| Zhang Siyuan,Peng Yushuo,Shi Chen,et al.A predictive model for internal mammary node clinical complete response after neoadjuvant therapy for breast cancer[J].Chinese Journal of Radiological Medicine and Protection,2026,46(4):359-366 |
| A predictive model for internal mammary node clinical complete response after neoadjuvant therapy for breast cancer |
| Received:December 08, 2025 |
| DOI：10.3760/cma.j.cn112271-20251208-00424 |
| KeyWords:Breast cancer|Internal mammary node|Neoadjuvant therapy|Nomogram|Treatment response |
| FundProject: |
| Author Name | Affiliation | E-mail | | Zhang Siyuan | Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), the Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China | | | Peng Yushuo | Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), the Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China | | | Shi Chen | Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), the Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China | | | Xiang Yirong | Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), the Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China | | | Guo Changkuo | Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), the Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China | | | Tie Jian | Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), the Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China | tie@yeah.net |
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| Abstract:: |
| Objective To develop and validate a nomogram model that integrates baseline clinicopathological and imaging characteristics to predict internal mammary node (IMN) clinical complete response (icCR) after neoadjuvant therapy for breast cancer patients. Methods A retrospective analysis was conducted on 208 breast cancer patients with baseline positive IMNs who were treated between 2018 and 2024. Predictors were selected from 33 candidate variables using the least absolute shrinkage and selection operator (LASSO) regression method with 10-fold cross-validation. Then, based on the selected predictors, a multivariable logistic regression model in the form of a nomogram was constructed. Internal validity was conducted for the model using 1 000 bootstrap resamples, followed by a comprehensive performance evaluation from three dimensions: discrimination, calibration, and clinical utility. Results Multivariable logistic regression analysis indicated the independent predictors of icCR included clinical N stage (cN2-3 vs. cN0-1: OR = 0.31, P = 0.001), molecular subtype (luminal vs. HER2-enriched: OR = 0.31, P = 0.005; triple-negative vs. HER2-enriched: OR = 0.24, P = 0.005), and the number of positive IMNs (> 1 vs. 1: OR = 2.09, P = 0.033) The final model incorporated seven predictors, yielding an area under the curve (AUC) of 0.762 and a bootstrap-corrected AUC of 0.728. Decision curve analysis (DCA) confirms that the occurrence of clinical net benefits across a wide risk threshold range of 17%-93%. Conclusions The nomogram model developed and validated in this study for the individualized icCR prediction following neoadjuvant therapy can provide a basis for decision-making in IMN de-escalated radiotherapy for patients for whom post-treatment imaging assessment is unavailable. |
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