Evidence map›Paper›PMID 37064083›Full record

ArticleProceedings of SPIE--the International Society for Optical Engineering2023

A training program to reduce reader search errors for liver metastasis detection in CT.

Scott S Hsieh, Akitoshi Inoue, Mariana Yalon, David A Cook, Jeff L Fidler, Hao Gong, Parvathy Sudhir Pillai, Andrew J Vercnocke, Matthew P Johnson, Shuai Leng and 5 more

Open access · greenAbstract read
In one paragraph

Article in Proceedings of SPIE--the International Society for Optical Engineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.2field-weighted citation impact, top 45% 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

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

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

15 authors at 2 institutions in 1 country.

Scott S HsiehDept. of Radiology, Mayo Clinic, Rochester, MN, USA 55902.
Akitoshi InoueDept. of Radiology, Mayo Clinic, Rochester, MN, USA 55902.
Mariana YalonDept. of Radiology, Mayo Clinic, Rochester, MN, USA 55902.
David A CookDept. of Internal Medicine, Mayo Clinic, Rochester, MN, USA 55902.
Jeff L FidlerDept. of Radiology, Mayo Clinic, Rochester, MN, USA 55902.
Hao GongDept. of Radiology, Mayo Clinic, Rochester, MN, USA 55902.
Parvathy Sudhir PillaiDept. of Radiology, Mayo Clinic, Rochester, MN, USA 55902.
Andrew J VercnockeDept. of Radiology, Mayo Clinic, Rochester, MN, USA 55902.
Matthew P JohnsonDept. of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA 55902.
Shuai LengDept. of Radiology, Mayo Clinic, Rochester, MN, USA 55902.
Lifeng YuDept. of Radiology, Mayo Clinic, Rochester, MN, USA 55902.
David R HolmesDept. of Radiology, Mayo Clinic, Rochester, MN, USA 55902.
Rickey E CarterDept. of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA 55902.
Cynthia H McColloughDept. of Radiology, Mayo Clinic, Rochester, MN, USA 55902.
Joel G FletcherDept. of Radiology, Mayo Clinic, Rochester, MN, USA 55902.
Mayo Clinic · USMayo Clinic in Florida · 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
NIBIB NIH HHS R01 EB017095
6 · The paper itself

Abstract

Detection of low contrast liver metastases varies between radiologists. Training may improve performance for lower-performing readers and reduce inter-radiologist variability. We recruited 31 radiologists (15 trainees, 8 non-abdominal staff, and 8 abdominal staff) to participate in four separate reading sessions: pre-test, search training, classification training, and post-test. In the pre-test, each radiologist interpreted 40 liver CT exams containing 91 metastases, circumscribed suspected hepatic metastases while under eye tracker observation, and rated confidence. In search training, radiologists interpreted a separate set of 30 liver CT exams while receiving eye tracker feedback and after coaching to increase use of coronal reformations, interpretation time, and use of liver windows. In classification training, radiologists interpreted up to 100 liver CT image patches, most with benign or malignant lesions, and compared their annotations to ground truth. Post-test was identical to pre-test. Between pre- and post-test, sensitivity increased by 2.8% (p = 0.01) but AUC did not change significantly. Missed metastases were classified as search errors (<2 seconds gaze time) or classification errors (>2 seconds gaze time) using the eye tracker. Out of 2775 possible detections, search errors decreased (10.8% to 8.1%; p < 0.01) but classification errors were unchanged (5.7% vs 5.7%). When stratified by difficulty, easier metastases showed larger reductions in search errors: for metastases with average sensitivity of 0-50%, 50-90%, and 90-100%, reductions in search errors were 16%, 35%, and 58%, respectively. The training program studied here may be able to improve radiologist performance by reducing errors but not classification errors.

Indexed as

Eye trackinglow contrast detectabilityreader performancereader training

Identifiers

PMID37064083
PMCPMC10099580
OpenAlexW4362489415

What OpenQuestion holds

Textmetadata
LicenceTDM
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.