目的: 旨在探讨卵巢上皮性癌发生的危险因素,构建基于临床及超声特征的列线图以提高卵巢肿瘤的术前评估价值。方法: 回顾性分析2022年1月到2024年9月于山西医科大学附属运城市中心医院妇科就诊的482名卵巢肿瘤患者,根据病理诊断结果将其分为良性肿瘤组（n=350）和卵巢上皮性癌组（n=132）。患者以6∶4的比例随机分为训练集（n=289）和验证集（n=193）,收集患者一般临床资料及超声特征参数。采用单因素和多因素回归分析影响卵巢上皮性癌发生的危险因素;使用R软件构建列线图预测卵巢上皮性癌的发生,并通过验证集进行内部验证,模型效能评估结果以ROC曲线、校准曲线以及DCA曲线进行表征。结果: 良性肿瘤组与卵巢上皮性癌组中年龄、是否绝经、妊娠次数、高血压、糖尿病、血清生化指标,多房肿瘤、是否存在实心成分、病灶位置、肿块最大直径以及子宫内膜厚度差异均具有统计学意义（P<0.05）;多因素Logistic回归分析显示年龄、肿块最大直径、病灶位置、是否存在实心成分以及血清指标CA153是引发卵巢上皮性癌的独立危险因素;基于上述指标构建的列线图模型在训练集中的AUC=0.89（95%CI：0.85~0.93）,在验证集的ACU=0.83（95%CI：0.76~0.89）,校准曲线与理想曲线拟合度良好,DCA显示模型净获益区间所对应的阈值概率为0.05~0.95。结论: 基于临床及超声特征构建的卵巢上皮性癌发生的列线图模型具有较高的预测性能和较好的临床应用价值,可为术前卵巢上皮性癌识别提供参考。
Abstract
Objective To explore the risk factors of ovarian epithelial carcinoma, and to construct a nomogram based on clinical and ultrasonic characteristics to improve the preoperative evaluation value of ovarian tumors. Methods A retrospective analysis was performed on 482 patients with ovarian tumor who were treated in the Gynecology Department of Yuncheng Central Hospital Affiliated to Shanxi Medical University from January 2022 to September 2024, and they were divided into benign tumor group (n=350) and ovarian epithelial carcinoma group (n=132) according to pathological diagnosis. Patients were randomly divided into a training set (n=289) and a validation set (n=193) at a ratio of 6∶4. General clinical data and ultrasonic characteristic parameters of patients were collected. The risk factors of ovarian epithelial carcinoma were analyzed by univariate and multifactorial regression. R software was used to construct a nomogram to predict the occurrence of ovarian epithelial carcinoma, which was internally verified by validation sets. The results of model efficacy evaluation were characterized by ROC curve, calibration curve and DCA curve. Results There were statistically significant differences between the benign tumor group and the ovarian epithelial carcinoma group in age, menopause, pregnancy frequency, hypertension, diabetes, serum biochemical indexes, multilocular tumor, solid component, lesion location, maximum mass diameter and endometrial thickness (P<0.05). Multivariate Logistic regression analysis showed that age, maximum mass diameter, lesion location, presence or absence of solid components and serum marker CA153 were independent risk factors for ovarian epithelial carcinoma. The AUC of the nomogram model constructed based on the above indexes in the training set was 0.89(95%CI: 0.85-0.93), and the ACU in the validation set was 0.83(95%CI: 0.76-0.89), the calibration curve fits the ideal curve well, and the threshold probability corresponding to the net benefit interval of the DCA model is 0.05-0.95. Conclusion The nomogram model of ovarian epithelial carcinoma based on clinical and ultrasonic features has high predictive performance and good clinical application value, and can provide reference for preoperative identification of ovarian epithelial carcinoma.
关键词
卵巢肿瘤 /
卵巢上皮性癌 /
列线图 /
预测模型
Key words
tumor ovarii /
ovarian epithelial carcinoma /
nomogram /
prediction model
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] SIEGEL R L, MILLER K D, JEMAL A.Cancer statistics, 2019[J]. CA Cancer J Clin.2019, 69(1): 7-34.
[2] LHEUREUX S, GOURLEY C, VERGOTE I, et al.Epithelial ovarian cancer[J]. Lancet, 2019, 393(10177): 1240-1253.
