Performance of radiomics models for tumour-infiltrating lymphocyte (TIL) prediction in breast cancer: the role of the dynamic contrast-enhanced (DCE) MRI phase.
Eur Radiol
; 32(2): 864-875, 2022 Feb.
Article
en En
| MEDLINE
| ID: mdl-34430998
ABSTRACT
OBJECTIVE:
To systematically investigate the effect of imaging features at different DCE-MRI phases to optimise a radiomics model based on DCE-MRI for the prediction of tumour-infiltrating lymphocyte (TIL) levels in breast cancer. MATERIALS ANDMETHODS:
This study retrospectively collected 133 patients with pathologically proven breast cancer, including 73 patients with low TIL levels and 60 patients with high TIL levels. The volumes of breast cancer lesions were manually delineated on T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and each phase of DCE-MRI, followed by 6250 quantitative feature extractions. The least absolute shrinkage and selection operator (LASSO) method was used to select predictive feature sets for the classifiers. Four models were developed for predicting TILs (1) single enhanced phase radiomics models; (2) fusion enhanced multi-phase radiomics models; (3) fusion multi-sequence radiomics models; and (4) a combined radiomics-based clinical model.RESULTS:
Image features extracted from the delayed phase MRI, especially DCE_Phase 6 (DCE_P6), demonstrated dominant predictive performances over features from other phases. The fusion multi-sequence radiomics model and combined radiomics-based clinical model achieved the highest predictive performances with areas under the curve (AUCs) of 0.934 and 0.950, respectively; however, the differences were not statistically significant.CONCLUSION:
The DCE-MRI radiomics model, especially image features extracted from the delayed phases, can help improve the performance in predicting TILs. The radiomics nomogram is effective in predicting TILs in breast cancer. KEY POINTS ⢠Radiomics features extracted from DCE-MRI, especially delayed phase images, help predict TIL levels in breast cancer. ⢠We developed a nomogram based on MRI to predict TILs in breast cancer that achieved the highest AUC of 0.950.Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Neoplasias de la Mama
Tipo de estudio:
Observational_studies
/
Prognostic_studies
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Risk_factors_studies
Límite:
Female
/
Humans
Idioma:
En
Revista:
Eur Radiol
Asunto de la revista:
RADIOLOGIA
Año:
2022
Tipo del documento:
Article
País de afiliación:
China