Linying Liu

Development and Validation of Prediction Models for the Prognosis of Clear Cell Adenocarcinoma of the Cervix: A Population‐Based Cohort Study

ABSTRACT Aims Clear cell adenocarcinoma of the cervix (CCAC) is a rare and aggressive malignancy with poor prognosis. This study aimed to develop and validate nomograms and risk‐stratification scores for predicting overall survival (OS) and cancer‐specific survival (CSS) in CCAC patients. Methods Data from 429 CCAC patients were extracted from the Surveillance, Epidemiology, and End Results (SEER) database (2000–2019). Patients were randomly assigned to training and validation sets. Cox regression analysis identified five independent prognostic factors for OS and CSS, which were used to construct nomograms for predicting 1‐, 3‐, and 5‐year OS and CSS. The models were evaluated using receiver operating characteristic (AUC) analysis, calibration curves, and decision curve analysis (DCA). The clinical utility of the nomograms was compared with the 2018 FIGO Stage System using C‐index, NRI, and IDI. Patients were stratified into low‐ and high‐risk groups based on predicted risk scores, and Kaplan–Meier survival analysis was performed. Results Multivariate Cox regression identified age at diagnosis, tumor size, and surgery as independent prognostic factors for both OS and CSS, while chemotherapy and radiotherapy were specifically associated with OS and CSS. The C‐index for OS and CSS in the training set was 0.83 and 0.84, respectively. The AUC for 1‐, 3‐, and 5‐year OS and CSS in the training set was 0.95, 0.95, and 0.88, respectively, with similar results in the validation set. Calibration curves showed good agreement between predicted and actual outcomes. DCA, NRI, and IDI analyses indicated that the nomogram outperformed the 2018 FIGO Stage System. Survival analysis revealed that high‐risk patients had worse prognosis compared to low‐risk patients. Conclusion This study developed and validated nomograms for predicting OS and CSS in CCAC patients using SEER data. These models offer a more accurate prognostic tool, enhancing clinical decision‐making and enabling individualized treatment planning.

Causes of death analysis and the prognostic model construction in neuroendocrine carcinoma of the cervix: A SEER‐based study

AbstractPurposeNeuroendocrine carcinoma of the cervix (NECC) is rare but results in poor prognosis. The causes of death (CODs) in NECC patients are rarely reported. Our study aimed to explore the distributions of death causes of NECC patients compared with squamous cell carcinoma (SCC) and adenocarcinoma (ADC) and to develop a validated survival prediction model.MethodsPatients diagnosed with NECC, SCC, or ADC were identified from the Surveillance, Epidemiology, and End Results Program database from 1975 to 2019. We analyzed the standardized mortality ratio (SMR) to determine each cause of death for each survival time category. The Kaplan–Meier method was used for survival analysis. Univariate and multivariate Cox regression analyses were used to establish a nomogram model.ResultsA total of 358 NECC patients were included in this study, and 270 (75.4%) died during the follow‐up period. Patients with NECC had 5.55 times (95% CI, 4.53–6.79, p < 0.0001) higher risk of death compared with patients with SCC and 10.38 times (95% CI, 8.28–13.01, p < 0.0001) higher compared with ADC. Cervical cancer is the main cause of death in NECC. As the diagnosis time increased, the risk of death from all causes and cervix cancer gradually decreased. While after at least 10 years of follow‐up time, the highest and most dramatical SMR values were observed for metastasis (SMR, 138.81; 95% CI, 37.82–355.40; p < 0.05) and other cancers as the reason for death has an over 7‐fold higher SMR (SMR: 7.07; 95% CI: 2.60–15.40, p < 0.05) more than 5 years after the cancer diagnosis. Race, FIGO stage, and surgery were independent risk factors for the overall survival (OS) of NECC patients. For the predictive nomogram, the C‐index was 0.711 (95% CI: 0.697–0.725) and was corrected to 0.709 (95% CI: 0.680, 0.737) by bootstrap 1000 resampling validation.ConclusionCompared with SCC and ADC, NECC patients have an elevated risk of mortality due to cervical cancer and metastasis. We successfully constructed a prognostic nomogram for patients with NECC. Based on refractoriness and high mortality of NECC, targeted treatment strategies and follow‐up plans should be further developed according to the risk of death and distribution characteristics of CODs.

