Trial reportScientific reports2018
Radiologists can detect the 'gist' of breast cancer before any overt signs of cancer appear.
Trial report in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
What it found
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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.
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.
Who cites it
39 citing papers in PubMed, 61 citations in OpenAlex.
- Subtraction of Temporally Sequential Digital Mammograms: Prediction and Localization of Near-Term Breast Cancer Occurrence.Journal of imaging informatics in medicine · 2025Article
- A Machine Learning Model Based on Global Mammographic Radiomic Features Can Predict Which Normal Mammographic Cases Radiology Trainees Find Most Difficult.Journal of imaging informatics in medicine · 2025Article
- Beyond minutiae: inferring missing details from global structure in fingerprints.Cognitive research: principles and implications · 2025Article
- Enhancing Radiologist Reading Performance by Ordering Screening Mammograms Based on Characteristics That Promote Visual Adaptation.Radiology · 2024Article
- Optimizing mammography interpretation education: leveraging deep learning for cohort-specific error detection to enhance radiologist training.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Test-set results can predict participants' development in breast-screen cancer detection: An observational cohort study.Health science reports · 2024Article
- Computer-extracted global radiomic features can predict the radiologists' first impression about the abnormality of a screening mammogram.The British journal of radiology · 2024Article
- Science, evidence, law, and justice.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Global Radiomic Features from Mammography for Predicting Difficult-To-Interpret Normal Cases.Journal of digital imaging · 2023Article
- Serial dependence in perception across naturalistic generative adversarial network-generated mammogram.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Using Radiomics-Based Machine Learning to Create Targeted Test Sets to Improve Specific Mammography Reader Cohort Performance: A Feasibility Study.Journal of personalized medicine · 2023Article
- Contributions of global and local processing on medical image perception.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Signals of global symmetry are important for abnormality detection in mammograms.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Using global feedback to induce learning of gist of abnormality in mammograms.Cognitive research: principles and implications · 2023Article
- Hip fracture or not? The reversed prevalence effect among non-experts' diagnosis.Cognitive research: principles and implications · 2023Article
- Reliability of radiologists' first impression when interpreting a screening mammogram.PloS one · 2023Article
- Article
- Autobiographical event memory and aging: older adults get the gist.Trends in cognitive sciences · 2022Review
- Spotting lesions in thorax X-rays at a glance: holistic processing in radiology.Cognitive research: principles and implications · 2022Article
- Comparable prediction of breast cancer risk from a glimpse or a first impression of a mammogram.Cognitive research: principles and implications · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 3 countries.
Funding
Abstract
Radiologists can detect abnormality in mammograms at above-chance levels after a momentary glimpse of an image. The study investigated this instantaneous perception of an abnormality, known as a "gist" response, when 23 radiologists viewed prior mammograms of women that were reported as normal, but later diagnosed with breast cancer at subsequent screening. Five categories of cases were included: current cancer-containing mammograms, current mammograms of the normal breast contralateral to the cancer, prior mammograms of normal cases, prior mammograms with visible cancer signs in a breast from women who were initially reported as normal, but later diagnosed with breast cancer at subsequent screening in the same breast, and prior mammograms without any visible cancer signs from women labelled as initially normal but subsequently diagnosed with cancer. Our findings suggest that readers can distinguish patients who were diagnosed with cancer, from individuals without breast cancer (normal category), at above-chance levels based on a half-second glimpse of the mammogram even before any lesion becomes visible on the mammogram. Although 20 of the 23 radiologists demonstrated this ability, radiologists' abilities for perceiving the gist of the abnormal varied between the readers and appeared to be linked to expertise. These results could have implications for identifying women of higher than average risk of a future malignancy event, thus impacting upon tailored screening strategies.
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Registered trials
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.