Oncology research and treatment · 2025
Nomogram Based on the Dynamic Change in the Neutrophil-to-Lymphocyte Ratio for Predicting the Pathological Complete Response of Breast Cancer after Neoadjuvant Systemic Therapy.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Gong Yichun, Wang Lexin, Yin Hong, Wang Mingyu, Shi Wenjie, Wang Jue, Xu Lu, Zha Xiaoming
Аннотация
INTRODUCTION: This study aimed to investigate the predictive effect of peripheral blood inflammatory indexes on total pathologic complete response (tpCR) in patients with breast cancer receiving neoadjuvant systemic therapy (NST).
METHODS: We identified significant prognostic factors for tpCR in the training cohort using univariate and multivariate logistic analysis to build a nomogram based on multicenter data. The performance of the model underwent 1,000-bootstrap resample internal validation and external validation. The area under the receiver operating characteristic (AUC) curve and the calibration curve were used to measure predictive accuracy and discriminative ability. This study was conducted under the Declaration of Helsinki and the approval and supervision of the Ethics Review Committee (2020-SR-053) retrospectively registered.
RESULTS: This retrospective study included 353 patients with breast cancer receiving NST, including 244 and 109 patients in the training and the external validation cohort. Multivariate logistic regression analysis revealed ER status, PR status, HER2 status, T stage, baseline lymphocyte, and percentage change in neutrophil-to-lymphocyte ratio (NLR, an immune system status-associated indicator) as independent predictors of tpCR. Baseline NLR in the tpCR group was significantly lower than that in the non-tpCR group, but percentage change in the NLR was significantly higher in the tpCR group, exhibiting opposite predictive trends. A nomogram was developed based on these results. The AUC curve of the training cohort, bootstrap resampling internal validation, and external validation cohort were 0.832, 0.806, and 0.814, respectively. The calibration curve for the probability of tpCR revealed optimal agreement between the probability and the actual probability. The subgroup analysis revealed that baseline NLR was significantly correlated with tpCR in patients with HER2 overexpression and luminal breast cancer.
CONCLUSION: A nomogram based on the dynamic change in the NLR was developed, thereby helping adjusting treatment plans because NST may change the functional phenotype of some inflammatory cells and affect tumor microenvironment.
METHODS: We identified significant prognostic factors for tpCR in the training cohort using univariate and multivariate logistic analysis to build a nomogram based on multicenter data. The performance of the model underwent 1,000-bootstrap resample internal validation and external validation. The area under the receiver operating characteristic (AUC) curve and the calibration curve were used to measure predictive accuracy and discriminative ability. This study was conducted under the Declaration of Helsinki and the approval and supervision of the Ethics Review Committee (2020-SR-053) retrospectively registered.
RESULTS: This retrospective study included 353 patients with breast cancer receiving NST, including 244 and 109 patients in the training and the external validation cohort. Multivariate logistic regression analysis revealed ER status, PR status, HER2 status, T stage, baseline lymphocyte, and percentage change in neutrophil-to-lymphocyte ratio (NLR, an immune system status-associated indicator) as independent predictors of tpCR. Baseline NLR in the tpCR group was significantly lower than that in the non-tpCR group, but percentage change in the NLR was significantly higher in the tpCR group, exhibiting opposite predictive trends. A nomogram was developed based on these results. The AUC curve of the training cohort, bootstrap resampling internal validation, and external validation cohort were 0.832, 0.806, and 0.814, respectively. The calibration curve for the probability of tpCR revealed optimal agreement between the probability and the actual probability. The subgroup analysis revealed that baseline NLR was significantly correlated with tpCR in patients with HER2 overexpression and luminal breast cancer.
CONCLUSION: A nomogram based on the dynamic change in the NLR was developed, thereby helping adjusting treatment plans because NST may change the functional phenotype of some inflammatory cells and affect tumor microenvironment.