Applying radiomics and dosimetry features to predict 2-year survival of esophageal cancer patients treated with radiotherapy / 中国肿瘤临床
Chinese Journal of Clinical Oncology
; (24): 334-337, 2020.
Article
en Zh
| WPRIM
| ID: wpr-861572
Biblioteca responsable:
WPRO
ABSTRACT
Objective: Applying radiomics and dosimetry features to establish machine learning models, which is used to predict the 2-year survival of esophageal patients with radiotherapy. Methods: Retrospective analysis of 579 esophageal cancer patients who underwent radiotherapy from January 2013 to December 2017 in Tianjin Medical University Cancer Institute and Hospital. Radiomics and dosimetry features were extracted from the GTV of the radiotherapy plan for patients with esophageal cancer. The maximum correlation and minimum redundancy and manual methods were used to reduce the feature vector. A total of 14 radiomics and 14 dosimetry features were selected, then normalized to the range [0,1]. The machine learning models such as support vector machines (SVM), Logistic regression (LR), and random forest (RF) were used to train and test the radiomics and dosimetry features, respectively, then to predict the 2-year survival of esophageal cancer patients treated with radiotherapy. Results: When only the radiomics features were used to predict the 2-year survival after radiotherapy, the accuracy of SVM, LR and RF models were 84.98%, 85.92% and 84.51%, respectively. Furthermore, when the combined features of radiomics and dosimetry were used for prediction, the accuracy of the SVM, LR and RF models were 86.32%, 83.02% and 90.01%, respectively. Using the radiomics and dosimetry features, the predictive accuracy of SVM and RF models are effectively improved. Conclusions: For the SVM and RF models, the radiomics and dosimetry features can effectively improve the accuracy of predicting 2-year survival for esophageal cancer patients after radiation therapy.
Texto completo:
1
Base de datos:
WPRIM
Tipo de estudio:
Guideline
/
Prognostic_studies
Idioma:
Zh
Revista:
Chinese Journal of Clinical Oncology
Año:
2020
Tipo del documento:
Article