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Table 1 Summary of studies included in this review

From: Deep learning applications to breast cancer detection by magnetic resonance imaging: a literature review

Study

Source of data (where data were obtained)

# of patients

Benign lesions

Malignant lesions

Image type

DL architecture

Box or contour

Ground truth

Cross validation

Heatmap

AUC

Accu

Sens

Spec

Adachi [13]

Tokyo Medical and Dental University (Japan)

72

20

52

MIP, DCE

RetinaNet

Bounding box

Rad per path; path, > 1y F/U

Hold-out method

No

0.93

Not reported

0.93

0.83

Antropova [24]

University of Chicago

690

212

478

MIP DCE; DCE subtraction

ConvNetVGGN

Manual ROI with bounding box

Rad per path; biopsy proven

Fivefold

No

0.88,

0.80,

0.83

Not reported

Not reported

Not reported

Ayatollahi [22]

Radbound University Medical Center (the Netherlands)

462

207

365

DCE

RetinaNet

Bounding box

Path, 2y F/U

Tenfold

No

0.90

Not reported

0.95

Not reported

Feng [24]

Tangdu Hospital and Xi'an International Medical Center Hospital (China)

100

32

68

DCE + DWI

KFLI

Bounding box

Path

Hold-out method

No

0.86

0.85

0.85

0.86

Fujioka [25]

Tokyo Medical and Dental University (Japan)

72

12 normal

13 benign

47

MIP DCE

InceptionResNetV2 Others

None

Path,

 > 1y F/U

Tenfold

Yes

0.83

Not reported

0.75

0.96

Haarburger [26]

University Hospital Aachen (Germany)

408

103

305

DCE + T2

Mask-R-CNN;

Retina U-Net;

3D ResNet18 Naive; ResNet18;

Curriculum

Coarse localization by radiologist

Whole breast images

Path, two-year F/U

Fivefold

Yes

0.88

0.89

0.50

0.89

0.93

0.77

0.82

0.45

0.81

0.93

Not reported

Not reported

Herent [27]

Journées Francophones de Radiologie (France)

335

212

123

Post-contrast

T1

CNN Resnet-50

Manual bounding boxes

Path

Threefold

No

0.82

Not reported

Not reported

Not reported

Hu [28]

University of Chicago

616

199

728

DCE, T2 (not coupled)

CNN

 

Path,

Rad reports

Fivefold

 

0.87

Not reported

0.78

0.79

Li [29]

Zhiejiang Cancer Hospital, China

143

66

77

DCE

2D vs. 3D CNN

Bounding box

Path verified

 

No

0.80 3D

0.78

3D

0.74

3D

0.82

3D

Liu [30]

Multiple institutions in the US (ISPY-1 data)*

438

  

DCE

CNN

Square cropping in sagittal plane

Path

Rad annotation

Fivefold

No

0.92

0.94

0.74

0.95

Marrone [31]

University of Naples (Italy)

42

25

42

DCE 4D

AlexNet CNN

Manual ROI

Path

Tenfold

No

0.76

0.76

0.83

0.79

Rasti [32]

Imaging Center of Milad Hospital (Tehran)

112

59

53

DCE 1st Post-subtraction

ME-CNN

 

Path

Fivefold

No

0.99

0.96

0.98

0.95

Truhn [33]

University of Aachen (Germany)

447

507

787

T2, pre- and post-contrast

CNN ResNet 18

Manual segmentation

Path,

F/U

Tenfold

No

0.88

Not reported

0.78

0.85

Wu [34]

Beijing University People's Hospital (China)

130

59

71

DCE

CNN

Bounding box

Path

 

Yes

0.91

0.88

0.86

Not reported

Yurttakal [35]

Haseki Training and Research Hospital (Turkey)

200

98

102

DCE subtraction

CNN

Cropping tumorous regions

Rectang box

Path,

Rad

 

No

Not reported

0.98

1.0

0.97

Zheng [36]

Renji Hospital (China)

72

45

27

DCE + DWI

Multi-timepoints

DC-LSTM, ResNet50

Labels by radiologists, then cropped 40 × 40x40

Rad per path; Path for B/M benign vs. mal

Threefold

No

Not reported

0.85

Not reported

Not reported

Zhou [37]

Not specified

(Likely China)

133

62

91

DCE

CNN ResNet50

Segment by fuzzy C-means after radiologist indicated location

Path

Tenfold

No

0.97–0.99

0.89

0.94

0.81

Zhou [38]

The Fifth Medical Center of Chinese PLA General Hospital (China)

307

101

206

DCE

3D DenseNet GAP

3D DenseNet GMP

Ensemble

Bounding box

Path,

3y F/U

 

Yes

0.86

0.86

0.86

0.81

0.81

0.83

0.86

0.92

0.91

0.70

0.61

0.69

  1. Studies are arranged alphabetically. DCE: dynamic contrast enhancement, DWI: diffusion-weighted imaging, MIP: maximum intensity projection, T2: T2-weighted MRI. F/U: follow-up, Rad: radiology, Path: pathology, CNN: convolutional neural network, AUC: area under the curve, Accu: accuracy, Sens: sensitivity, and Spec: specificity
  2. *public dataset (see text for link)