Table 3 Coefficients and odds ratios for individual features of the LungCNN-TMB model.

From: Comparative analysis of machine learning approaches to classify tumor mutation burden in lung adenocarcinoma using histopathology images

 

Feature

Estimate (95% CI)

p-value

OR

Model coefficients

Solid

1.13 (0.49, 1.73)

0.001*

3.10

Smoking status

0.90 (0.41, 1.36)

0.002*

2.46

Age

−0.86 (−1.44, −0.29)

0.062

0.42

Pathologic stage

−0.51 (−1.12, 0.09)

0.157

0.60

Intercept

−1.56 (−1.90, −1.22)

0.000*

–

  1. Two-sided P-values were estimated using bootstrapping. A logistic regression model combines features from the 9 image features of LungCNN-Histo and 4 clinical features (age in years, sex, pathologic stage, smoking status) to predict TMB status. The image features (quantified as proportion of total tumor area) and age (in years) are treated as continuous variables, pathologic stage (I–IV) as ordinal, and smoking status (binary) and sex (M,F) as categorical. Features in this table represent those providing best model via cross-validation (see “Methods”).
  2. OR odds ratio
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