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Table 1 Key studies in automated parenchymal texture analysis for breast cancer risk assessment

From: Beyond breast density: a review on the advancing role of parenchymal texture analysis in breast cancer risk assessment

Study

Mammograms

Dataset

Breast sampling

Texture features

Year

Participating institutions

F/D

View

A

B

S1

S2

T1

T2

T3

T4

T5

Distinguishing or predicting cancer cases from controls

Byng et al. (1997) [60]

University of Toronto, Sunnybrook Health Science Centre, Ontario Cancer Institute

F

CC

354P

354

x

 

x

  

x

 

Torres-Mejia et al. (2005) [72]

LSHTM, Guy’s Hospital, UNAM, IPOFG

F

CC/MLO

111P

3100

x

 

x

  

x

 

Wu et al. (2008) [76]

University of Michigan

F

CC

128C

549

x

   

x

  

Manduca et al. (2009) [66]

Mayo Clinic, Moffitt

F

CC/MLO

246P

522

x

 

x

x

x

x

x

Wei et al. (2011) [73]

University of Michigan

F

CC

136C

246

x

   

x

  

Nielsen et al. (2011) [61]

University of Copenhagen, Nordic Bioscience, Delft University of Technology, RadboudUMC, Mayo Clinic

F

MLO

245P

250

x

    

x

x

Brandt et al. (2011) [74]

University of Copenhagen, RadboudUMC, Synarc Imaging Technologies

F

MLO

245P

245

 

x

   

x

 

Häberle et al. (2012) [56]

Erlangen University Hospital, Fraunhofer Institute for Integrated Circuits IIS, IMPRS, UCLA

F

CC

864C

418

x

 

x

x

x

x

x

Li et al. (2012) [84]

University of Chicago

D

CC

75C

328

x

 

x

x

 

x

x

Chen et al. (2014) [75]

University of Manchester

D

MLO

50C

50

 

x

    

x

Nielsen et al. (2014) [64]

​University of Copenhagen, Nordic Bioscience, Biomediq, RadboudUMC, Mayo Clinic

F

CC/MLO

471P,C

692

x

    

x

x

Li et al. (2014) [71]

University of Chicago

D

CC

75C

328

x

 

x

x

  

x

Karemore et al. (2014) [89]

​University of Copenhagen, RadboudUMC

F

MLO

245P

250

 

x

   

x

x

Zheng et al. (2015) [51]

University of Pennsylvania

D

MLO

106C

318

 

x

x

x

x

x

 

Sun et al. (2015) [53]

University of Texas, China Northeastern University, University of Oklahoma, TTUHS, Guiyang Medical University

D

CC

141P

199

 

x

x

x

  

x

Tan et al. (2015) [77]

University of Texas, University of Oklahoma, University of Pittsburgh

D

CC/MLO

812P

1084

x

 

x

x

x

x

 

Tan et al. (2015) [78]

University of Oklahoma, University of Pittsburgh

D

CC/MLO

430P

440

x

  

x

x

x

x

Predicting the risk of carrying a high-risk genetic mutation

Huo et al. (2000) [80]

University of Chicago

F

CC

15

143

x

 

x

x

  

x

Huo et al. (2002) [55]

University of Chicago, University of Pennsylvania

F

CC

30

142

x

 

x

x

  

x

Li et al. (2004) [54]

University of Chicago, University of Pennsylvania

F

CC

30

60

x

 

x

x

 

x

x

Li et al. (2005) [81]

University of Chicago

F

CC

30

142

x

 

x

x

 

x

x

Li et al. (2007) [82]

University of Chicago

F

CC

30

142

x

    

x

 

Li et al. (2008) [83]

University of Chicago

F

CC

30

142

x

     

x

Li et al. (2012) [84]

University of Chicago

D

CC

53

328

x

 

x

x

 

x

x

Li et al. (2014) [71]

University of Chicago

D

CC

53

328

x

 

x

x

  

x

Gierach et al. (2014) [85]

University of Chicago, NCI-NIH, Washington Radiology Associates, Genentech, USUHS, UCL, WRNMC, Westat Inc.

F

CC

137

100

x

 

x

x

 

x

x

  1. The Table describes the image data used in each study, including type of mammograms and dataset size, as well as methodological details for the computerized texture analysis, the technique of breast sampling, and algorithm implementation of texture features
  2. IMPRS International Max Planck Research School for Optics and Imaging, IPOFG Instituto Português de Oncologia Francisco Gentil, LSHTM London School of Hygiene and Tropical Medicine, Moffitt Moffitt Cancer Center and Research Institute, NCI-NIH National Cancer Institute, National Institutes of Health, RadboudUMC Radboud University Nijmegen Medical Centre, TTUHS Texas Tech University Health Sciences, UCL University College London, UCLA University of California at Los Angeles, UNAM Universidad Nacional Autónoma de México, USUHS Uniformed Services University of the Health Sciences, WRNMC Walter Reed National Military Medical Center
  3. Mammograms: F Digitized screen-film, D Full-field digital, CC cranio-caudal, MLO mediolateral-oblique; Dataset: A cancer cases (Pprior, unaffected, images, Cimages from the contralateral, unaffected, breast at the time of cancer diagnosis) or other high-risk population (i.e., BRCA1/2 carriers), B controls; Breast sampling: S1 retro-areolar region or the entire breast/dense tissue as a single region of interest (ROI), S2 multiple ROIs covering the entire breast; Types of texture features: T1 gray-level histogram, T2 co-occurrence, T3 run-length, T4 structural/pattern, T5 multi-resolution/spectral