Applications of radiomics-based analysis pipeline for predicting epidermal growth factor receptor mutation status.
Biomed Eng Online
; 22(1): 17, 2023 Feb 21.
Article
en En
| MEDLINE
| ID: mdl-36810090
ABSTRACT
BACKGROUND:
This study aimed to develop a pipeline for selecting the best feature engineering-based radiomic path to predict epidermal growth factor receptor (EGFR) mutant lung adenocarcinoma in 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT).METHODS:
The study enrolled 115 lung adenocarcinoma patients with EGFR mutation status from June 2016 and September 2017. We extracted radiomics features by delineating regions-of-interest around the entire tumor in 18F-FDG PET/CT images. The feature engineering-based radiomic paths were built by combining various methods of data scaling, feature selection, and many methods for predictive model-building. Next, a pipeline was developed to select the best path.RESULTS:
In the paths from CT images, the highest accuracy was 0.907 (95% confidence interval [CI] 0.849, 0.966), the highest area under curve (AUC) was 0.917 (95% CI 0.853, 0.981), and the highest F1 score was 0.908 (95% CI 0.842, 0.974). In the paths based on PET images, the highest accuracy was 0.913 (95% CI 0.863, 0.963), the highest AUC was 0.960 (95% CI 0.926, 0.995), and the highest F1 score was 0.878 (95% CI 0.815, 0.941). Additionally, a novel evaluation metric was developed to evaluate the comprehensive level of the models. Some feature engineering-based radiomic paths obtained promising results.CONCLUSIONS:
The pipeline is capable of selecting the best feature engineering-based radiomic path. Combining various feature engineering-based radiomic paths could compare their performances and identify paths built with the most appropriate methods to predict EGFR-mutant lung adenocarcinoma in 18FDG PET/CT. The pipeline proposed in this work can select the best feature engineering-based radiomic path.Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Adenocarcinoma del Pulmón
/
Neoplasias Pulmonares
Tipo de estudio:
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Año:
2023
Tipo del documento:
Article