Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppression and diffusion-weighted MRI.
Eur Radiol
; 28(2): 582-591, 2018 Feb.
Article
en En
| MEDLINE
| ID: mdl-28828635
ABSTRACT
OBJECTIVES:
To predict sentinel lymph node (SLN) metastasis in breast cancer patients using radiomics based on T2-weighted fat suppression (T2-FS) and diffusion-weighted MRI (DWI).METHODS:
We enrolled 146 patients with histologically proven breast cancer. All underwent pretreatment T2-FS and DWI MRI scan. In all, 10,962 texture and four non-texture features were extracted for each patient. The 0.623 + bootstrap method and the area under the curve (AUC) were used to select the features. We constructed ten logistic regression models (orders of 1-10) based on different combination of image features using stepwise forward method.RESULTS:
For T2-FS, model 10 with ten features yielded the highest AUC of 0.847 in the training set and 0.770 in the validation set. For DWI, model 8 with eight features reached the highest AUC of 0.847 in the training set and 0.787 in the validation set. For joint T2-FS and DWI, model 10 with ten features yielded an AUC of 0.863 in the training set and 0.805 in the validation set.CONCLUSIONS:
Full utilisation of breast cancer-specific textural features extracted from anatomical and functional MRI images improves the performance of radiomics in predicting SLN metastasis, providing a non-invasive approach in clinical practice. KEY POINTS ⢠SLN biopsy to access breast cancer metastasis has multiple complications. ⢠Radiomics uses features extracted from medical images to characterise intratumour heterogeneity. ⢠We combined T 2 -FS and DWI textural features to predict SLN metastasis non-invasively.Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Neoplasias de la Mama
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Imagen de Difusión por Resonancia Magnética
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Ganglio Linfático Centinela
/
Ganglios Linfáticos
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Límite:
Female
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Humans
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Middle aged
Idioma:
En
Revista:
Eur Radiol
Asunto de la revista:
RADIOLOGIA
Año:
2018
Tipo del documento:
Article