Evaluating axillary lymph node metastasis risks in breast cancer patients via Semi-ALNP: a multicenter studyResearch in context
Summary: Background: Accurately evaluating axillary lymph nodes (ALNs) is essential for guiding both staging and treatment strategies in breast cancer (BC) patients. Currently, traditional pathological staging methods still rely on invasive biopsies or surgeries. This study aimed to construct, eval...
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2025-07-01
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| author | Limeng Qu Jinfeng Zhu Xilong Mei Zixi Yi Na Luo Songlin Yuan Xuan Liu Mingwen Liu Haiqing Xie Xiongqiang Hu Liangrui Pan Qingchun Liang Yanhui Li Qiongyan Zou Qin Zhou Danhua Zhang Meirong Zhou Lei Pei Ke Qian Qian Long Qitong Chen Xi Chen Jennifer K. Plichta Qingyao Shang Meishuo Ouyang Jiachi Xu Wenjun Yi |
| author_facet | Limeng Qu Jinfeng Zhu Xilong Mei Zixi Yi Na Luo Songlin Yuan Xuan Liu Mingwen Liu Haiqing Xie Xiongqiang Hu Liangrui Pan Qingchun Liang Yanhui Li Qiongyan Zou Qin Zhou Danhua Zhang Meirong Zhou Lei Pei Ke Qian Qian Long Qitong Chen Xi Chen Jennifer K. Plichta Qingyao Shang Meishuo Ouyang Jiachi Xu Wenjun Yi |
| author_sort | Limeng Qu |
| collection | DOAJ |
| description | Summary: Background: Accurately evaluating axillary lymph nodes (ALNs) is essential for guiding both staging and treatment strategies in breast cancer (BC) patients. Currently, traditional pathological staging methods still rely on invasive biopsies or surgeries. This study aimed to construct, evaluate, and validate a semisupervised classifier utilizing radiomic and machine learning (ML) techniques to noninvasively identify axillary nodal disease. Methods: Data from 4191 ALNs in 494 patients with invasive BC were retrospectively analyzed at the Second Xiangya Hospital of Central South University between January 31, 2016, and July 31, 2024, including a labeled cohort (214 patients, 1769 ALNs, divided into ultra-low and ultra-high risk groups) and an unlabeled cohort (280 patients, 2422 ALNs). Regions of interest (ROIs) were segmented, and CT radiomic features were extracted. 11 supervised learning models were built on the basis of labeled ALNs, and pseudolabels (low-risk, high-risk groups) were assigned to unlabeled ALNs. Seven ML algorithms developed semisupervised multiclassifiers on the basis of the predicted probabilities for 4191 ALNs. For multicenter validation, additional data were collected from the First People’s Hospital of Chenzhou City, the First People’s Hospital of Changde City, and the First People’s Hospital of Xiangtan City. The best-performing multiclassifier was evaluated in two independent multicenter cohorts: 212 clinically node-positive (cN+) patients who underwent core needle biopsy (CNB) or fine needle aspiration (FNA), and 450 clinically node-negative (cN0) patients. The research was registered at www.isrctn.com with registration number ISRCTN54288903. Findings: The supervised multilayer perceptron (MLP) model, built from labeled ALNs, exhibited excellent classification performance, with an area under the curve (AUC) of 0.959 (95% CI: 0.937–0.981), a sensitivity of 0.899, and a specificity of 0.932. Pseudolabels for the unlabeled ALNs were generated via this model, and the semisupervised MLP multiclassifier (Semi-ALNP) was constructed by combining the labeled and unlabeled data. The AUCs for predicting nodal metastases were 0.906 (95% CI: 0.894–0.917), 0.936 (95% CI: 0.928–0.945), 0.948 (95% CI: 0.940–0.956), and 0.955 (95% CI: 0.946–0.965) for the ultra-low risk, low-risk, high-risk, and ultra-high risk groups, respectively. Validation in both the biopsy and cN0 cohorts revealed strong diagnostic performance: in the biopsy cohort, the model achieved a false negative rate (FNR) of 1.21%, a false positive rate (FPR) of 14.89%, a sensitivity of 98.79%, and a specificity of 85.11%; in the cN0 cohort, the FNR was 8.33%, the FPR was 9.94%, the sensitivity was 91.67%, and the specificity was 90.06%. Interpretation: Semi-ALNP, which is based on the MLP algorithm, has high accuracy in assessing the statuses of ALNs across all types of BC patients. It is particularly effective for identifying high-risk patients with ALN metastasis, which can help guide personalized treatment decisions. Future prospective studies are planned to further validate the clinical utility of this approach in real-world settings. Funding: This study was funded by the Science and Technology Innovation Program of Hunan Province (Grant No. 2021SK2026) and the Innovation Platform and Talent Plan of Hunan Province (2023SK4019). Funding sources were not involved in the study design, data collection, analysis and interpretation, writing of the report, or decision to submit the article for publication. |
| format | Article |
| id | doaj-art-ebe69e3a6b98443ebd95217cd854f56f |
| institution | Kabale University |
| issn | 2589-5370 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | EClinicalMedicine |
