Non-invasive prediction of axillary lymph node dissection exemption in breast cancer patients post-neoadjuvant therapy: A radiomics and deep learning analysis on longitudinal DCE-MRI data.

Autor:	Yu, Yushuai, Chen, Ruiliang, Yi, Jialu, Huang, Kaiyan, Yu, Xin, Zhang, Jie, Song, Chuangui
Předmět:	CONTRAST-enhanced magnetic resonance imaging DEEP learning LOGISTIC regression analysis NEOADJUVANT chemotherapy SUPPORT vector machines LYMPHATIC metastasis AXILLARY lymph node dissection
Zdroj:	Breast; Oct2024, Vol. 77, pN.PAG-N.PAG, 1p
Abstrakt:	In breast cancer (BC) patients with clinical axillary lymph node metastasis (cN+) undergoing neoadjuvant therapy (NAT), precise axillary lymph node (ALN) assessment dictates therapeutic strategy. There is a critical demand for a precise method to assess the axillary lymph node (ALN) status in these patients. A retrospective analysis was conducted on 160 BC patients undergoing NAT at Fujian Medical University Union Hospital. We analyzed baseline and two-cycle reassessment dynamic contrast-enhanced MRI (DCE-MRI) images, extracting 3668 radiomic and 4096 deep learning features, and computing 1834 delta-radiomic and 2048 delta-deep learning features. Light Gradient Boosting Machine (LightGBM), Support Vector Machine (SVM), RandomForest, and Multilayer Perceptron (MLP) algorithms were employed to develop risk models and were evaluated using 10-fold cross-validation. Of the patients, 61 (38.13 %) achieved ypN0 status post-NAT. Univariate and multivariable logistic regression analyses revealed molecular subtypes and Ki67 as pivotal predictors of achieving ypN0 post-NAT. The SVM-based "Data Amalgamation" model that integrates radiomic, deep learning features, and clinical data, exhibited an outstanding AUC of 0.986 (95 % CI: 0.954–1.000), surpassing other models. Our study illuminates the challenges and opportunities inherent in breast cancer management post-NAT. By introducing a sophisticated, SVM-based "Data Amalgamation" model, we propose a way towards accurate, dynamic ALN assessments, offering potential for personalized therapeutic strategies in BC. • Feasible exemption of axillary lymph node dissection post-NAT. • Developed a precise method for assessing axillary lymph node status post-NAT. • Model using longitudinal DCE-MRI data improves surgical risk stratification. [ABSTRACT FROM AUTHOR]
Databáze:	Supplemental Index
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