A CT radiomics-based machine learning model for predicting high-grade pathological components in stage IA lung
1Department of Radiology, The Third People's Hospital of Honghe Hani and Yi Autonomous Prefecture, Gejiu, 661000, China.
Aim:
To explore the application value of a machine learning model based on CT radiomics in predicting high-grade components in clinical stage IA lung adenocarcinoma.
Materials And Methods:
A retrospective dataset of 405 patients with pathologically confirmed stage IA lung adenocarcinoma who underwent surgical resection at two hospitals was collected (156 cases in the HGC group and 249 cases in the non-HGC group). Radiomic features were extracted from each patient's gross tumor volume (GTV) and peritumoral volume (PTV). Lasso-SVM was employed to develop radiomics, clinical, and combined models in the training dataset. The models' performance and clinical utility were evaluated in both internal and external validation datasets using receiver operating characteristic (ROC) curves and decision curve analysis (DCA).
Results:
A total of 12 GTV features, 9 PTV features, and 14 GPTV features were selected for building the model. The combined model incorporating radiomic features and the clinical feature achieved an area under the curve (AUC) value of 0.860 (95% CI: 0.809-0.912) in the training set, 0.849 (95% CI: 0.764-0.933) in the internal validation set, and 0.822 (95% CI: 0.730-0.914) in the external validation set.
Conclusion:
Machine learning model based on CT radiomics are helpful in preoperatively identifying high-grade components in clinical stage IA lung adenocarcinoma.
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