ORCID Profile
0000-0002-8440-1711
Current Organisation
University of York
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Publisher: Public Library of Science (PLoS)
Date: 25-04-2023
DOI: 10.1371/JOURNAL.PONE.0284605
Abstract: Previous studies showed that radiologists can detect the gist of an abnormality in a mammogram based on a half-second image presentation through global processing of screening mammograms. This study investigated the intra- and inter-observer reliability of the radiologists’ initial impressions about the abnormality (or "gist signal"). It also examined if a subset of radiologists produced more reliable and accurate gist signals. Thirty-nine radiologists provided their initial impressions on two separate occasions, viewing each mammogram for half a second each time. The intra-class correlation (ICC) values showed poor to moderate intra-reader reliability. Only 13 radiologists had an ICC of 0.6 or above, which is considered the minimum standard for reliability, and only three radiologists had an ICC exceeding 0.7. The median value for the weighted Cohen’s Kappa was 0.478 (interquartile range = 0.419–0.555). The Mann-Whitney U-test showed that the "Gist Experts", defined as those who outperformed others, had significantly higher ICC values (p = 0.002) and weighted Cohen’s Kappa scores (p = 0.026). However, even for these experts, the intra-radiologist agreements were not strong, as an ICC of at least 0.75 indicates good reliability and the signal from none of the readers reached this level of reliability as determined by ICC values. The inter-reader reliability of the gist signal was poor, with an ICC score of 0.31 (CI = 0.26–0.37). The Fleiss Kappa score of 0.106 (CI = 0.105–0.106), indicating only slight inter-reader agreement, confirms the findings from the ICC analysis. The intra- and inter-reader reliability analysis showed that the radiologists’ initial impressions are not reliable signals. In particular, the absence of an abnormal gist does not reliably signal a normal case, so radiologists should keep searching. This highlights the importance of "discovery scanning," or coarse screening to detect potential targets before ending the visual search.
Publisher: Springer Science and Business Media LLC
Date: 07-06-2018
DOI: 10.1038/S41598-018-26100-5
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 in iduals 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.
Publisher: SPIE
Date: 22-05-2020
DOI: 10.1117/12.2564275
Publisher: Springer Science and Business Media LLC
Date: 11-10-2021
DOI: 10.1038/S41598-021-99582-5
Abstract: The information captured by the gist signal, which refers to radiologists’ first impression arising from an initial global image processing, is poorly understood. We examined whether the gist signal can provide complementary information to data captured by radiologists (experiment 1), or computer algorithms (experiment 2) based on detailed mammogram inspection. In the first experiment, 19 radiologists assessed a case set twice, once based on a half-second image presentation (i.e., gist signal) and once in the usual viewing condition. Their performances in two viewing conditions were compared using repeated measure correlation (rm-corr). The cancer cases (19 cases × 19 readers) exhibited non-significant trend with rm-corr = 0.012 ( p = 0.82, CI: −0.09, 0.12). For normal cases (41 cases × 19 readers), a weak correlation of rm-corr = 0.238 ( p 0.001, CI: 0.17, 0.30) was found. In the second experiment, we combined the abnormality score from a state-of-the-art deep learning-based tool (DL) with the radiological gist signal using a support vector machine (SVM). To obtain the gist signal, 53 radiologists assessed images based on half-second image presentation. The SVM performance for each radiologist and an average reader, whose gist responses were the mean abnormality scores given by all 53 readers to each image was assessed using leave-one-out cross-validation. For the average reader, the AUC for gist, DL, and the SVM, were 0.76 (CI: 0.62–0.86), 0.79 (CI: 0.63–0.89), and 0.88 (CI: 0.79–0.94). For all readers with a gist AUC significantly better than chance-level, the SVM outperformed DL. The gist signal provided malignancy evidence with no or weak associations with the information captured by humans in normal radiologic reporting, which involves detailed mammogram inspection. Adding gist signal to a state-of-the-art deep learning-based tool improved its performance for the breast cancer detection.
Publisher: SPIE
Date: 06-07-2018
DOI: 10.1117/12.2318321
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Karla Evans.