Early signs of cancer present in the fine detail of mammograms

Karla Evans, Emma Raat

Research output: Contribution to journalArticlepeer-review


The gist of abnormality can be rapidly extracted by medical experts from global information in medical images, such as mammograms, to identify abnormal mammograms with above chance accuracy—even before any abnormalities are localizable. The current study evaluated the effect of different high-pass filters on expert radiologists’ performance in detecting the gist of abnormality in mammograms, especially those acquired prior to any visibly actionable lesions. Thirty-four expert radiologists viewed unaltered and high-pass filtered versions of normal and abnormal mammograms. Abnormal mammograms consisted of obvious abnormalities, subtle abnormalities, and currently normal mammograms from women who would go to develop cancer in 2–3 years. Four levels of high-pass filtering were tested (0.5,
1, 1.5, and 2 cycles per degree (cpd) after brightening and contrast normalizing to the unfiltered mammograms. Overall performance for 0.5 and 1.5 did not change compared to unfiltered but was reduced for 1 and 2 cpd. Critically, filtering that eliminated frequencies below 0.5 and 1.5 cpd significantly boosted performance on mammograms acquired years prior appearance of localizable abnormalities. Filtering at 0.5 did not change the radiologist’s decision criteria compared to unfiltered mammograms whereas other filters resulted in more conservative ratings. The findings bring us closer to identifying the characteristics of the gist of the abnormal that affords radiologists detection of the earliest signs of cancer. A 0.5 cpd high-pass filter significantly boosts subtle, global signals of future cancerous abnormalities,
potentially providing an image enhancement strategy for rapid assessment of impending cancer risk.
Original languageEnglish
Article numbere0282872
Number of pages16
JournalPLoS ONE
Issue number4
Publication statusPublished - 5 Apr 2023

Bibliographical note

© 2023 Raat, Evans

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