A Nomogram Based on Hematological Parameters and Clinicopathological Characteristics for Predicting Local–Regional Recurrence After Breast-Conserving Therapy - Sun, Luhao; Zhao, Wei; Wang, Fukai; Song, Xiang; Wang, Xinzhao; Li, Chao; Yu, Zhiyong (Frontiers in Oncology, 2022)
Abstract Objectives: This study aims to identify the risk factors for local-regional recurrence (LRR) after breast-conserving therapy (BCT) and establish a practical nomogram for predicting the possibility of an LRR model after BCT based on hematological parameters and clinicopathological characteristics. Methods: A total of 2085 consecutive patients diagnosed with breast cancer who received BCT in Shandong Cancer Hospital from 2006 to 2016 were retrospectively included and analyzed, including 1460 in the training cohort and 625 in the validation cohort. Univariate and multivariate analyses were performed based on hematological parameters (fibrinogen, platelets, mean platelet volume, neutrophils, monocytes, and lymphocytes) and clinicopathological characteristics to identify the independent risk factors for LRR. Subsequently, a prognostic nomogram was established to individualize the LRR by logistic regression analysis. The nomogram was validated in a separate cohort of 625 patients from the original randomized clinical trial (the validation cohort). Results: During the 66-month median follow-up period, 44 (3.01%) patients suffered LRR in the training cohort and 19 (3.04%) in the validation cohort. Multivariate analysis showed six independent risk factors related to LRR, including molecular subtype, pathological N stage, re-resection, radiotherapy or not, platelet count*MPV*fibrinogen (PMF), and neutrophil count/lymphocyte count ratio (NLR). Six variables were entered into logistic regression to establish the nomogram for predicting the probability of LRR. The nomogram of LRR showed excellent discriminative ability and predictive accuracy. The area under the receiver operating characteristic (AUC) was 0.89 (P<0.001, 95%CI=0.83, 0.95) in the training cohort and 0.86 (P<0.001, 95%CI=0.77, 0.96) in the validation cohort. Calibration curves for the LRR probability in the training and validation cohorts both demonstrated satisfactory consistency between the nomogram-predicted and actual LRR. Conclusion: The combination of hematological parameters and clinicopathological characteristics can predict local-regional recurrence after BCT. A predictive nomogram based on hematological parameters might serve as a practical tool for individualized prognostication. The predictive nomogram based on preoperative and postoperative indicators of BCT might serve as a practical tool for individualized prognostication. More prospective studies should be performed to verify the model.