【作者】 李晓峰；

【导师】 杨兴升；

【作者基本信息】 山东大学 ， 妇产科学（专业学位）， 2024， 硕士

【摘要】 背景:子宫颈癌目前是影响全球女性生存与健康的常见恶性肿瘤之一,当前的新发病例趋于年轻化。盆腔淋巴结(Pelvic lymph node,PLN)和腹主动脉旁淋巴结(Para-aortic lymph node,PALN)是常见的子宫颈癌淋巴结转移部位,淋巴结转移常提示宫颈癌患者的预后不佳,PALN转移则意味着更晚的分期和更差的预后。2018年的FIGO子宫颈癌新分期中,所有出现淋巴结转移情况的宫颈癌患者均被定义为ⅢC期,这强调了临床中宫颈癌患者出现淋巴结转移与较差预后的关联,反映了对于淋巴结转移患者的诊疗处理需求。但目前影像学诊断PALN转移的准确率有限,且并非所有患者都适合通过手术病理明确淋巴结转移情况。此外,ⅢC1期宫颈癌患者在选择是否行预防性腹主动脉旁淋巴结引流区延伸野外照射(Extended-field radiation therapy,EFRT)时,如何精准筛选出PALN转移高风险患者的标准尚无定论。针对上述问题,本研究设计了基于PALN转移预测模型的风险分层系统,能够实现对宫颈癌患者PALN转移的预测,并能够精准筛选出PALN转移高风险ⅢC1期宫颈癌患者。目的:本研究的目的是研究PALN转移的独立危险因素,并探究它们在PALN转移的预测价值,并进一步将PALN转移风险预测模型用于筛选PALN转移高风险ⅢC1期患者中,以此来讨论PALN转移风险预测模型的应用价值。研究方法:研究收集2011年1月至2021.年12月期间,于山东大学齐鲁医院就诊的宫颈癌初治患者,共计1115例。首先,使用逻辑回归分析,初步筛选出PALN转移相关的独立危险因素,建立子宫颈癌PALN转移风险预测模型,并进一步验证其预测效能;其次,使用本研究建立的宫颈癌PALN转移预测模型,对所有患者进行风险评分,根据模型受试者工作曲线(Receiver operating characteristic curve,ROC)的最佳截断值进行风险分层,通过比较ⅢC1期不同风险层级患者的预后差异,验证该模型在ⅢC1期患者中筛选PALN转移高风险群体的应用价值。结果:1.PALN转移风险预测模型的建立与验证本研究共纳入患者1115名,其中93名PALN转移患者为阳性组,1022名PALN未转移患者为阴性组。将所有患者按7:3随机拆分为训练集和验证集,其中训练集共784人,验证集共331人。在训练集中行单因素及多因素Logistic回归分析得出:绝经史、血清鳞状细胞癌抗原(Squamous cell carcinoma antigen,SCC-Ag)、血白细胞计数(White blood cell,WBC)、肿瘤最大直径、PLN转移数目、淋巴脉管间隙浸润(Lymphatic vascular space infiltration,LVSI)为PALN转移的独立危险因素。基于以上特征变量建立PALN转移预测模型,利用nomogram实现可视化。训练集和验证集的ROC曲线、校准曲线和临床决策曲线分析(Decision curve analysis,DCA)提示该模型具有良好的区分度、校准度和临床效能。2.预测模型在筛选PALN转移高风险ⅢC1期宫颈癌患者中的应用使用PALN转移风险预测模型对所有患者进行风险评分,根据模型ROC曲线最佳截断值0.259(灵敏度:0.882、特异度:0.818),将ⅢC1期患者中PALN转移风险>.0.259的患者定义为PALN转移高危组,转移风险≤0.259的患者定义为低危组,其中高危组48例,低危组192例。应用倾向性评分匹配(Propensity score matching,PSM)平衡组间差异,并进行1:1队列匹配,匹配后高危组患者的复发率、3年无病生存率、3年生存率、5年无病生存率和5年生存率均比低危组更差,而且Kaplan-Meier生存分析提示高危组有更短的无病生存期。以上结果表明,本模型在ⅢC1期患者中筛选出的.PLLN转移高危组人群具有更差的生存结局,证明了模型在ⅢC1期患者中筛选具有PALN转移高风险群体的应用价值。结论:1.基于绝经史、SCC-Ag、WBC、肿瘤最大直径、PLN转移数目和LVSI建立的PALN转移预测模型具有良好的区分度、校准度和临床效能,可用于宫颈癌患者的PALN转移预测。2.使用PALN转移风险预测模型筛选出的PALN转移高风险ⅢC1期患者具有更差的预后,说明本模型能较为精准地区分不同预后的ⅢC1期宫颈癌人群,能够在预防性EFRT目标人群的筛选中发挥应用价值。更多还原

