Related Experiment Video
Updated: Sep 2, 2025
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
A Nomogram Based on Hematological Parameters and Clinicopathological Characteristics for Predicting Local-Regional
Luhao Sun1, Wei Zhao1, Fukai Wang1
1Breast Cancer Center, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
Objectives:
The aim of this study was to identify the factors for local-regional recurrence (LRR) after breast-conserving therapy (BCT). We established a practical nomogram to predict the likelihood of LRR after BCT based on hematological parameters and clinicopathological features.
Methods:
A retrospective analysis was performed on 2,085 consecutive breast cancer patients who received BCT in Shandong Cancer Hospital from 2006 to 2016, including 1,460 patients in the training cohort and 625 patients 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 factors for LRR. Subsequently, a nomogram for predicting LRR was established by logistic regression analysis. The nomogram was validated in 625 patients in the validation cohort.
Results:
During the median follow-up period of 66 months, 44 (3.01%) patients in the training cohort and 19 (3.04%) patients in the validation cohort suffered from LRR. Multivariate analysis showed six independent 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 LRR. The nomogram of LRR showed excellent discrimination and prediction accuracy. The area under the receiver operating characteristic curve (AUC) was 0.89 (p < 0.001, 95% CI = 0.83, 0.95) in the training cohort and 0.88 (p < 0.001, 95% CI = 0.8, 0.96) in the validation cohort. Calibration curves for the prediction model 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 LRR after BCT. 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.