[3] FU Z, TAYLOR S, MODUGNO F.Lifetime ovulations and epithelial ovarian cancer risk and survival: A systematic review and meta-analysis[J]. Gynecol Oncol, 2022, 165(3): 650-663.
[4] XIANG H, XIAO Y, LI F, et al.Development and validation of an interpretable model integrating multimodal information for improving ovarian cancer diagnosis[J]. Nat Commun, 2024, 15(1): 2681.
[5] 他林昆, 黄燕玲, 张静秋, 等. 阴道彩色多普勒超声参数与老年卵巢癌微血管密度及恶性程度的相关性[J]. 中国老年学杂志, 2020, 40(12): 2534-2536.
[6] ZHANG L, TANG L, CHEN S, et al.A nomogram for predicting the 4-year risk of chronic kidney disease among Chinese elderly adults[J]. Int Urol Nephrol, 2023, 55(6): 1609-1617.
[7] CAI Z, LIN H, LI Z, et al.A prediction nomogram for postoperative gastroparesis syndrome in right colon cancer: a retrospective study[J]. Langenbecks Arch Surg, 2023, 408(1): 148.
[8] WANG X, ZHAO M, ZHANG C, et al.Establishment and Clinical Application of the Nomogram Related to Risk or Prognosis of Hepatocellular Carcinoma: A Review[J]. J Hepatocell Carcinoma, 2023, 10: 1389-1398.
[9] POLDRACK R A, HUCKINS G, VAROQUAUX G.Establishment of Best Practices for Evidence for Prediction: A Review[J]. JAMA Psychiatry, 2020,77(5): 534-540.
[10] VICKERS A J, VAN CALSTER B, STEYERBERG E W.A simple, step-by-step guide to interpreting decision curve analysis[J]. Diagn Progn Res, 2019, 3: 18.
[11] CHEN W, ZHENG R, BAADE P D, et al.Cancer statistics in China, 2015[J]. CA Cancer J Clin, 2016, 66(2): 115-132.
[12] REID B M, PERMUTH J B, SELLERS T A.Epidemiology of ovarian cancer: a review[J]. Cancer Biol Med, 2017, 14(1): 9-32.
[13] WU M, HU Y, REN A, et al.Nomogram Based on Ultrasonography and Clinical Features for Predicting Malignancy in Soft Tissue Tumors[J]. Cancer Manag Res, 2021, 13: 2143-2152.
[14] FERLAY J, SOERJOMATARAM I, DIKSHIT R, et al.Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012[J]. Int J Cancer, 2015, 136(5): E359-E386.
[15] JUNG W, PARK T, KIM Y, et al.Validation of a nomogram to predict the risk of cancer in patients with intraductal papillary mucinous neoplasm and main duct dilatation of 10 mm or less[J]. Br J Surg, 2019, 106(13): 1829-1836.
[16] ZHENG H, TANG H, WANG H, et al.Nomogram to predict lymph node metastasis in patients with early oesophageal squamous cell carcinoma[J]. Br J Surg, 2018, 105(11): 1464-1470.
[17] GUO B L, OUYANG F S, OUYANG L Z, et al.Development and validation of an ultrasound-based nomogram to improve the diagnostic accuracy for malignant thyroid nodules[J]. Eur Radiol, 2019, 29(3): 1518-1526.
[18] QIU S Q, ZENG H C, ZHANG F, et al.A nomogram to predict the probability of axillary lymph node metastasis in early breast cancer patients with positive axillary ultrasound[J]. Sci Rep, 2016, 6: 21196.
[19] HAVAS A, YIN S, ADAMS PD.The role of aging in cancer[J]. Mol Oncol, 2022, 16(18): 3213-3219.
[20] SHAN D Y, CHENG S L, MA Y C, et al.Serum levels of tumor markers and their clinical significance in epithelial ovarian cancer[J]. Zhong Nan Da Xue Xue Bao Yi Xue Ban, 2023, 48(7): 1039-1049.
[21] LIN H, NI L.Diagnostic utility of LDH, CA125 and CYFRA21-1 in tuberculosis pleural effusion[J]. Med Clin (Barc) , 2022, 158(2): 70-72.
基金
山西省运城市中心医院院级课题“双重超声造影对子宫内膜息肉样病变的诊断效果探讨”（YJ2022056）
PDF(2000 KB)
