Evidence map›Paper›PMID 27716311›Full record

ArticleBreast cancer research : BCR2016

Novel mammographic image features differentiate between interval and screen-detected breast cancer: a case-case study.

Fredrik Strand, Keith Humphreys, Abbas Cheddad, Sven Törnberg, Edward Azavedo, John Shepherd, Per Hall, Kamila Czene

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Article in Breast cancer research : BCR, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
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

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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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. E-Science technologies in a workflow for personalized medicine using cancer screening as a case study.Journal of the American Medical Informatics Association : JAMIA · 2017
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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.

Fredrik StrandDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, P.O. Box 281, Stockholm, SE-171 77, Sweden. fredrik.strand@ki.se.ORCID 0000-0003-3910-7086
Keith HumphreysDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, P.O. Box 281, Stockholm, SE-171 77, Sweden.
Abbas CheddadDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, P.O. Box 281, Stockholm, SE-171 77, Sweden.
Sven TörnbergDepartment of Cancer Screening, Stockholm-Gotland Regional Cancer Centre, Stockholm, Sweden.
Edward AzavedoDepartment of Diagnostic Radiology, Karolinska University Hospital, Solna, Sweden.
John ShepherdDepartment of Radiology and Biomedical Imaging, UCSF School of Medicine, University of California San Francisco, San Francisco, CA, USA.
Per HallDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, P.O. Box 281, Stockholm, SE-171 77, Sweden.
Kamila CzeneDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, P.O. Box 281, Stockholm, SE-171 77, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInterval breast cancers are often diagnosed at a more advanced stage than screen-detected cancers. Our aim was to identify features in screening mammograms of the normal breast that would differentiate between future interval cancers and screen-detected cancers, and to understand how each feature affects tumor detectability.

methodsFrom a population-based cohort of invasive breast cancer cases in Stockholm-Gotland, Sweden, diagnosed from 2001 to 2008, we analyzed the contralateral mammogram at the preceding negative screening of 394 interval cancer cases and 1009 screen-detected cancers. We examined 32 different image features in digitized film mammograms, based on three alternative dense area identification methods, by a set of logistic regression models adjusted for percent density with interval cancer versus screen-detected cancer as the outcome. Features were forward-selected into a multiple logistic regression model adjusted for mammographic percent density, age, BMI and use of hormone replacement therapy. The associations of the identified features were assessed also in a sample from an independent cohort.

resultsTwo image features, 'skewness of the intensity gradient' and 'eccentricity', were associated with the risk of interval compared with screen-detected cancer. For the first feature, the per-standard deviation odds ratios were 1.32 (95 % CI: 1.12 to 1.56) and 1.21 (95 % CI: 1.04 to 1.41) in the primary and validation cohort respectively. For the second feature, they were 1.20 (95 % CI: 1.04 to 1.39) and 1.17 (95%CI: 0.98 to 1.39) respectively. The first feature was associated with the tumor size at screen detection, while the second feature was associated with the tumor size at interval detection.

conclusionsWe identified two novel mammographic features in screening mammograms of the normal breast that differentiated between future interval cancers and screen-detected cancers. We present a starting point for further research into features beyond percent density that might be relevant for interval cancer, and suggest ways to use this information to improve screening.

Indexed as

MammographyAgedBreast DensityBreast NeoplasmsEarly Detection of CancerFemaleHumansImage Processing, Computer-AssistedMass ScreeningMiddle AgedNeoplasm GradingNeoplasm MetastasisNeoplasm StagingPopulation SurveillanceSwedenTumor BurdenBreast cancerCancer screeningComputer-assisted image processingEarly detectionInterval cancerMammography

Identifiers

PMID27716311
PMCPMC5053212

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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.