Table 6 Multiple linear regression model of EEF based on imaging features.

From: Multi-sequence magnetic resonance imaging radiomics combined with imaging features predicts the difficulty of HIFU treatment of uterine fibroids

model

R

R2

Adjusted R2

F

P

Durbin watson

1

0.401a

0.161

0.156

33.714

0.000a

 

2

0.492b

0.242

0.233

27.955

0.000b

 

3

0.565c

0.320

0.308

27.258

0.000c

 

4

0.606d

0.367

0.352

25.089

0.000d

 

5

0.620e

0.384

0.367

21.485

0.000e

2.038

  1. a. Predictor variable: (constant), volume of uterine fibroid.
  2. b. Predictor variables: (constants), volume of uterine fibroid, shortest distance from the ventral side to the skin at the level of the center of the target fibroid.
  3. c. Predictor variables: (constants), volume of uterine fibroid, shortest distance from the ventral side to the skin at the level of the center of the target fibroid, submucosal fibroid.
  4. d. Predictor variables: (constant), volume of uterine fibroid, shortest distance from the ventral side to the skin at the level of the center of the target fibroid, submucosal fibroid, significant enhancement of T1WI.
  5. e. Predictor variables: (constant), volume of uterine fibroid, shortest distance from the ventral side to the skin at the level of the center of the target fibroid, submucosal fibroid, significant enhancement of T1WI, distance from the dorsal side of the uterine fibroid to the sacrum.
  6. f. Dependent variable: EEF.
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