A reliable and accurate staging method for predicting the long-term survival of cervical cancer patients is important in guiding postoperative treatment decisions and follow-up. The development of 2018 FIGO staging indicates that the cervical cancer staging system has changed from clinical to surgical and pathological staging. Previous studies revealed that clinicopathological factors such as Lvsi and myometrial depth of stromal invasion, in addition to tumour diameter, may affect prognosis. Nevertheless, they do not participate in tumour staging. This study sought to establish an individualized prediction model based on 2018 FIGO staging and clinicopathological factors.
Lvsi is a common clinicopathological occurrence in malignant tumours. Tumour cells entering and spreading through blood or lymphatic vessels are the basis of tumour metastasis[14]. Previous studies have demonstrated that the incidence of lymph node metastasis is greater in Lvsi-positive than Lvsi-negative patients [15]. Balaya reported that Lvsi was significantly associated with the 5-year DFS reduction of 2018 FIGO staging IB for cervical cancer in a 2018 FIGO staging prospective study on the impact of Lvsi on the prognosis of IB1 patients. Special consideration should be given to the status of Lvsi in early cervical cancer to carry out a more accurate risk assessment. It is proposed that Lvsi be included in the new FIGO staging [16]. This study also discovered that Lvsi was an independent risk factor for OS of staging IB cervical cancer, with the Lvsi-positive patients having twice the chance of death as adverse patients.
The depth of tumour invasion is classified into three groups (deep third, middle third and superficial third ) based on the sedlis criteria [13][17]. Because many early cases were pathologically divided into only two groups (deep 1 / 2 and superficial 1 / 2) in this study, and the imaging of patients undergoing CCRT can only be divided into two groups for the depth of muscle invasion (inner 1 / 2 and outer 1 / 2). Therefore, the classification of muscle invasion in this study is based on 1 / 2. Deep stromal invasion is discovered to be an independent risk factor affecting OS of stage IB cervical cancer.
Previous studies reported that adenocarcinoma had a worse prognosis than that squamous cell carcinoma. It was one of the indications for postoperative supplementary radiotherapy in the intermediate-risk group [18][19]. In this study, it was discovered that the pathological type did not affect OS. Because adenocarcinoma is a risk factor for postoperative supplementary treatment, the proportion of patients in this study with adenocarcinoma and adenosquamous carcinoma receiving postoperative supplementary treatment (24.0%) is greater than that of patients with squamous carcinoma (17.5%). The discrepancy in OS is eliminated by vigorous postoperative supplemental treatment. Surgery and CCRT are two standard treatment options for IB cervical cancer staging. The decision to supplement treatment after surgery is determined by tumour diameter, Lvsi and muscle invasion. This study found that the treatment plan is not a factor influencing OS, which could be due to the FIGO stage, Lvsi and deep structure invasion affecting the treatment decision. This results in the treatment becoming an intermediate variable affecting prognosis and eliminating the impact of treatment on OS. Furthermore, previous studies suggest that some factors, such as serum SCC, may affect the prognosis but are not included in the model. In this study, SCC was not included in the statistics because about 1/3 of the cases included in the study had missing SCC data. SCC is considered to be only associated with squamous cell carcinoma, and the level of SCC is closely related to lymph node metastasis [21]. Since lymph node metastasis was classified as the IIIC stage in the FIGO stage 2018, it was acceptable that this indicator was not included in this study.
The study comprised 1185 patients who were re-staged using the 2018 FIGO staging system. We evaluated the factors that could affect the OS of stage IB cervical cancer. We identified seven predictors with statistical significance from the univariate analysis (FIGO stage, age, Lvsi, deep stromal invasion, paraterine invasion and treatment). The Cox regression analysis identified four independent risk factors that influenced the prognosis (FIGO stage, age, Lvsi, deep stromal invasion). Using the risk factors, a nomogram model was established to predict the 2-,3- and 5-year OS of 2018 FIGO staging IB cervical cancer. The C-index and ROC curves demonstrated that the training and validation sets had better prediction accuracy compared with FIGO staging. The calibration curve indicated that the model has acceptable consistency. The DCA curve demonstrates that the model has a significant clinical net benefit compared with FIGO staging. The log-rank test can separate three risk categories based on the nomo-score, with significant differences (P < 0.001). Compared with the three subgroups (IB1, IB2 and IB3) of FIGO staging, they have an apparent risk-stratifying ability, that can help us better identify high-risk groups. Moreover, from the perspective of clinical application, the more model variables, the stronger the prediction ability, although the simplicity is reduced. The nomogram model developed in this study comprises only four variables, which improves the accuracy and simplicity of the model, as well as, its clinical application value.
This study has some limitations. First, since this is a retrospective study, some factors are prone to data or record bias, causing inevitable bias. Secondly, the case data are all from the same hospital, which may result in inaccurate results because the treatment methods are too consistent with the external environment. Additionally, our study did not have external validation of nomograms, and more hospitals or databases should be added for external validation.