Abstract:

Objective

To evaluate the clinical value of a postoperative recurrence risk prediction model for luminal B breast cancer based on preoperative automated breast ultrasound （ABUS） images combined with clinicopathological features.

Methods

A retrospective analysis was conducted on 293 female patients with luminal B breast cancer who were diagnosed and underwent surgical treatment at Xijing Hospital from January 2016 to December 2019.All patients underwent preoperative ABUS examination.Patients were categorized into a recurrence group and a non-recurrence group based on their recurrence status.Differences in clinicopathological characteristics and ABUS imaging features between the two groups were compared.Factors identified to have statistical significance in the univariate analysis were included in a multivariate Cox regression model to identify independent risk factors for postoperative recurrence in luminal B breast cancer patients.Based on the identified independent risk factors， three predictive models were constructed： a clinicopathological model， an ABUS model，and a combination model.Receiver operating characteristic （ROC） curves were generated to assess the predictive performance of each model.The area under the ROC curve （AUC） values of the three models were compared using the DeLong test.The model with the best performance was used to develop a nomogram， and its internal validation was performed using the Bootstrap method.Calibration curves and decision curve analysis （DCA） were used to evaluate the efficacy of the optimal model.Kaplan-Meier survival curves stratified by different recurrence risk factors were constructed， and the differences were tested using the Log-rank test.

Results

Among the 293 postoperative luminal B breast cancer patients， 36 experienced recurrence during a median follow-up of 68 months，resulting in a postoperative recurrence rate of 12.3% （36/293）.Significant differences were observed between the recurrence and non-recurrence groups in N stage， histological grade， Ki-67 expression， maximum tumor diameter，calcification， and the presence of a coronal skip sign （P＜0.05）.Multivariate Cox regression analysis identified N3 stage（hazard ratio ［HR］=3.762， 95% confidence interval ［CI］： 1.147-12.337， P=0.029）， histological grade III（HR=3.558， 95%CI： 1.631-7.759， P=0.001）， calcification（HR=4.066， 95%CI： 1.888-8.757， P＜0.001）， and the coronal skip sign（HR=2.178， 95%CI： 1.064-4.460， P=0.033） as independent risk factors for recurrence-free survival in luminal B patients.Three predictive models were developed， with concordance indices （C-index） of 0.687 for the clinicopathological model， 0.734 for the ABUS model， and 0.791 for the combination model.ROC curve analysis demonstrated that the AUC values for the clinicopathological model， ABUS model， and combination model were 0.688， 0.707， and 0.779 in predicting 3-year recurrence risk， and 0.724， 0.745， and 0.819 in predicting 5-year recurrence risk， respectively.The DeLong test indicated that the AUC of the combination model at years was significantly higher than those of the clinicopathological model and the ABUS model （P＜0.05）.Internal validation revealed satisfactory stability for the combination model （C-index=0.788）.The calibration curve of the combination model closely matched the ideal curve.DCA showed that the combination model had higher clinical net benefit within a wide threshold probability range of 3.0% to 81.0%.Survival analysis demonstrated statistically significant differences in cumulative survival rates among patients with varying N stages， histological grades， Ki-67 expression levels， maximum tumor diameters， the presence or absence of calcification， and the presence or absence of a coronal skip sign （P＜0.05）.

Conclusion

The predictive model established by combining ABUS imaging features with clinicopathological characteristics demonstrates favorable performance， providing an efficient， accurate， and cost-effective approach for predicting postoperative recurrence risk in luminal B breast cancer patients.This model can assist clinicians in assessing recurrence risk and developing individualized followup strategies.

Key words: Automated breast ultrasound, Breast cancer, Luminal B type, Recurrence risk, Predictive model