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Predicting axillary lymph node metastasis in breast cancer patients: A radiomics-based multicenter approach with interpretability analysis.
Liu, Zilin; Hong, Minping; Li, Xinhua; Lin, Lifu; Tan, Xueyuan; Liu, Yushuang.
Afiliación
  • Liu Z; Department of Radiology and Imaging, The Second Affiliated Hospital of Guangdong Medical University, Minyou Road, Zhanjiang, 524000, China.
  • Hong M; Department of Radiology, Jiaxing Hospital of Traditional Chinese Medical, Zhejiang, 310060, China.
  • Li X; Department of Radiology, The Affiliated Hospital of Guangdong Medical University, Wenming East Road, Zhanjiang, 524000, China.
  • Lin L; Department of Radiology and Imaging, The Second Affiliated Hospital of Guangdong Medical University, Minyou Road, Zhanjiang, 524000, China.
  • Tan X; Department of Radiology and Imaging, The Second Affiliated Hospital of Guangdong Medical University, Minyou Road, Zhanjiang, 524000, China.
  • Liu Y; Department of Radiology and Imaging, The Second Affiliated Hospital of Guangdong Medical University, Minyou Road, Zhanjiang, 524000, China. Electronic address: 1534744160@qq.com.
Eur J Radiol ; 176: 111522, 2024 Jul.
Article en En | MEDLINE | ID: mdl-38805883
ABSTRACT

PURPOSE:

To develop a MRI-based radiomics model, integrating the intratumoral and peritumoral imaging information to predict axillary lymph node metastasis (ALNM) in patients with breast cancer and to elucidate the model's decision-making process via interpretable algorithms.

METHODS:

This study included 376 patients from three institutions who underwent contrast-enhanced breast MRI between 2021 and 2023. We used multiple machine learning algorithms to combine peritumoral, intratumoral, and radiological characteristics with the building of radiological, radiomics, and combined models. The model's performance was compared based on the area under the curve (AUC) obtained from the receiver operating characteristic analysis and interpretable machine learning techniques to analyze the operating mechanism of the model.

RESULTS:

The radiomics model, incorporating features from both intratumoral tissue and the 3 mm peritumoral region and utilizing the backpropagation neural network (BPNN) algorithm, demonstrated superior diagnostic efficacy, achieving an AUC of 0.820. The AUC of the combination of the RAD score, clinical T stage, and spiculated margin was as high as 0.855. Furthermore, we conducted SHapley Additive exPlanations (SHAP) analysis to evaluate the contributions of RAD score, clinical T stage, and spiculated margin in ALNM status prediction.

CONCLUSIONS:

The interpretable radiomics model we propose can better predict the ALNM status of breast cancer and help inform clinical treatment decisions.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Axila / Neoplasias de la Mama / Imagen por Resonancia Magnética / Metástasis Linfática Límite: Adult / Aged / Female / Humans / Middle aged Idioma: En Revista: Eur J Radiol Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Irlanda
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Axila / Neoplasias de la Mama / Imagen por Resonancia Magnética / Metástasis Linfática Límite: Adult / Aged / Female / Humans / Middle aged Idioma: En Revista: Eur J Radiol Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Irlanda