Machine Learning Radiomics-Based Prediction of Non-sentinel Lymph Node Metastasis in Chinese Breast Cancer Patients with 1-2 Positive Sentinel Lymph Nodes: A Multicenter Study

Guihan Lin; Weiyue Chen; Yingying Fan; Yi Zhou; Xia Li; Xin Hu; Xue Cheng; Mingzhen Chen; Chunli Kong; Minjiang Chen; Min Xu; Zhiyi Peng; Jiansong Ji

doi:10.1016/j.acra.2024.02.010

Machine Learning Radiomics-Based Prediction of Non-sentinel Lymph Node Metastasis in Chinese Breast Cancer Patients with 1-2 Positive Sentinel Lymph Nodes: A Multicenter Study

Acad Radiol. 2024 Mar 14:S1076-6332(24)00080-1. doi: 10.1016/j.acra.2024.02.010. Online ahead of print.

Authors

Guihan Lin¹, Weiyue Chen¹, Yingying Fan², Yi Zhou¹, Xia Li¹, Xin Hu³, Xue Cheng¹, Mingzhen Chen⁴, Chunli Kong¹, Minjiang Chen¹, Min Xu⁵, Zhiyi Peng⁶, Jiansong Ji⁷

Affiliations

¹ Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China; Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui 323000, China.
² Department of Stomatology, Zhongnan Hospital of Wuhan University, Wuhan 430071, China.
³ School of Medicine, Shaoxing University, Shaoxing 312000, China.
⁴ Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China; Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China.
⁵ Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China; Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui 323000, China; School of Medicine, Shaoxing University, Shaoxing 312000, China.
⁶ Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China.
⁷ Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China; Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui 323000, China; School of Medicine, Shaoxing University, Shaoxing 312000, China. Electronic address: jjstcty@wmu.edu.cn.

PMID: 38490840
DOI: 10.1016/j.acra.2024.02.010

Abstract

Rationale and objectives: This study aimed to construct a machine learning radiomics-based model using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images to evaluate non-sentinel lymph node (NSLN) metastasis in Chinese breast cancer (BC) patients who underwent total mastectomy (TM) and had 1-2 positive sentinel lymph nodes (SLNs).

Materials and methods: In total, 494 patients were retrospectively enrolled from two hospitals, and were divided into the training (n = 286), internal validation (n = 122), and external validation (n = 86) cohorts. Features were extracted from DCE-MRI images for each patient and screened. Six ML classifies were trained and the best classifier was evaluated to calculate radiomics (Rad)-scores. A combined model was developed based on Rad-scores and clinical risk factors, then the calibration, discrimination, reclassification, and clinical usefulness were evaluated.

Results: 14 radiomics features were ultimately selected. The random forest (RF) classifier showed the best performance, with the highest average area under the curve (AUC) of 0.833 in the validation cohorts. The combined model incorporating RF-based Rad-scores, tumor size, lymphovascular invasion, and proportion of positive SLNs resulted in the best discrimination ability, with AUCs of 0.903, 0.890, and 0.836 in the training, internal validation, and external validation cohorts, respectively. Furthermore, the combined model significantly improved the classification accuracy and clinical benefit for NSLN metastasis prediction.

Conclusion: A RF-based combined model using DCE-MRI images exhibited a promising performance for predicting NSLN metastasis in Chinese BC patients who underwent TM and had 1-2 positive SLNs, thereby aiding in individualized clinical treatment decisions.

Keywords: Axillary management; Breast cancer; Machine learning; Non-sentinel lymph node; Radiomics.