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Table 3 The performance evaluation of the datasets and subgroups

From: A meta-analysis of the diagnostic performance of machine learning-based MRI in the prediction of axillary lymph node metastasis in breast cancer patients

Dataset No. of studies Sensitivity Specificity PLR NLR DOR
Testing set 3 0.76 (0.64–0.86)# 0.82 (0.72–0.89)## 3.90 (2.49–6.11)## 0.29 (0.19–0.46)# 12.88 (5.99–27.71)##
Validation set 12 0.79 (0.74–0.84)# 0.77 (0.73–0.81)# 3.47 (2.91–4.14)# 0.27 (0.22–0.33)# 12.92 (9.34–17.87)#
Overall validation group       
Magnetic field strength (T)
1.5 T 7 0.81 (0.75–0.87)# 0.76 (0.71–0.81)# 3.38 (2.70–4.24)# 0.25 (0.18–0.34)# 13.15 (8.37–20.66)#
3.0 T 5 0.86 (0.78–0.89)** 0.76 (0.70–0.82)# 3.46 (2.69–4.46)# 0.23 (0.11–0.48)** 17.78 (10.54–30.00)##
Sequence
T2-FS 3 0.67 (0.54–0.79)# 0.80 (0.71–0.88)# 3.43 (2.22–5.29)## 0.41 (0.28–0.59)# 8.01 (4.01–16.00)##
T1CE 8 0.80 (0.75–0.84)## 0.80 (0.75–0.84)# 4.04 (3.25–5.02)# 0.25 (0.19–0.31)## 16.27 (11.08–23.89)#
DWI 2 0.76 (0.60–0.89)# 0.75 (0.63–0.85)# 3.10 (1.96–4.91)# 0.31 (0.17–0.56)# 10.00 (3.97–25.17)#
Algorithm
SVM 5 0.77 (0.71–0.82)** 0.79 (0.73–0.83)## 3.67 (2.87–4.70)# 0.30 (0.19–0.47)* 13.43 (8.62–20.93)##
LR 4 0.70 (0.58–0.81)# 0.78 (0.68–0.85)# 3.20 (2.17–4.71)# 0.37 (0.26–0.55)# 8.60 (4.44–16.66)#
LDA 3 0.83 (0.77–0.88)# 0.75 (0.68–0.81)# 3.35 (2.58–4.35)## 0.22 (0.16–0.31)# 14.78 (9.01–24.26)#
Segmentation
2D 4 0.77 (0.70–0.83)## 0.76 (0.70–0.82)# 3.30 (2.55–4.28)# 0.29 (0.21–0.39)# 11.46 (7.05–18.62)#
3D 9 0.80 (0.75–0.84)## 0.78 (0.73–0.82)# 3.56 (2.88–4.39)# 0.26 (0.21–0.34)## 13.52 (9.27–19.72)#
  1. PLR, positive likelihood ratio; NLR, negative likelihood ratio; DOR, diagnostic odds ratio; SVM, support vector machine; LR, linear regression; LDA, linear discriminant analysis; T2-FS, fat-suppressed T2; T1CE, contrast-enhanced T1; #, 0–25% of I2 values; ##, 25–50%; *, 50–75%; **, > 75%; # and ##, fixed-effects model; * and **, random-effects model