object size were varied for the task of localization of patient liver lesions in both uniform and liver backgrounds.

Indexed as

Models, TheoreticalObserver VariationPhantoms, ImagingAlgorithmsHumansImage Processing, Computer-AssistedLiver NeoplasmsRadiation DosageRadiographic Image Interpretation, Computer-AssistedRadiography, AbdominalROC CurveTomography, X-Ray Computed

Identifiers

PMID30995626
PMCPMC6598689
OpenAlexW2939018358

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.