A novel nomogram based on inflammation biomarkers for predicting radiation cystitis in patients with local advanced cervical cancer

AbstractBackgroundsPlatelet‐to‐albumin ratio (PAR) is a new systemic inflammatory prognostic indicator associated with many inflammatory diseases. However, its role in radiation cystitis (RC) is obscure. This study aimed to explore whether PAR could be used as an effective parameter for predicting the RC risk in local advanced cervical cancer (CC) treated with radiotherapy.MethodsA total of 319 local advanced CC patients who received radical radiotherapy at Fujian Cancer Hospital were enrolled between December 2018 and January 2021. Demographics and clinical parameters were retrospectively analyzed. Univariate and multivariate analyses were used to identify the risk factors for RC. Backward and stepwise regression was applied to construct two monograms‐one with primary significant factors and the other with extra inflammatory biomarkers. A DeLong test was applied to compare the prediction abilities of two nomograms. Calibration curves and decision curve analysis (DCA) evaluated its prediction consistency, discrimination ability, and clinical net benefit.ResultsUnivariate analysis showed that age, tumor size, stage, total radiation dose, pelvic radiation dose, Systemic Immune‐Inflammation Index (SII), platelet‐to‐lymphocyte ratio (PLR), and PAR were significantly associated with RC occurrence (all p < 0.05). Multivariate analyses indicated that age, tumor size, stage, total radiation dose, and PAR were independent factors (all p < 0.05). Then, the area under curve (AUC) value of the nomogramSII+PAR was higher (AUC = 0.774) compared to that of the baseline nomogram (AUC = 0.726) (pDelong = 0.02). Also, the five‐cross validation confirmed the stability of the nomogramSII+PAR. Moreover, the calibration curve and DCA exhibited the nomograms' good prediction consistency and clinical practicability.ConclusionsPAR and SII could be valued for CC patients who are treated with radiation therapy. The nomogram based on PAR and SII could stratify patients who need extra intervention and nursing care to prevent bladder radiation damage and improve patients' quality of life.

A novel nomogram and risk stratification for early metastasis in cervical cancer after radical radiotherapy

AbstractObjectThis study aimed to establish an effective risk nomogram to predict the early distant metastasis (EDM) probability of cervical cancer (CC) patients treated with radical radiotherapy to aid individualized clinical decision‐making.MethodsA total of 489 patients with biopsy‐confirmed CC between December 2018 and January 2021 were enrolled. Logistic regression with the stepwise backward method was used to identify independent risk factors. The nomogram efficacy was evaluated by using the area under the receiver operating characteristic curve (AUC), C‐index by 1000 bootstrap replications, etc. Finally, patients were divided into high‐ and low‐risk groups of EDM based on the cut‐off value of nomogram points.Results36 (7.36%) CC patients had EDM, and 20 (55.6%) EDM had more than one metastatic site involved. Age below 51 (OR = 2.298, p < 0.001), tumor size larger than 4.5 cm (OR = 3.817, p < 0.001) and radiotherapy (OR = 3.319, p < 0.001) were independent risk factors of EDM. For the nomogram model, C‐index was 0.701 (95% CI = 0.604–0.798), and 0.675 (95% CI = 0.578–0.760) after 1000 bootstrap resampling validations. The Hosmer–Lemeshow test demonstrated no overfitting (p = 0.924). According to the Kaplan–Meier curve of risk score, patients with high risk were more prone to get EDM (p < 0.001).ConclusionThis is the first research to focus on EDM in CC patients. We have developed a robust scoring system to predict the risk of EDM in CC patients to screen out appropriate cases for consolidation therapy and more intensive follow‐up.