| spelling | doaj-art-ebe69e3a6b98443ebd95217cd854f56f2025-08-20T03:26:29ZengElsevierEClinicalMedicine2589-53702025-07-018510331110.1016/j.eclinm.2025.103311Evaluating axillary lymph node metastasis risks in breast cancer patients via Semi-ALNP: a multicenter studyResearch in contextLimeng Qu0Jinfeng Zhu1Xilong Mei2Zixi Yi3Na Luo4Songlin Yuan5Xuan Liu6Mingwen Liu7Haiqing Xie8Xiongqiang Hu9Liangrui Pan10Qingchun Liang11Yanhui Li12Qiongyan Zou13Qin Zhou14Danhua Zhang15Meirong Zhou16Lei Pei17Ke Qian18Qian Long19Qitong Chen20Xi Chen21Jennifer K. Plichta22Qingyao Shang23Meishuo Ouyang24Jiachi Xu25Wenjun Yi26Department of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China; Clinical Research Center For Breast Disease In Hunan Province, Changsha, Hunan, 410011, ChinaDepartment of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China; Clinical Research Center For Breast Disease In Hunan Province, Changsha, Hunan, 410011, ChinaDepartment of Radiology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, 410011, ChinaMathematics, Central South University, Changsha, Hunan, 410083, ChinaBreast and Thyroid Surgery, The First People’s Hospital of Changde City, Changde, Hunan, 415000, ChinaBreast and Thyroid Surgery, The First People’s Hospital of Changde City, Changde, Hunan, 415000, ChinaBreast and Thyroid Surgery, The First People’s Hospital of Xiangtan City, Xiangtan, Hunan, 411100, ChinaBreast and Thyroid Surgery, The First People’s Hospital of Xiangtan City, Xiangtan, Hunan, 411100, ChinaBreast and Thyroid Surgery, The First People’s Hospital of Chenzhou City, Chenzhou, Hunan, 423000, ChinaBreast and Thyroid Surgery, The First People’s Hospital of Chenzhou City, Chenzhou, Hunan, 423000, ChinaCollege of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, 410083, ChinaDepartment of Pathology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, ChinaDepartment of Radiology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, 410011, ChinaDepartment of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China; Clinical Research Center For Breast Disease In Hunan Province, Changsha, Hunan, 410011, ChinaDepartment of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China; Clinical Research Center For Breast Disease In Hunan Province, Changsha, Hunan, 410011, ChinaDepartment of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China; Clinical Research Center For Breast Disease In Hunan Province, Changsha, Hunan, 410011, ChinaDepartment of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China; Clinical Research Center For Breast Disease In Hunan Province, Changsha, Hunan, 410011, ChinaDepartment of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China; Clinical Research Center For Breast Disease In Hunan Province, Changsha, Hunan, 410011, ChinaDepartment of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China; Clinical Research Center For Breast Disease In Hunan Province, Changsha, Hunan, 410011, ChinaDepartment of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China; Clinical Research Center For Breast Disease In Hunan Province, Changsha, Hunan, 410011, ChinaDepartment of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China; Clinical Research Center For Breast Disease In Hunan Province, Changsha, Hunan, 410011, ChinaDepartment of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China; Clinical Research Center For Breast Disease In Hunan Province, Changsha, Hunan, 410011, ChinaDuke Cancer Institute, Duke University Medical Center, Durham, NC, USA; Department of Population Health Sciences, Duke University School of Medicine, Durham, NC, USA; Department of Surgery, Duke University School of Medicine, Durham, NC, USANational Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Chaoyang, Beijing, 100021, ChinaDepartment of Surgery, Duke University School of Medicine, Duke University, Durham, NC, USADepartment of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China; Clinical Research Center For Breast Disease In Hunan Province, Changsha, Hunan, 410011, China; Corresponding author. Department of General Surgery, The Second Xiangya Hospital of Central South University, No. 139, Renmin Central Road, Changsha, 410011, China.Department of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China; Clinical Research Center For Breast Disease In Hunan Province, Changsha, Hunan, 410011, China; Corresponding author. Department of General Surgery, The Second Xiangya Hospital of Central South University, No. 139, Renmin Central Road, Changsha, 410011, China.Summary: Background: Accurately evaluating axillary lymph nodes (ALNs) is essential for guiding both staging and treatment strategies in breast cancer (BC) patients. Currently, traditional pathological staging methods still rely on invasive biopsies or surgeries. This study aimed to construct, evaluate, and validate a semisupervised classifier utilizing radiomic and machine learning (ML) techniques to noninvasively identify axillary nodal disease. Methods: Data from 4191 ALNs in 494 patients with invasive BC were retrospectively analyzed at the Second Xiangya Hospital of Central South University between January 31, 2016, and July 31, 2024, including a labeled cohort (214 patients, 1769 ALNs, divided into ultra-low and ultra-high risk groups) and an unlabeled cohort (280 patients, 2422 ALNs). Regions of interest (ROIs) were segmented, and CT radiomic features were extracted. 