{"title":"基于MRI放射学预测乳腺癌隐匿前哨淋巴结转移和D2-40表达。","authors":"Mingming Chen, Chen Wang, Qiyue Zhang, Zhongyuan Li, Peiji Song, Aimei Ouyang","doi":"10.1177/15330338251386676","DOIUrl":null,"url":null,"abstract":"<p><p>IntroductionPatients with Magnetic Resonance Imaging (MRI) axillary lymph node (ALN) negative in breast cancer may still have occult sentinel lymph node (SLN) metastases, which can influence clinical treatment strategies. This study aimed to develop an MRI-based radiomics model for predicting occult SLN metastases and D2-40 expression, and to investigate the intrinsic associations between D2-40 expression, SLN status, and related radiomics features.MethodsThis retrospective study included 141 MRI-diagnosed ALN-negative breast cancer patients from Center 1, randomly divided into training (n = 98) and validation (n = 43) sets (7:3 ratio). An independent external validation cohort (n = 40) from Centers 1 + 2 was established for model validation. Three logistic regression models were developed: (1) a clinical model, (2) a radiomics model, and (3) a clinic-radiomics nomogram, which predict SLN metastasis (Model 1) and D2-40 expression (Model 2). In addition, the correlation between D2-40 expression and SLN status was analyzed in this study using chi-square test. And the feature correlation between SLN radiomics model and D2-40 radiomics model and the strength of association between D2-40 and Model 1 features were assessed by Spearman and Pearson correlation analysis, respectively.ResultsThe nomogram outperformed the other models in both Model 1 (AUC: 0.821 validation/0.746 external) and Model 2 (AUC: 0.810 validation/0.645 external). D2-40 correlated with SLN status (<i>P</i> < .001). There were feature correlations between Model 1 and Model 2 features (Spearman) and between D2-40 and Model 1 features (Pearson).ConclusionsMRI-based radiomics features were effective in predicting occult SLN metastasis and D2-40 expression status in MRI ALN-negative breast cancers. An association was identified between D2-40 expression and SLN status, along with the corresponding radiomics features.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"24 ","pages":"15330338251386676"},"PeriodicalIF":2.8000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12536181/pdf/","citationCount":"0","resultStr":"{\"title\":\"MRI Radiomics-Based Prediction of Occult Sentinel Lymph Node Metastasis and D2-40 Expression in Breast Cancer.\",\"authors\":\"Mingming Chen, Chen Wang, Qiyue Zhang, Zhongyuan Li, Peiji Song, Aimei Ouyang\",\"doi\":\"10.1177/15330338251386676\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>IntroductionPatients with Magnetic Resonance Imaging (MRI) axillary lymph node (ALN) negative in breast cancer may still have occult sentinel lymph node (SLN) metastases, which can influence clinical treatment strategies. This study aimed to develop an MRI-based radiomics model for predicting occult SLN metastases and D2-40 expression, and to investigate the intrinsic associations between D2-40 expression, SLN status, and related radiomics features.MethodsThis retrospective study included 141 MRI-diagnosed ALN-negative breast cancer patients from Center 1, randomly divided into training (n = 98) and validation (n = 43) sets (7:3 ratio). An independent external validation cohort (n = 40) from Centers 1 + 2 was established for model validation. Three logistic regression models were developed: (1) a clinical model, (2) a radiomics model, and (3) a clinic-radiomics nomogram, which predict SLN metastasis (Model 1) and D2-40 expression (Model 2). In addition, the correlation between D2-40 expression and SLN status was analyzed in this study using chi-square test. And the feature correlation between SLN radiomics model and D2-40 radiomics model and the strength of association between D2-40 and Model 1 features were assessed by Spearman and Pearson correlation analysis, respectively.ResultsThe nomogram outperformed the other models in both Model 1 (AUC: 0.821 validation/0.746 external) and Model 2 (AUC: 0.810 validation/0.645 external). D2-40 correlated with SLN status (<i>P</i> < .001). There were feature correlations between Model 1 and Model 2 features (Spearman) and between D2-40 and Model 1 features (Pearson).ConclusionsMRI-based radiomics features were effective in predicting occult SLN metastasis and D2-40 expression status in MRI ALN-negative breast cancers. An association was identified between D2-40 expression and SLN status, along with the corresponding radiomics features.</p>\",\"PeriodicalId\":22203,\"journal\":{\"name\":\"Technology in Cancer Research & Treatment\",\"volume\":\"24 \",\"pages\":\"15330338251386676\"},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2025-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12536181/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Technology in Cancer Research & Treatment\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1177/15330338251386676\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/10/15 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Technology in Cancer Research & Treatment","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/15330338251386676","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/10/15 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"ONCOLOGY","Score":null,"Total":0}
MRI Radiomics-Based Prediction of Occult Sentinel Lymph Node Metastasis and D2-40 Expression in Breast Cancer.
IntroductionPatients with Magnetic Resonance Imaging (MRI) axillary lymph node (ALN) negative in breast cancer may still have occult sentinel lymph node (SLN) metastases, which can influence clinical treatment strategies. This study aimed to develop an MRI-based radiomics model for predicting occult SLN metastases and D2-40 expression, and to investigate the intrinsic associations between D2-40 expression, SLN status, and related radiomics features.MethodsThis retrospective study included 141 MRI-diagnosed ALN-negative breast cancer patients from Center 1, randomly divided into training (n = 98) and validation (n = 43) sets (7:3 ratio). An independent external validation cohort (n = 40) from Centers 1 + 2 was established for model validation. Three logistic regression models were developed: (1) a clinical model, (2) a radiomics model, and (3) a clinic-radiomics nomogram, which predict SLN metastasis (Model 1) and D2-40 expression (Model 2). In addition, the correlation between D2-40 expression and SLN status was analyzed in this study using chi-square test. And the feature correlation between SLN radiomics model and D2-40 radiomics model and the strength of association between D2-40 and Model 1 features were assessed by Spearman and Pearson correlation analysis, respectively.ResultsThe nomogram outperformed the other models in both Model 1 (AUC: 0.821 validation/0.746 external) and Model 2 (AUC: 0.810 validation/0.645 external). D2-40 correlated with SLN status (P < .001). There were feature correlations between Model 1 and Model 2 features (Spearman) and between D2-40 and Model 1 features (Pearson).ConclusionsMRI-based radiomics features were effective in predicting occult SLN metastasis and D2-40 expression status in MRI ALN-negative breast cancers. An association was identified between D2-40 expression and SLN status, along with the corresponding radiomics features.
期刊介绍:
Technology in Cancer Research & Treatment (TCRT) is a JCR-ranked, broad-spectrum, open access, peer-reviewed publication whose aim is to provide researchers and clinicians with a platform to share and discuss developments in the prevention, diagnosis, treatment, and monitoring of cancer.