Noninvasive identification of HER2-low-positive status by MRI-based deep learning radiomics predicts the disease-free survival of patients with breast cancer.
Eur Radiol
; 34(2): 899-913, 2024 Feb.
Article
en En
| MEDLINE
| ID: mdl-37597033
ABSTRACT
OBJECTIVE:
This study aimed to establish a MRI-based deep learning radiomics (DLR) signature to predict the human epidermal growth factor receptor 2 (HER2)-low-positive status and further verified the difference in prognosis by the DLR model.METHODS:
A total of 481 patients with breast cancer who underwent preoperative MRI were retrospectively recruited from two institutions. Traditional radiomics features and deep semantic segmentation feature-based radiomics (DSFR) features were extracted from segmented tumors to construct models separately. Then, the DLR model was constructed to assess the HER2 status by averaging the output probabilities of the two models. Finally, a KaplanâMeier survival analysis was conducted to explore the disease-free survival (DFS) in patients with HER2-low-positive status. The multivariate Cox proportional hazard model was constructed to further determine the factors associated with DFS.RESULTS:
First, the DLR model distinguished between HER2-negative and HER2-overexpressing patients with AUCs of 0.868 and 0.763 in the training and validation cohorts, respectively. Furthermore, the DLR model distinguished between HER2-low-positive and HER2-zero patients with AUCs of 0.855 and 0.750, respectively. Cox regression analysis showed that the prediction score obtained using the DLR model (HR, 0.175; p = 0.024) and lesion size (HR, 1.043; p = 0.009) were significant, independent predictors of DFS.CONCLUSIONS:
We successfully constructed a DLR model based on MRI to noninvasively evaluate the HER2 status and further revealed prospects for predicting the DFS of patients with HER2-low-positive status. CLINICAL RELEVANCE STATEMENT The MRI-based DLR model could noninvasively identify HER2-low-positive status, which is considered a novel prognostic predictor and therapeutic target. KEY POINTS ⢠The DLR model effectively distinguished the HER2 status of breast cancer patients, especially the HER2-low-positive status. ⢠The DLR model was better than the traditional radiomics model or DSFR model in distinguishing HER2 expression. ⢠The prediction score obtained using the model and lesion size were significant independent predictors of DFS.Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Neoplasias de la Mama
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Aprendizaje Profundo
Tipo de estudio:
Diagnostic_studies
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Prognostic_studies
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Risk_factors_studies
Límite:
Female
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Humans
Idioma:
En
Año:
2024
Tipo del documento:
Article