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Updated: Jun 15, 2025
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
A Diagnostic Nomogram Incorporating Prognostic Nutritional Index for Predicting Vaginal Invasion in Stage IB - IIA
Ning Xie1, Jie Lin1, Haijuan Yu1
1Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian Province, China.
Introduction:
With the advancements in cancer prevention and diagnosis, the proportion of newly diagnosed early-stage cervical cancers has increased. Adjuvant therapies based on high-risk postoperative histopathological factors significantly increase the morbidity of treatment complications and seriously affect patients' quality of life.
Objectives:
Our study aimed to establish a diagnostic nomogram for vaginal invasion (VI) among early-stage cervical cancer (CC) that can be used to reduce the occurrence of positive or close vaginal surgical margins.
Methods:
We assembled the medical data of early-stage CC patients between January 2013 and December 2021 from the Fujian Cancer Hospital. Data on demographics, laboratory tests, MRI features, physical examination (PE), and pathological outcomes were collected. Univariate and multivariate logistic regression analyses were employed to estimate the diagnostic variables for VI in the training set. Finally, the statistically significant factors were used to construct an integrated nomogram.
Results:
In this retrospective study, 540 CC patients were randomly divided into training and validation cohorts according to a 7:3 ratio. Multivariate logistic analyses showed that age [odds ratio (OR) = 2.41, 95% confidence interval (CI), 1.29-4.50, P = 0.006], prognostic nutritional index (OR = 0.18, 95% CI, 0.04-0.77, P = 0.021), histological type (OR = 0.28, 95% CI, 0.08-0.94, P = 0.039), and VI based on PE (OR = 3.12, 95% CI, 1.52-6.45, P = 0.002) were independent diagnostic factors of VI. The diagnostic nomogram had a robust ability to predict VI in the training [area under the receiver operating characteristic curve (AUC) = 0.76, 95% CI: 0.70-0.82] and validation (AUC = 0.70, 95% CI: 0.58-0.83) cohorts, and the calibration curves, decision curve analysis, and confusion matrix showed good prediction power.
Conclusion:
Our diagnostic nomograms could help gynaecologists quantify individual preoperative VI risk, thereby optimizing treatment options, and minimizing the incidence of multimodality treatment-related complications and the economic burden.
