Establishment of a Novel Risk Stratification System Integrating Clinical and Pathological Parameters for Prognostication and Clinical Decision‐Making in Early‐Stage Cervical Cancer

Autor: Haiying Wu, Lin Huang, Xiangtong Chen, Yi OuYang, JunYun Li, Kai Chen, Xiaodan Huang, Foping Chen, XinPing Cao
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Cancer Medicine, Vol 13, Iss 22, Pp n/a-n/a (2024)
Druh dokumentu: article
ISSN: 2045-7634
DOI: 10.1002/cam4.70394
Popis: ABSTRACT Background Highly heterogeneity and inconsistency in terms of prognosis are widely identified for early‐stage cervical cancer (esCC). Herein, we aim to investigate for an intuitional risk stratification model for better prognostication and decision‐making in combination with clinical and pathological variables. Methods We enrolled 2071 CC patients with preoperative biopsy‐confirmed and clinically diagnosed with FIGO stage IA‐IIA who received radical hysterectomy from 2013 to 2018. Patients were randomly assigned to the training set (n = 1450) and internal validation set (n = 621), in a ratio of 7:3. We used recursive partitioning analysis (RPA) to develop a risk stratification model and assessed the ability of discrimination and calibration of the RPA‐derived model. The performances of the model were compared with the conventional FIGO 2018 and 9th edition T or N stage classifications. Results RPA divided patients into four risk groups with distinct survival: 5‐year OS for RPA I to IV were 98%, 95%, 85.5%, and 64.2%, respectively, in training cohort; and 99.5%, 93.2%, 85%, and 68.3% in internal validation cohort (log‐rank p
Databáze: Directory of Open Access Journals
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