肿瘤预防与治疗2023，Vol.36Issue(12)：1016-1025,10.DOI:10.3969/j.issn.1674-0904.2023.12.006
基于最优子集回归和临床病理/血液学参数的早期宫颈癌预后预测研究
Prognostic Prediction Study of Early Cervical Cancer Based on Best Sub-set Regression and Clinicopathological/Hematological Parameters
摘要
Abstract
Objective:To explore prognostic factors of ear-ly-stage cervical cancer and establish a predictive model,in order to provide clinical evidence for postoperative adjuvant therapy.Meth-ods:Clinicalpathological factors as well as preoperative hematological parameters were collected from 172 cervical cancer patients(IB1,IB2 and IIA1)who underwent radical hysterectomy.A prognostic model based on these data was established through best subset regres-sion,and its prediction efficacy was evaluated.Results:The median age of the entire cohort was 50 years old;the median disease-free survival(DFS)was 33.5 months,with 1-and 3-year DFS rates of 94.8%and 89.7%,respectively.We found that pathological stage,tumor size,depth of invasion,vaginal margin status,surgical approach(laparoscopy or laparotomy),and radiation therapy were closely associated with the prognosis of cervical cancer patients.Additionally,the proportion of the neutrophils,the proportion of monocytes,and hematocrit were also related to the prognosis of cervical cancer patients.The consistency index of the prognostic model based on these factors was 0.822(95%CI:0.781～0.863).Through 1-and 3-year calibration curve analysis and decision curve analysis,the model demonstrated higher predictive accuracy and significant net benefits.The average C-index obtained from internal validation using Boot-strap was 0.681(95%CI:0.637～0.729).Conclusion:The prognostic model for cervical cancer developed in this study based on clin-icalpathological factors and hematological parameters is capable of providing intuitive predictions for the 1-and 3-year DFS of early-stage cervical cancer patients.关键词
宫颈癌/预后/最优子集回归Key words
Cervical cancer/Prognosis/Best subset regression分类
医药卫生引用本文复制引用
李思敏,李柯臻,杨曼,车雨柔,王卫东..基于最优子集回归和临床病理/血液学参数的早期宫颈癌预后预测研究[J].肿瘤预防与治疗,2023,36(12):1016-1025,10.基金项目
This study was supported by grants from Science and Technology Department of Sichuan Province(No.2022NSFSC0051,No.2020YJ0446),Chengdu Science and Technology Bureau(No.2021YF0501659SN)and Sichuan Cancer Hospital(No.YB2022003). 四川省自然科学基金重点项目(编号:2022 NSFSC0051) （No.2022NSFSC0051,No.2020YJ0446）
四川省科技计划项目(编号:2020YJ0446) （编号:2020YJ0446）
成都科技创新研发项目(编号:2021YF0501695SN) （编号:2021YF0501695SN）
四川省肿瘤医院临床科学家项目(编号:YB2022003) （编号:YB2022003）
