Research discovers that pelvic lymph node metastasis is a high-risk factor for cervical cancer patients, and has got consistent approval by lots of studies[13, 14]. Until 2018, FIGO agreed that lymph node metastasis had the greatest effect on prognosis, except for spreading to adjacent pelvic organs or distant organs. However, study by Xiaoliang Liu found that even in stage IIIC1, the survival rate also is heterogeneous, and tumor size, extension range, etc. have significant effect on prognosis of stage IIIC1. Therefore, we included 10 variables from the SEER database to analyze the factors which affect the prognosis of stage IIIC1. Furthermore, previous studies demonstrated that these 10 variables were significantly associated with the prognosis of cervical cancer. For this reason, univariate and multivariate Cox proportional hazard regression were performed for all these 10 variables[15-17].
Then, we established OS and CSS nomograms based on the result of multivariate Cox proportional hazard regression. The factors in OS nomogram include age, race, tumor size, differentiation, histology, extension range, surgery, radiotherapy. For nomogram of CSS, only age is excluded. In previous studies on prognostic analysis of cervical cancer, elderly patients always have the shorter overall survival due to weak immune systems, and aging organ is related to poor prognosis[18, 19]. But it was not an independent prognosis factor for CSS in stage IIIC1 according to our analysis. As we can see in Table S1, the possibility of death from cervical cancer was obviously lower in elderly patients. Therefore, the effect of age on CSS needs more detailed study.
Our nomograms are well innovated and practical. Firstly, although nomograms for cervical cancer have been widely used[17, 20], there is still no one for stage IIIC1 in cervical cancer. Secondly, contrast to FIGO stage, patient demographics (age, race), tumor characteristics (tumor size, histology, differentiation, extension range) and treatment (surgery, radiotherapy) which were independent prognosis factors for OS or CSS were included in our nomograms. Further, these variables were easily obtained in clinical. So, our nomograms could reduce the bias caused by patient demographics and different treatment when predicting prognosis of cervical cancer. Thirdly, our nomograms were verified by external data sets. This process can test the predictive ability of nomogram in different groups of people, and judge its applicability to various groups of people.
The C-indexes of nomograms trended to be between 0.65 and 0.75, which were acceptable[22, 23]. And the C-indexes of random sampling of SEER and external data sets all reached this area, indicating that our nomograms have favourable discrimination ability. Besides, whether the nomograms are incorrectly estimated or over-fitting can be measured according to calibration plots. When the plot completely meets the 45-degree line, the prediction model is considered to have a fine calibration. And our calibration plots fit well with the 45-degree line. It means our nomograms have good calibration in 3- and 5-year OS and CSS prediction. In addition, DCA was used to evaluate the clinical applicability of the constructed nomogram when quantifying the net improvement benefits under different threshold probabilities. After validation, DCA confirmed that our nomograms have a better clinical benefit and utility in predicting the survive of cervical cancer in stage IIIC1.
It is worth noting that the tumor size shared the largest contribution to nomograms, whether OS or CSS. The influence of tumor size on prognosis has been confirmed in various cancers, including thyroid cancer, breast cancer[26, 27]. In cervical cancer, the influence of tumor size on prognosis in stage IB and stage Ⅱ has been confirmed and shown in FIGO stage[28, 29]. According to multivariate Cox proportional hazard regression, as the tumor size increases, the prognosis of patients with stage IIIC1 becomes significantly worse. Furthermore, the HR was worse than any other factors included in the nomograms. Meanwhile, imaging can be evidence of FIGO stage. Studies on the application of imaging to assess the tumor size of cervical cancer before surgery show that the diagnostic power of imaging is obviously stronger than clinical assessment, especially MRI, depending on its superior contrast resolution, which can visualize tumor volume and size. We can conclude that compared to other pathological characteristics, the effect of tumor size on prognosis in cervical cancer holds unity across all stages. Further research revealed the value of tumor size as prognostic indicator. Therefore, we suggest that IIIC1 can be further divided into three sub stages according to tumor size.
Even though the nomograms were verified by external data set, our study still has some limitations. Firstly, as a retrospective study, this research filtered data from data sets and excluded patients with missing data on the collected variables, leading to a selection bias. Secondly, some key indicators are in lack, especially dosage of radiotherapy and details of chemotherapy project, etc. For example, only “Yes” and “No” showed in SEER database about chemotherapy, leading to the weaken impact of chemotherapy on survival. Thirdly, insufficient sample size of external data set and missing part of data caused inadequate verification.