【Abstract】 BackgroundCervical cancer is one of the malignancies that significantly impact women’s health and lifespan worldwide.Common sites of cervical lymph node metastasis include the pelvic lymph nodes(PLN)and para-aortic lymph nodes(PALN),which are crucial characteristics in cervical cancer patients with apoor prognosis.PALN metastasis is often linked to advanced stages and a worse prognosis.In the FIGO 2018 classification,patients with lymph node metastasis are categorized as stage ⅢC,emphasizing the influence of lymph node involvement on the prognosis of cervical cancer patients and highlighting the importance of diagnosing and treating patients with lymph node metastasis.However,the accuracy of imaging techniques in diagnosing PALN metastasis is limited,and not all patients are suitable for confirming lymph node metastasis through surgical pathology.Moreover,in cases where stage ⅢC1 cervical cancer patients are deciding whether to undergo preventive extended-field radiation therapy(EFRT),there is still uncertainty about how to effectively identify patients at risk of PALN metastasis.To address these challenges this study has developed a risk stratification system based on a PALN metastasis prediction model.This system aims to predict PALN metastasis in cervical cancer patients and accurately identify high-risk IIIC I cervical cancer patients with PALN involvement.ObjectivesThe objective of this research was to examine the independent risk factors for PALN metastasis,their predictive significance for PALN involvement,and to assess the utility of a PALN metastasis risk prediction model in identifying patients at high risk of stage IIIC1 PALN metastasis.MethodsThe study involved 1115 patients who underwent initial treatment for cervical cancer at Qilu Hospital of Shandong University from January 2011 to December 2021.Initially,independen risk factors for PALN metastasis were identified through univariate and multivariate logistic regression analyses.Subsequently,a predictive model for PALN metastasis risk was developed,and its predictive accuracy was validated The predictive model was then utilized to assign risk scores to stage ⅢC1 patients,and risk stratification was conducted based on the optimal cutoff value from the Receiver Operating Characteristic(ROC)curve of the model.Prognostic differences among patients with varying risk levels were assessed to evaluate the model’s utility in identifying high-risk groups for PALN metastasis in IIIC1 patients.Results1.Establishment and Verification of PALN Metastasis Risk Prediction ModelA total of 1115 patients participated in this study,with 93 patients having PALN metastasis classified in the positive group and 1022 patients without PALN metastasis in the negative group.All patients were randomly split into a training set and a validation set at a ratio of 7:3,comprising 784 individuals in the training set and 331 individuals in the validation set Univariate and multivariate logistic regression analyses were conducted on the training set revealing that menopausal history,serum Squamous Cell Carcinoma Antigen(SCC-Ag),White Blood Cell count(WBC),maximum tumor diameter,number of pelvic lymph node(PLN)metastases,and lymphovascular space invasion(LVSI)were independent risk factors for PALN metastasis.A,PALN metastasis prediction model was developed using the aforementioned variables and represented visually through a nomogram.Evaluation of the model using ROC curve analysis,calibration curve and Clinical Decision Curve Analysis(DCA)on both the training and validation sets demonstrated its favorable discriminative ability,calibration,and clinical utility.2.Application of predictive model in screening patients with Stage ⅢC1 cervical cancer at high risk of PALN metastasisThe PALN metastasis risk prediction model was utilized to assign risk scores to stage IIIC1 cervical cancer patients.Based on the optimal cut-off value of the model’s ROC curve,set at 0.259(sensitivity:0.793,specificity:0.918),patients with a PALN metastasis risk score>0.259 were classified as the high-risk PALN metastasis group while those with a risk score≤0.259 were categorized as the low-risk group.Among these,there were 48 cases in the high-risk group and 192 cases in the low-risk group.Propensity score matching(PSM)was employed to address disparities between the groups,revealing that,following matching:Patients in the high-risk group exhibited lower rates of recurrence,3-year disease-free survival,3-year survival,5-year disease-free survival,and 5-year survival.Kaplan-Meier survival analysis further indicated that the high-risk group had a shorter disease-free survival time.These findings demonstrate that the model identifies a subset of stage ⅢC1 patients at high risk of PALN metastasis with poorer survival outcomes,underscoring the model’s utility in screening for high-risk PALN metastasis groups in this patient population.Conclusions1.The PALN metastasis prediction model which incorporates menopausal history,SCC-Ag,WBC,maximum tumor diameter,number of PLN metastasis and LVSI demonstrates high accuracy,differentiation and clinical efficacy.2.The PALN metastasis risk prediction model effectively identifies stage IIIC1 patients at high risk of PALN metastasis,leading to a poorer prognosis.This highlights the model’s ability to accurately differentiate within the stage ⅢC1 cervical cancer population based on prognosis,illustrating its potential utility in screening for target populations suitable for Extended-Field Radiation Therapy(EFRT)prevention measures.更多还原