Table 2 Logistic regression to predict amyloid-beta positivity in patients with mild cognitive impairment using demographics and magnetic resonance image features.

From: Do radiomics or diffusion-tensor images provide additional information to predict brain amyloid-beta positivity?

 

Logistic regression

Support vector model

Random forest

Mean

SD

p

Mean

SD

p

Mean

SD

p

Step 1 Demographic features

 Age, sex, education

0.52

0.08

 < 0.001

0.51

0.08

 < 0.001

0.55

0.08

 < 0.001

 Age, sex, education + MMSE (total)

0.58

0.09

 < 0.001

0.56

0.09

 < 0.001

0.64

0.08

0.059

 Age, sex, education + SNSB (detailed)

0.69

0.08

Ref

0.69

0.08

Ref

0.66

0.08

Ref

Step 2 T1-weighted magnetic resonance image

 Age, sex, education + SNSB (detailed)

0.69

0.08

Ref

0.69

0.08

Ref

0.66

0.08

Ref

 Age, sex, education + SNSB (detailed) + T1b (thickness)

0.68

0.09

0.03

0.67

0.09

0.008

0.62

0.09

 < 0.001

 Age, sex, education + SNSB (detailed) + T1 (texture)

0.71

0.08

0.06

0.71

0.08

0.12

0.70

0.08

 < 0.001

 Age, sex, education + SNSB (detailed) + T1 (volume)

0.73

0.08

 < 0.001

0.73

0.08

 < 0.001

0.73

0.08

 < 0.001

Step 3 Diffusion-tensor magnetic resonance image

 Age, sex, education + SNSB (detailed) + T1 (volume)

0.73

0.08

Ref

0.73

0.08

Ref

0.73

0.08

Ref

 Age, sex, education + SNSB (detailed) + T1 (volume) + DTI (FA)

0.73

0.08

0.60

0.73

0.08

0.42

0.73

0.08

0.85

 Age, sex, education + SNSB (detailed) + T1 (volume) + DTI (MD)

0.74

0.08

0.20

0.73

0.08

0.20

0.73

0.08

0.64

  1. SD standard deviation, MMSE mini-mental state examination, SNSB Seoul neuropsychological screening battery, DTI diffusion-tensor image, FA functional anisotropy, MD mean diffusivity, Ref reference.
Back to article page