Deep radiomic model based on the sphere-shell partition for predicting treatment response to chemotherapy in lung cancer.
Transl Oncol
; 35: 101719, 2023 Sep.
Article
em En
| MEDLINE
| ID: mdl-37320871
ABSTRACT
BACKGROUND:
The prognosis of chemotherapy is important in clinical decision-making for non-small cell lung cancer (NSCLC) patients.OBJECTIVES:
To develop a model for predicting treatment response to chemotherapy in NSCLC patients from pre-chemotherapy CT images. MATERIALS ANDMETHODS:
This retrospective multicenter study enrolled 485 patients with NSCLC who received chemotherapy alone as a first-line treatment. Two integrated models were developed using radiomic and deep-learning-based features. First, we partitioned pre-chemotherapy CT images into spheres and shells with different radii around the tumor (0-3, 3-6, 6-9, 9-12, 12-15 mm) containing intratumoral and peritumoral regions. Second, we extracted radiomic and deep-learning-based features from each partition. Third, using radiomic features, five sphere-shell models, one feature fusion model, and one image fusion model were developed. Finally, the model with the best performance was validated in two cohorts.RESULTS:
Among the five partitions, the model of 9-12 mm achieved the highest area under the curve (AUC) of 0.87 (95% confidence interval 0.77-0.94). The AUC was 0.94 (0.85-0.98) for the feature fusion model and 0.91 (0.82-0.97) for the image fusion model. For the model integrating radiomic and deep-learning-based features, the AUC was 0.96 (0.88-0.99) for the feature fusion method and 0.94 (0.85-0.98) for the image fusion method. The best-performing model had an AUC of 0.91 (0.81-0.97) and 0.89 (0.79-0.93) in two validation sets, respectively.CONCLUSIONS:
This integrated model can predict the response to chemotherapy in NSCLC patients and assist physicians in clinical decision-making.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Clinical_trials
/
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article