| Breast cancer is a leading reason for cancerous death in female both globally and domestically.Due to lower social and economical environment,there are still lots of improvement to be made in our country,from early detection of cancer to pharmaceutical treatment,to follow up of patients till the end of their lives.There are certain differences in breast tissue density,prevalence of breast cancer and tumor growth rates between Asian and Caucasian females.Invasive breast neoplasm could be subdivided into 4 types,which are luminal A,Luminal B,triple negative and HER2-enriched.The prognoses are largely different between each other,which could be attributed mostly to the new development of hormonal therapy and targeted therapy.Most breast cancer patients would under through one or more of the therapeutic choices such as surgery,chemotherapy,radiotherapy,hormonal therapy and targeted therapy.The main purpose of surgery is to remove tumor and conform tumor stage,and choose the ideal treatment plan thereafter.Surgical treatment for breast cancer involves breast-conserving surgery or mastectomy.Radical mastectomy includes removal of the entire breast,lymph nodes under the arm and underlying chest wall muscle,whereas a modified radical mastectomy does not involve chest wall muscle.Radiation is applied after surgery to damage cancer cells remaining in the breast and underarm area.Patients went through breast-conserving surgery typically followed by radiation treatment since this procedure has been proved to reduce the risk of recurrence and death rate largely.Radiation is also an option for advanced breast cancer patients.Systemic therapy is treatment has the potential to affect and treat all parts of the body,not just one area.For breast cancer,these cancer drugs are injected into vascular system or given orally.Systemic therapy includes chemotherapy,hormonal therapy,and targeted therapy.There is a global trend to study gene or targeted drug aim areas as prognostic factors for breast neoplasm patients.There are certain drawbacks in such methods:they are extravagant,very time consuming and require highly skilled professionals.The flaws are so big that they are only practical in theory even in developed countries.On the other hand,there are copious lab data produced in hospital daily activities,served their purpose merely as a couple of numbers,which no doubt is a great waste of valuable information.This research aim to fill that gap by forming a predict model using only inexpensive lab tests results.Objectives:The prognostic value of routine laboratory variables in breast cancer has been largely overlooked.Based on laboratory tests commonly performed in clinical practice,we aimed to develop a new model to predict disease free survival（DFS）after surgical removal of primary breast cancer.Use the data from our cancer center to verify the predictive model to see how it works in clinical practice.Methods:In a cohort of 1,596 breast cancer patients,we analyzed the associations of 33laboratory variables with patient DFS.Demographic variables including age,race/ethnicity,smoking status,and drinking status were collected in this study.Basic clinical variables included tumor size,stage,grade,histology,lymph nodes metastatic rate,ER status,PR status,and treatments（hormone therapy,chemotherapy,and radiation therapy）.Routine blood-based laboratory test data were also obtained from medical charts,which included a total of 33 variables in four categories:complete blood count（CBC）,comprehensive metabolic panel（CMP）,coagulation panel,and leukocyte differentiation tests.Comparisons of demographic,clinical,and laboratory variables between training and testing sets were performed using the chi-square test for categorical variables and Student’s t test for continuous variables.The association between each variable and patient DFS was assessed using Kaplan-Meier and Cox proportional hazards regression analyses in the training set.For the laboratory variables,we conducted stepwise selection using multivariate Cox proportional hazards model with significant laboratory variables identified in the univariate analysis.All continuous variables were kept continuous in the multivariate Cox regression and model construction process to avoid loss of power and residual confounding.Two methods were used for model validation and applied in both training and testing sets.Model’s capability to predict recurrence was assessed by constructing the ROC curves and calculating the AUCs70.In the second validation method,patients were classified into three risk groups based on the prognostic index calculated by the model.The cutoff value was determined by tertile distribution of the prognostic index.HRs with 95%CI in different risk groups were assessed by Cox proportional hazards model.Survival curves were plotted using Kaplan-Meier method and compared using the log-rank test.All statistical tests were two-sided,and a P-value of less than 0.05 was considered statistically significant.Results:1.Ten demographic and basic clinical variables（age,race,tumor stage,tumor size,lymph nodes metastatic rate,ER status,PR status,chemotherapy,radiation therapy,and hormone therapy）were significantly associated with DFS（Supplementary Table S2）.Among the remaining 21 laboratory variables,8 exhibited significant associations with DFS in a univariate basis（Table 2）,including HCT,HGB,RBC,and RDW from the CBC panel,albumin and ALP from the CMP panel,and INR and PT from the coagulation panel.2.Three variables（HGB,ALP,and INR）which were selected from≥6 imputed datasets were included in the final model.3.Based on 3 significant laboratory variables（hemoglobin,alkaline phosphatase,and international normalized ratio）,together with important demographic and clinical variables,we conducted a new predictive model.4.The AUCs were 0.79（95%CI:0.75-0.83）and 0.74（95%CI:0.69-0.79）in the training and testing set,respectively（Figure 2）.Compared with patients in the low-risk group,patients in the medium-and high-risk group had a significantly increased risk of recurrence with a hazard ratio（HR）of 1.75（95%confidence interval[CI]1.30-2.38）and 4.66（95%CI 3.54-6.14）,respectively in the training set.5.Data from our cancer center verified a relatively high predictivity of the model while applied in our patients.The Kaplan-Meier curves showed the model has a fine prognostic ability in definding people at high risk of recurrence.Conclusions:1.We conducted a new prognostic model incorporating readily available routine laboratory tests,together with demographic and basic clinical variables.2.The model demonstrated its predictive power.It is powerful in identifying breast cancer patients who are at high risk of recurrence.All results were verified by testing group to show its stability.3.Data from our cancer center verified that the predictive model is powerful in identifying breast cancer patients who are at high risk of recurrence. |
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