Individualized prediction of non-sentinel lymph node metastasis in Chinese breast cancer patients with ≥ 3 positive sentinel lymph nodes based on machine-learning algorithms

• Published in: BMC cancer Vol. 24; no. 1; pp. 1090 - 16
• Main Authors: Xie, Xiangli, Fang, Yutong, He, Lifang, Chen, Zexiao, Chen, Chunfa, Zeng, Huancheng, Chen, Bingfeng, Huang, Guangsheng, Guo, Cuiping, Zhang, Qunchen, Wu, Jundong
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 02-09-2024; BioMed Central; BMC
• Subjects: Accuracy; Adult; Aged; Algorithms; Axilla; Axillary lymph node dissection; Biopsy; Breast cancer; Breast Neoplasms - pathology; Breast Neoplasms - surgery; Cancer patients; Cancer therapies; Care and treatment; China - epidemiology; Clinical medicine; Clinical trials; Complications; Data mining; Decision trees; Development and progression; Diagnosis; East Asian People; Endocrine therapy; Feature selection; Female; Gene amplification; Health aspects; Humans; Learning algorithms; Lymph Node Excision; Lymph nodes; Lymph Nodes - pathology; Lymph Nodes - surgery; Lymphatic metastasis; Lymphatic Metastasis - pathology; Lymphatic system; Lymphedema; Machine Learning; Mastectomy; Medical colleges; Medical prognosis; Metastases; Metastasis; Methods; Methylene blue; Middle Aged; Non-sentinel lymph node metastasis; Patient outcomes; Patients; Pharmaceuticals; Postoperative; Quality of life; Radiation therapy; Regression analysis; Retrospective Studies; ROC Curve; Sentinel Lymph Node - pathology; Sentinel Lymph Node - surgery; Sentinel lymph node biopsy; Sentinel Lymph Node Biopsy - methods; Surgery; Survival analysis; China; Canada; Non-sentinel lymph node metastasis; Breast cancer; Axillary lymph node dissection; Machine-learning; Sentinel lymph node biopsy
• ISSN: 1471-2407; 1471-2407
• DOI: 10.1186/s12885-024-12870-x

Axillary lymph node dissection (ALND) is a standard procedure for early-stage breast cancer (BC) patients with three or more positive sentinel lymph nodes (SLNs). However, ALND can lead to significant postoperative complications without always providing additional clinical benefits. This study aims to develop machine-learning (ML) models to predict non-sentinel lymph node (non-SLN) metastasis in Chinese BC patients with three or more positive SLNs, potentially allowing the omission of ALND.
Data from 2217 BC patients who underwent SLN biopsy at Shantou University Medical College were analyzed, with 634 having positive SLNs. Patients were categorized into those with ≤ 2 positive SLNs and those with ≥ 3 positive SLNs. We applied nine ML algorithms to predict non-SLN metastasis. Model performance was evaluated using ROC curves, precision-recall curves, and calibration curves. Decision Curve Analysis (DCA) assessed the clinical utility of the models.
The RF model showed superior predictive performance, achieving an AUC of 0.987 in the training set and 0.828 in the validation set. Key predictive features included size of positive SLNs, tumor size, number of SLNs, and ER status. In external validation, the RF model achieved an AUC of 0.870, demonstrating robust predictive capabilities.
The developed RF model accurately predicts non-SLN metastasis in BC patients with ≥ 3 positive SLNs, suggesting that ALND might be avoided in selected patients by applying additional axillary radiotherapy. This approach could reduce the incidence of postoperative complications and improve patient quality of life. Further validation in prospective clinical trials is warranted.