Inter-equipment validation of PET-based radiomics for predicting EGFR mutation statuses in patients with non-small cell lung cancer.

AIM

To validate the inter-equipment generality of the radiomics based on PET images to predict the EGFR mutation status of patients with non-small cell lung cancer.

MATERIALS AND METHODS

Patients were retrospectively collected in the departments of nuclear medicine of Heyi branch (Siemens equipment) and East branch (General Electric (GE) equipment) of the first affiliated hospital of Zhengzhou university. 5 predicting logistic regression models were established. The 1 one was trained and tested by the GE dataset; The 2 one was trained and tested by the Siemens dataset; The 3 one was trained and tested by the mixed dataset consisting of GE and Siemens. The 4 one was trained by GE and tested by Siemens; The 5 one was trained by Siemens and tested by GE.

RESULTS

For the 1 ∼ 5 models, the mean values of AUCs for training/testing datasets were 0.78/0.73, 0.74/0.72, 0.75/0.70, 0.74/0.65 and 0.68/0.63, respectively.

CONCLUSION

The AUCs of the models trained and tested on the datasets from the same equipment were higher than those for different equipment. The inter-equipment generality of the radiomics was not good enough in clinical practice.