| 【Background and objective】Breast cancer is one of the major public health problems endangering women’s health worldwide.In recent decades,the prognosis of breast cancer has greatly improved,based on the stratification of breast cancer risk and the development of corresponding precise personalized treatment plans.Different treatment plans are formulated for patients with different risk levels.Many factors are independent factors affecting breast cancer survival,including patient age,tumor size,lymph node metastasis status,ER,PR,and HER2 expression.Currently,the patient’s prognosis risk is evaluated using the TNM staging system in clinical practice,and corresponding treatment plans are formulated accordingly.However,we believe that the TNM staging system alone has certain shortcomings in assessing the overall tumor burden of breast cancer,especially for patients with satellite lesions or multiple lesions of the primary tumor,or with more lymph node metastasis.Studies have shown that evaluating tumor burden using primary tumor volume is better than using tumor diameter to classify T stage in traditional TNM staging.Tumor volume can be used as a better prognostic predictor of tumor burden,and corresponding prognostic prediction models have been proposed and validated for their effectiveness.Since the N stage in TNM staging is based on the number of lymph node metastases,regardless of the size of the metastatic lymph nodes or the actual size of the lymph node metastatic foci,the prognosis is poorer even if the number of metastatic lymph nodes is small in patients with lymph node fusion or larger lymph node metastasis.Therefore,based on the N stage,we explored and validated the inclusion of the actual size of lymph node metastatic foci in the prognostic prediction model.Improving or establishing a new breast cancer prognostic model based on this has important clinical significance for guiding clinical treatment and predicting patient survival.The purpose of this study is to evaluate the predictive value of primary tumor volume(TV)combined with the actual metastatic size of lymph node(NA)on the prognosis of breast cancer.The study is based on a population of patients with axillary lymph node metastasis who have not undergone neoadjuvant therapy.The study establishes a survival model to predict the prognosis of breast cancer patients with lymph node metastasis who have not undergone neoadjuvant therapy by calculating the primary tumor volume and the actual metastatic size of lymph node under the microscope after surgical resection of the breast cancer.The model is validated in this population.The aim is to better predict the disease-free survival period of breast cancer patients with lymph node metastasis who have not undergone neoadjuvant therapy,providing more comprehensive evidencebased support for patient management and treatment decision-making for clinical physicians.【Methods】This retrospective study collected data from breast cancer patients with lymph node metastasis who were admitted for inpatient treatment at the Second Affiliated Hospital of Guangzhou Medical University and the First People’s Hospital of Zhaoqing from July 2014 to November 2021,and who did not receive neoadjuvant therapy.The data includes age,pathological results,histological type,HR status,Ki-67,tumor size,lymph node status,TNM staging,recurrence and metastasis(local recurrence & distant metastasis)time,death time,etc.The main variables are TV and NA,corresponding to the calculation formulas: volume = π/6 × length × width × height,area = length ×width.The cut-off value of TV was determined to be 7.200cm~3 using the ROC curve,and patients with TV higher than 7.200cm~3 were classified as the high TV group,while patients with TV lower than 7.200cm~3 were classified as the low TV group.The cut-off value of NA was determined to be0.985 cm~2,and patients with NA higher than 0.985 cm~2 were classified as the high NA group,while patients with NA lower than 0.985 cm~2 were classified as the low NA group.Kaplan-Meier survival analysis was performed using tumor primary volume and NA,and compared with traditional T and N staging.Single-factor COX analysis was performed on TV,NA,T staging,N staging,clinical staging,age,histological type,Ki-67,HR status and other factors.Relevant risk factors were selected for multi-factor COX analysis,and independent prognostic risk factors were finally determined.A Nomogram prediction model was established based on these independent prognostic risk factors,and discrimination and internal validation were performed using C-index,ROC curve,calibration curve and DCA curve.Finally,the efficacy of the Nomogram prediction model was compared with that of the traditional TNM staging model using the ROC curve area.【Results】A total of 352 patients(January 2014 to November 2021)were included in this study,with a mean age of 55 years(range 27-75 years)and a mean survival time of 50.1 ± 26.4 months.During a median follow-up of 43.1 months(range 5.9-109.7 months),there were 28 cases of recurrence and metastasis events,with 22 cases(16.9%)in the high TV group,26 cases(16%)in the high NA group,6 cases(2.7%)in the low TV group,and 2 cases(1.1%)in the low NA group experiencing RFS events.Among patients with recurrence and metastasis,there were 2 cases of local recurrence(7.1%),14 cases of bone metastasis(50%),7 cases of lung metastasis(25%),6 cases of liver metastasis(21.4%),and 5 cases of brain metastasis(17.9%).During follow-up,there were 18 deaths,with 12 cases(9.2%)in the high TV group,14 cases(8.6%)in the high NA group,6 cases(2.7%)in the low TV group,and4 cases(2.1%)in the low NA group.The multivariate COX regression model for RFS showed that age ≥ 50 years(P = 0.018),high TV(P = 0.001),high NA(P = 0.003),and triple-negative breast cancer(P = 0.042)were independent prognostic risk factors for RFS.Kaplan-Meier survival curves were plotted with RFS as the primary outcome and OS as the secondary outcome,showing statistical differences between the high and low TV groups(RFS: P < 0.0001;OS: P = 0.0166)and between the high and low NA groups(RFS: P < 0.0001;OS: P = 0.0072).Compared with the low-risk group(low TV + low NA),patients with high risk(high TV + high NA)had poorer RFS(P < 0.0001)and relatively poorer OS(P = 0.0048).A Nomogram prediction model was established based on age,histological type,TV,and NA,and after internal validation using ROC curves,calibration curves,and DCA curves,the Nomogram prediction model showed excellent efficacy in predicting RFS in patients with lymph node metastasis who did not receive neoadjuvant therapy.The AUC values of the Nomogram prediction model’s ROC curves at 1,3,and 5 years were 0.944,0.865,and 0.857,respectively,and its predictive value was higher than that of the traditional TNM staging(AUC=0.656).【Conclusions】1.In breast cancer patients with axillary lymph node metastasis who did not receive neoadjuvant therapy,high total tumor volume and high total axillary lymph node metastasis were associated with poor RFS and OS,and in RFS,total tumor primary volume,total axillary lymph node metastasis and age were independent prognostic risk factors.2.Four predictive factors including age,molecular typing,TV and NA were screened out to establish a Nomogram prediction model for postoperative recurrence in patients with axillary lymph node metastasis,and its reliability was evaluated by internal verification.To provide auxiliary and objective basis for the choice of treatment and prognosis of breast cancer patients with axillary lymph node metastasis;In patients with axillary lymph node metastasis who have not received neoadjuvant therapy,our Nomogram model has better efficacy than the traditional TNM staging model,and has certain clinical value. |
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