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Construction and validation of prediction model for cervical cancer recurrence based on systemic inflammation response index and clinicopathological parameters
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Tinghong GUAN1, Chunxia GONG2, Yuan TU1, Chenfan TIAN1, Jiaxin YU1, Peng JIANG1
Journal of Army Medical University | 2025, 47(16) : 1950 - 1961
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Journal of Army Medical University | 2025, 47(16): 1950-1961
• Clinical Medicine •
Construction and validation of prediction model for cervical cancer recurrence based on systemic inflammation response index and clinicopathological parameters
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Tinghong GUAN1, Chunxia GONG2, Yuan TU1, Chenfan TIAN1, Jiaxin YU1, Peng JIANG1
Affiliations
  • 1Department of Gynecology and Obstetrics,the First Affiliated Hospital of Chongqing Medical University,Chongqing
  • 2Department of Gynecology and Obstetrics,Women and Children’s Hospital Affiliated to Chongqing Medical University,Chongqing,China
Published: 2025-08-30 doi: 10.16016/j.2097-0927.202504075
Outline
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Objective‍

To investigate the predictive value of preoperative systemic inflammatory response index (SIRI) combined with clinicopathological parameters for postoperative recurrence in cervical cancer and to construct a prognostic model in order to optimize recurrence risk assessment.

Methods‍

Patients with cervical cancer who underwent standard surgical treatment at the First Affiliated Hospital of Chongqing Medical University (training cohort, n=996) and Chongqing Maternal and Child Health Hospital (validation cohort, n=496) between January 2017 and January 2022 were retrospectively enrolled based on our strict inclusion and exclusion criteria. Univariate and multivariate Cox regression analyses were performed to identify independent prognostic factors for recurrence-free survival (RFS), and then a nomogram was constructed. Receiver operating characteristic (ROC) curve was plotted to assess the predictive performance of the model, and the area under the curve (AUC) and calibration curve were employed to evaluate the model. Kaplan-Meier survival analysis was performed to determine the clinical application.

Results‍

Cox regression analysis demonstrated that International Federation of Gynecology and Obstetrics (FIGO) stage (P<0.001), tumor size (P<0.001), pathological type (P<0.001), tumor grade (P=0.007), parametrial invasion (P<0.001), depth of myometrial invasion (P=0.019), lymphovascular space invasion (P=0.019), vaginal margin involvement (P=0.010), adjuvant therapy (P=0.012), and SIRI (P<0.001) were independent prognostic factors for RFS. Our nomogram model based on above prognostic factors exhibited superior predictive performance for 1-, 3-, and 5-year RFS, with a significantly higher AUC value (0.886) than those of single-parameter models.

Conclusion‍

Our nomogram model demonstrated good accuracy in predicting RFS in cervical cancer patients, providing a potential tool for personalized clinical decision-making in recurrence risk management.

cervical cancer  /  systemic inflammatory response index  /  prognosis  /  prediction model
管停宏, 龚春霞, 涂袁, 田晨帆, 余佳欣, 蒋鹏. 全身炎症反应指数联合临床病理参数预测宫颈癌复发的模型建立与验证. 陆军军医大学学报, 2025 , 47 (16) : 1950 -1961 . DOI: 10.16016/j.2097-0927.202504075
Tinghong GUAN, Chunxia GONG, Yuan TU, Chenfan TIAN, Jiaxin YU, Peng JIANG. Construction and validation of prediction model for cervical cancer recurrence based on systemic inflammation response index and clinicopathological parameters[J]. Journal of Army Medical University, 2025 , 47 (16) : 1950 -1961 . DOI: 10.16016/j.2097-0927.202504075
Year 2025 volume 47 Issue 16
18
6
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Article Info
doi: 10.16016/j.2097-0927.202504075
  • Receive Date:2025-04-18
  • Online Date:2026-10-28
  • Published:2025-08-30
Article Data
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History
  • Received:2025-04-18
  • Revised:2025-07-18
Affiliations
    1Department of Gynecology and Obstetrics,the First Affiliated Hospital of Chongqing Medical University,Chongqing
    2Department of Gynecology and Obstetrics,Women and Children’s Hospital Affiliated to Chongqing Medical University,Chongqing,China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
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属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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