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Table 4 Results of machine learning models for predicting local lung fibrosis

From: Radiomics for prediction of radiation-induced lung injury and oncologic outcome after robotic stereotactic body radiotherapy of lung cancer: results from two independent institutions

 

Coxnet

Gradient boost

Features

number of features

CCI train-set

CCI cross-valid

CCI test-set

Number of features

CCI train-set

CCI cross-valid

CCI test-set

Clinical/dosimetric

3§

0.71

p < 0.005

0.68 ± 0.11

0.65

p = 0.04*

3§

0.73

p < 0.005

0.64 ± 0.12

0.62

n.s

Radiomics

10

0.79

p < 0.005

0.64 ± 0.13

0.58

n.s

2†

0.75

p < 0.005

0.72 ± 0.11

0.59

p = 0.02*

Combined

4 + 7

0.74

p < 0.005

0.67 ± 0.12

0.66

p = 0.03*

0 + 2†

0.72

p < 0.005

0.72 ± 0.11

0.59

p = 0.02*

  1. CCI concordance index, means ± standard deviation are shown, p-values: significance level of the model risk score in univariate Cox regression analysis
  2. §Age/ GTVMeanDose/LungD1ml
  3. †wavelet_HLH_glcm_MCC/wavelet_HLL_glcm_MCC (= GrayLevelCo-occurrence matrix maximal correlation coefficient)
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Radiation Oncology

ISSN: 1748-717X

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