Early prediction of neoadjuvant chemotherapy response by exploiting a transfer learning approach on breast DCE-MRIs.
Sci Rep
; 11(1): 14123, 2021 07 08.
Article
em En
| MEDLINE
| ID: mdl-34238968
ABSTRACT
The dynamic contrast-enhanced MR imaging plays a crucial role in evaluating the effectiveness of neoadjuvant chemotherapy (NAC) even since its early stage through the prediction of the final pathological complete response (pCR). In this study, we proposed a transfer learning approach to predict if a patient achieved pCR (pCR) or did not (non-pCR) by exploiting, separately or in combination, pre-treatment and early-treatment exams from I-SPY1 TRIAL public database. First, low-level features, i.e., related to local structure of the image, were automatically extracted by a pre-trained convolutional neural network (CNN) overcoming manual feature extraction. Next, an optimal set of most stable features was detected and then used to design an SVM classifier. A first subset of patients, called fine-tuning dataset (30 pCR; 78 non-pCR), was used to perform the optimal choice of features. A second subset not involved in the feature selection process was employed as an independent test (7 pCR; 19 non-pCR) to validate the model. By combining the optimal features extracted from both pre-treatment and early-treatment exams with some clinical features, i.e., ER, PgR, HER2 and molecular subtype, an accuracy of 91.4% and 92.3%, and an AUC value of 0.93 and 0.90, were returned on the fine-tuning dataset and the independent test, respectively. Overall, the low-level CNN features have an important role in the early evaluation of the NAC efficacy by predicting pCR. The proposed model represents a first effort towards the development of a clinical support tool for an early prediction of pCR to NAC.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Mama
/
Neoplasias da Mama
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Imageamento por Ressonância Magnética
Tipo de estudo:
Diagnostic_studies
/
Guideline
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Adult
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Female
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Humans
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Middle aged
Idioma:
En
Revista:
Sci Rep
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Itália