Paget’s disease (PD) of the breast is a relatively rare clinical manifestation with a reported incidence of 1–3% of all primary breast cancers (BCs) [5–8]. PD occurs mainly in women, but can also occur in men much more rarely[19, 20]. Typical manifestations are pruritus, erythema, scaly skin, nipple hemorrhage, nipple erosion, or ulcers in the nipple and areola area , and occasionally there are cases of nipple loss. Delayed treatment often misdiagnosed as benign disease such as eczema affects prognosis [22–24]. There is no independent guideline for the treatment of PD, because it is often accompanied with diabetes, and the treatment plan is based on the associated Tumor formulation. The current research on the total sample size of PD complications replaces the clinical basis of the disease. There is not much data in this case. However, owing to the low incidence, there has been no patient-controlled study that conducted survival analysis of Paget's disease. Therefore, it is necessary to use Cox regression analysis and develop line charts to provide a comprehensive predictive model that includes not only systematic demographic information, but also surgical treatment and other clinical parameters to predict the risk of PD.
Although the AJCC staging system can help to predict the prognosis of PD patients , it ignores some important risk factors, such as age, ethnicity, and marital status. A nomogram is a convenient graphical representation of a mathematical model. It provides an intuitive way to combine important factors and predict a specific endpoint. Nomogram is also a reliable tool for quantifying risk and widely used in applied tumor prognosis. A well-developed clinical nomogram is a popular decision-making tool that can be used to predict the outcome of an individual and benefit both clinicians and patients . Prognostic nomograms are the visualization of complicated statistical model that were used to predicting individual survival outcomes, and numerous advantages were observed in prognostic nomograms including good accuracy, user-friendliness and comprehensibility, allowing for wide application in clinical practice [27–29]. In our study, we built a more comprehensive model based on a combination of various risk factors to better predict the prognosis of PD patients. Divided with the traditional AJCC staging system, based on the seven variables of age, race, AJCC stage, marital status, race, radiotherapy and surgery, and the traditional AJCC staging system ranking, it can better predict and assess the prognosis of PD patients.
Paget's disease is almost always associated with an underlying in situ or invasive cancer or both . In previous study, increasing age and being unmarried (divorced, separated, and never married) were found to be potential factors that increased the risk of Paget’s disease[31–33]. According to the previous study, for PD alone and patients with intraductal ductal carcinoma and breast cancer, breast conserving surgery combined with radiotherapy had a 5-year local recurrence rate of 5% , which hintingthat surgery combined with radiotherapy could effectively affect the prognosis of Paget's disease. Our analysis was consistent with these previous reports.
Based on the above-mentioned risk factors, and considering the limitations of the traditional AJCC staging system, it may not be possible to predict the overall survival of Paget's disease better. Therefore, we constructed a nomogram by combining seven independent prognostic factors to predict the overall survival of Paget's disease. Our model found that the AJCC stage and age had a significant effect on the total score prediction. The C-index and calibration curve were satisfactory when verified, indicating that the model is reproducible and reliable. In our nomogram, age > 51 years and AJCC stage > II grades scored higher, which indicated that patients with higher AJCC stage had worse prognosis as age increases after age 51. By comparing it with the traditional AJCC stage, we evaluated the value of this novel nomogram in predicting OS. Compared with the AJCC stage, our nomogram had better resolvability and accuracy in predicting 3-year and 5-year OS. In addition, using DCA, it had been fully demonstrated that the established nomogram could predict survival better than the AJCC staging system. Similarly, some studies had used DCA to validate benefits and models (clinical applications of predictive power [35, 36]). This was the first study to compare the newly established model with the traditional AJCC staging model and proved its better predictive ability for PD patients. We believed that our model would directly help clinicians to quantify the risk of cancer-specific deaths and to design more appropriate treatment strategies for PD patients.