title: Novel mammographic image features differentiate between interval and screen-detected breast cancer: a case-case study.
abstract: Interval 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.
  
  
  
  From 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.
  
  
  
  Two 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.
  
  
  
  We 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.
authors.affiliation: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, P.O. Box 281, Stockholm, SE-171 77, Sweden. fredrik.strand@ki.se.|Department of Diagnostic Radiology, Karolinska University Hospital, Solna, Sweden. fredrik.strand@ki.se.
authors.forename: Fredrik
authors.identifier: 0000-0003-3910-7086
authors.initials: F
authors.lastname: Strand
authors.affiliation: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, P.O. Box 281, Stockholm, SE-171 77, Sweden.|Swedish eScience Research Centre (SeRC), Karolinska Institutet, Solna, Sweden.
authors.forename: Keith
authors.initials: K
authors.lastname: Humphreys
authors.affiliation: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, P.O. Box 281, Stockholm, SE-171 77, Sweden.
authors.forename: Abbas
authors.initials: A
authors.lastname: Cheddad
authors.affiliation: Department of Cancer Screening, Stockholm-Gotland Regional Cancer Centre, Stockholm, Sweden.
authors.forename: Sven
authors.initials: S
authors.lastname: Törnberg
authors.affiliation: Department of Diagnostic Radiology, Karolinska University Hospital, Solna, Sweden.|Department of Molecular Medicine and Surgery, Karolinska Institutet, Solna, Sweden.
authors.forename: Edward
authors.initials: E
authors.lastname: Azavedo
authors.affiliation: Department of Radiology and Biomedical Imaging, UCSF School of Medicine, University of California San Francisco, San Francisco, CA, USA.
authors.forename: John
authors.initials: J
authors.lastname: Shepherd
authors.affiliation: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, P.O. Box 281, Stockholm, SE-171 77, Sweden.
authors.forename: Per
authors.initials: P
authors.lastname: Hall
authors.affiliation: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, P.O. Box 281, Stockholm, SE-171 77, Sweden.
authors.forename: Kamila
authors.initials: K
authors.lastname: Czene
mesh_terms: D000368:Aged; D000071060:Breast Density; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000453:epidemiology / Q000473:pathology*; D055088:Early Detection of Cancer / Q000379:methods; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008327:Mammography / Q000379:methods; D008403:Mass Screening / Q000379:methods; D008875:Middle Aged; D060787:Neoplasm Grading; D009362:Neoplasm Metastasis; D009367:Neoplasm Staging; D011159:Population Surveillance; D013548:Sweden; D047368:Tumor Burden
keywords: Breast cancer; Cancer screening; Computer-assisted image processing; Early detection; Interval cancer; Mammography