11 supervised learning models were built on the basis of labeled ALNs, and pseudolabels (low-risk, high-risk groups) were assigned to unlabeled ALNs. Seven ML algorithms developed semisupervised multiclassifiers on the basis of the predicted probabilities for 4191 ALNs. For multicenter validation, additional data were collected from the First People’s Hospital of Chenzhou City, the First People’s Hospital of Changde City, and the First People’s Hospital of Xiangtan City. The best-performing multiclassifier was evaluated in two independent multicenter cohorts: 212 clinically node-positive (cN+) patients who underwent core needle biopsy (CNB) or fine needle aspiration (FNA), and 450 clinically node-negative (cN0) patients. The research was registered at www.isrctn.com with registration number ISRCTN54288903. Findings: The supervised multilayer perceptron (MLP) model, built from labeled ALNs, exhibited excellent classification performance, with an area under the curve (AUC) of 0.959 (95% CI: 0.937–0.981), a sensitivity of 0.899, and a specificity of 0.932. Pseudolabels for the unlabeled ALNs were generated via this model, and the semisupervised MLP multiclassifier (Semi-ALNP) was constructed by combining the labeled and unlabeled data. The AUCs for predicting nodal metastases were 0.906 (95% CI: 0.894–0.917), 0.936 (95% CI: 0.928–0.945), 0.948 (95% CI: 0.940–0.956), and 0.955 (95% CI: 0.946–0.965) for the ultra-low risk, low-risk, high-risk, and ultra-high risk groups, respectively. Validation in both the biopsy and cN0 cohorts revealed strong diagnostic performance: in the biopsy cohort, the model achieved a false negative rate (FNR) of 1.21%, a false positive rate (FPR) of 14.89%, a sensitivity of 98.79%, and a specificity of 85.11%; in the cN0 cohort, the FNR was 8.33%, the FPR was 9.94%, the sensitivity was 91.67%, and the specificity was 90.06%. Interpretation: Semi-ALNP, which is based on the MLP algorithm, has high accuracy in assessing the statuses of ALNs across all types of BC patients. It is particularly effective for identifying high-risk patients with ALN metastasis, which can help guide personalized treatment decisions. Future prospective studies are planned to further validate the clinical utility of this approach in real-world settings. Funding: This study was funded by the Science and Technology Innovation Program of Hunan Province (Grant No. 2021SK2026) and the Innovation Platform and Talent Plan of Hunan Province (2023SK4019). Funding sources were not involved in the study design, data collection, analysis and interpretation, writing of the report, or decision to submit the article for publication.http://www.sciencedirect.com/science/article/pii/S2589537025002433Breast cancerAxillary lymph nodeCT radiomicsMachine learningSemi-supervised classifier |
| spellingShingle | Limeng Qu Jinfeng Zhu Xilong Mei Zixi Yi Na Luo Songlin Yuan Xuan Liu Mingwen Liu Haiqing Xie Xiongqiang Hu Liangrui Pan Qingchun Liang Yanhui Li Qiongyan Zou Qin Zhou Danhua Zhang Meirong Zhou Lei Pei Ke Qian Qian Long Qitong Chen Xi Chen Jennifer K. Plichta Qingyao Shang Meishuo Ouyang Jiachi Xu Wenjun Yi Evaluating axillary lymph node metastasis risks in breast cancer patients via Semi-ALNP: a multicenter studyResearch in context EClinicalMedicine Breast cancer Axillary lymph node CT radiomics Machine learning Semi-supervised classifier |
| title | Evaluating axillary lymph node metastasis risks in breast cancer patients via Semi-ALNP: a multicenter studyResearch in context |
| title_full | Evaluating axillary lymph node metastasis risks in breast cancer patients via Semi-ALNP: a multicenter studyResearch in context |
| title_fullStr | Evaluating axillary lymph node metastasis risks in breast cancer patients via Semi-ALNP: a multicenter studyResearch in context |
| title_full_unstemmed | Evaluating axillary lymph node metastasis risks in breast cancer patients via Semi-ALNP: a multicenter studyResearch in context |
| title_short | Evaluating axillary lymph node metastasis risks in breast cancer patients via Semi-ALNP: a multicenter studyResearch in context |
| title_sort | evaluating axillary lymph node metastasis risks in breast cancer patients via semi alnp a multicenter studyresearch in context |
| topic | Breast cancer Axillary lymph node CT radiomics Machine learning Semi-supervised classifier |
| url | http://www.sciencedirect.com/science/article/pii/S2589537025002433 |
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