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Table 2 Positive results of univariate and multivariate logistic regression for clinical and CT characteristics in patients

From: CT-based deep learning radiomics nomogram for the prediction of pathological grade in bladder cancer: a multicenter study

Variable

Univariate

Multivariate

OR (95% CI)

p value

OR (95% CI)

p value

Age

1.038(1.020–1.057)

<0.001

1.032(1.012–1.051)

0.001

Size

1.408(1.253–1.582)

<0.001

1.277(1.119–1.457)

<0.001

Shape

1.374(1.091–1.732)

0.007

1.565(1.188–2.062)

0.001

Boundary

0.618(0.421–0.906)

0.014

0.503(0.324–0.780)

0.002

Cystic necrosis

3.507(1.441–8.535)

0.006

1.058(0.378–2.964)

0.914

Stalk

0.414(0.262–0.656)

<0.001

0.575(0.341–0.969)

0.038

Extramural infiltration

8.941(3.176–25.175)

<0.001

4.296(1.369–13.479)

0.012

RLN metastasis

13.422(1.793-100.489)

0.011

5.819(0.696–48.658)

0.104

LCT V-C

1.012(1.003–1.021)

0.010

1.009(0.997–1.020)

0.143

LCTV-N

1.015(1.004–1.026)

0.006

1.010(0.995–1.025)

0.198

LCTV-E

1.011(1.002–1.021)

0.020

0.999(0.988–1.010)

0.905

  1. OR, odds ratio; CI, confidence interval; RLN, reginal lymph node; LCTV-C, lesion CT value in corticomedullary-phase; LCTV-N, lesion CT value in nephrographic-phase; LCTV-E, lesion CT value in excretory-phase