In this study, we established a nomogram model for the postoperative survival of patients with stage I NSCLC. The nomogram model incorporated three variables: CA125, age and tumor size. The nomogram model had good discrimination ability, the C-index of the training cohort was 0.759, and the C-index of the validation cohort was 0.707. The analysis of DCA and ROC curves also further showed that the nomogram model had better net clinical benefits. We also divided patients into low-risk groups and high-risk groups, which can better guide clinicians in the follow-up and treatment of stage I NSCLC patients after surgery.
In recent years, there have been a large number of nomogram studies on tumor prognosis, including breast cancer [10, 11], colorectal cancer , gastric cancer and so on. These studies showed that the nomogram model had a better predictive value for the prognosis of tumors than the traditional TNM staging system. There are many nomogram models for the prognosis of lung cancer. For example, a nomogram was established to predict brain metastasis in NSCLC patients after radical surgery . There are also many nomogram models for predicting disease-free survival after surgery for early NSCLC (pathological stage I and stage II) [14–16]. However, there are still few nomogram models for predicting the survival rate of stage I NSCLC patients that include clinical data, pathological data, and laboratory indicators.
In this study, 48 variables, including clinical and pathological data and laboratory indicators, were selected through LASSO regression. Cox regression analysis found that there were three independent risk factors for postoperative survival in stage I NSCLC patients: age, tumor size, CA125. Many research reports have shown that age is a risk factor for poor postoperative prognosis in lung cancer patients, whether in early or advanced lung cancer [17–19]. Tumor size was an major factor for the prognosis of NSCLC patients, especially stage I NSCLC patients. TNM staging system also fully considered the tumor size, but the TNM staging system only analyzed the tumor size according to 1 cm, 2 cm, and 3 cm sizes. We believe that the actual tumor size will have a great influence on OS of stage I NSCLC patients. Therefore, this paper analyzed tumor size as a continuous variable, which may have better predictive value for the prognosis of with stage I NSCLC patients, which was also consistent with the study of Cao et al. . Laboratory indicators are generally not included in other nomogram. But laboratory indicators are easy to obtain and easy to monitor, So the nomogram also includes laboratory indices such as serum tumor markers. CA125, as common tumor markers, have great value in predicting the prognosis of tumors. In some studies, it has also been shown that elevated CA125 is an independent risk factor for the survival of NSCLC patients [22, 23].
This study combined clinical, pathological and laboratory variables to establish a nomogram model to predict the postoperative survival of stage I NSCLC patients. The calibration curve showed that there was good agreement between the predicted survival rates of stage I NSCLC patients at 1, 3, and 5 years after surgery and actual observations. The C-index also showed the good predictive ability of the nomogram model (0.759for the training cohort and 0.707 for the validation cohort). Both DCA and the clinical impact curve indicated that the nomogram model had better overall net benefits than the TNM staging system. The 5-year survival rate of patients with stage I NSCLC was relatively high. Therefore, not all patients need additional treatment and frequent follow-up.
.In this study, the risk stratification of patients with stage Ⅰ NSCLC was based on the score of the nomogram model. Not all patients with stage Ⅰ NSCLC need adjuvant therapy .For stage Ⅰ NSCLC patients in the low-risk group, regular follow-up is enough, while for patients in the high-risk group, more careful follow-up、examination and adjuvant therapy may be required. This could also help clinicians make individualized treatment decisions.
This study also had some shortcomings, as follows: 1. This study was a single-center study, and there may be regional biases. 2. A relatively small sample size will limit statistical analysis. Although the nomogram model had been verified internally and externally to verify the accuracy of the model, data from different regions still needed to be further verified. 3. This study failed to include some important genetic factors, such as EGFR mutations.