Predicting Breast Cancer Subtypes Using Magnetic Resonance Imaging Based Radiomics With Automatic Segmentation.
J Comput Assist Tomogr
; 47(5): 729-737, 2023.
Article
en En
| MEDLINE
| ID: mdl-37707402
ABSTRACT
OBJECTIVE:
The aim of the study is to demonstrate whether radiomics based on an automatic segmentation method is feasible for predicting molecular subtypes.METHODS:
This retrospective study included 516 patients with confirmed breast cancer. An automatic segmentation-3-dimensional UNet-based Convolutional Neural Networks, trained on our in-house data set-was applied to segment the regions of interest. A set of 1316 radiomics features per region of interest was extracted. Eighteen cross-combination radiomics methods-with 6 feature selection methods and 3 classifiers-were used for model selection. Model classification performance was assessed using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.RESULTS:
The average dice similarity coefficient value of the automatic segmentation was 0.89. The radiomics models were predictive of 4 molecular subtypes with the best average AUC = 0.8623, accuracy = 0.6596, sensitivity = 0.6383, and specificity = 0.8775. For luminal versus nonluminal subtypes, AUC = 0.8788 (95% confidence interval [CI], 0.8505-0.9071), accuracy = 0.7756, sensitivity = 0.7973, and specificity = 0.7466. For human epidermal growth factor receptor 2 (HER2)-enriched versus non-HER2-enriched subtypes, AUC = 0.8676 (95% CI, 0.8370-0.8982), accuracy = 0.7737, sensitivity = 0.8859, and specificity = 0.7283. For triple-negative breast cancer versus non-triple-negative breast cancer subtypes, AUC = 0.9335 (95% CI, 0.9027-0.9643), accuracy = 0.9110, sensitivity = 0.4444, and specificity = 0.9865.CONCLUSIONS:
Radiomics based on automatic segmentation of magnetic resonance imaging can predict breast cancer of 4 molecular subtypes noninvasively and is potentially applicable in large samples.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Neoplasias de la Mama
/
Neoplasias de la Mama Triple Negativas
Tipo de estudio:
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Female
/
Humans
Idioma:
En
Revista:
J Comput Assist Tomogr
Año:
2023
Tipo del documento:
Article