易亚男, 韩崇旭, 梁成通, 杨明瑜, 王孟婷. 卵巢癌肺转移患者预后危险因素识别及列线图建立[J]. 实用临床医药杂志, 2023, 27(1): 1-8, 15. DOI: 10.7619/jcmp.20223123
引用本文: 易亚男, 韩崇旭, 梁成通, 杨明瑜, 王孟婷. 卵巢癌肺转移患者预后危险因素识别及列线图建立[J]. 实用临床医药杂志, 2023, 27(1): 1-8, 15. DOI: 10.7619/jcmp.20223123
YI Yanan, HAN Chongxu, LIANG Chengtong, YANG Mingyu, WANG Mengting. Risk factors identification for prognosis of ovarian cancer patients with lung metastasis and establishment of nomogram[J]. Journal of Clinical Medicine in Practice, 2023, 27(1): 1-8, 15. DOI: 10.7619/jcmp.20223123
Citation: YI Yanan, HAN Chongxu, LIANG Chengtong, YANG Mingyu, WANG Mengting. Risk factors identification for prognosis of ovarian cancer patients with lung metastasis and establishment of nomogram[J]. Journal of Clinical Medicine in Practice, 2023, 27(1): 1-8, 15. DOI: 10.7619/jcmp.20223123

卵巢癌肺转移患者预后危险因素识别及列线图建立

Risk factors identification for prognosis of ovarian cancer patients with lung metastasis and establishment of nomogram

  • 摘要:
    目的 基于美国监测、流行病学和最终结果数据库(SEER)探讨卵巢癌肺转移患者预后的独立影响因素并建立生存率预测模型。
    方法 收集2010—2015年1 804例卵巢癌肺转移患者的临床数据,按2∶1比例分为建模集(1 203例)和验证集(601例),通过Cox回归分析评估卵巢癌肺转移患者预后的独立影响因素并建立生存率列线图(Nomogram)预测模型,采用C指数、受试者工作特征(ROC)曲线和矫正曲线对模型准确性进行评价。
    结果 多因素Cox回归分析结果显示,年龄>80岁(HR=1.42,95%CI:1.15~1.76)、肿瘤分化程度为中分化或低分化或未分化(HR=3.96、4.24、3.03,95%CI:1.21~12.98、1.34~13.43、0.95~9.70)、N分期为Nx期(HR=1.25,95%CI:1.06~1.47)、阳性淋巴结检出数量≥10个(HR=1.44,95%CI:1.01~2.10)、骨转移(HR=1.42,95%CI:1.10~1.83)、肝转移(HR=1.28,95%CI:1.12~1.47)、糖类抗原125(CA125)升高(HR=1.89,95%CI:1.18~3.05)是患者总生存率的独立危险因素(P < 0.05),病理组织类型为Ⅱ型上皮性(HR=0.70,95%CI:0.52~0.92)、手术R0切除或其他手术方式(HR=0.40、0.54,95%CI:0.31~0.51、0.45~0.66)、化疗(HR=0.31,95%CI:0.26~0.36)、已婚状态(HR=0.86,95%CI:0.75~0.99)是患者总生存率的独立保护因素(P < 0.05);年龄>80岁、未手术、未化疗、肝转移、骨转移、CA125升高是患者癌症特异性生存率的独立危险因素(P < 0.05)。针对总生存率和癌症特异性生存率分别建立Nomogram预测模型,2种预测模型内部、外部验证的C指数分别为0.767、0.761和0.750、0.742,曲线下面积分别为0.775、0.783和0.749、0.764。
    结论 识别卵巢癌肺转移患者预后的独立影响因素并建立可以定量的可视化Nomogram预测模型,有助于临床医师更加准确地评估患者预后。

     

    Abstract:
    Objective To investigate the independent prognostic factors for ovarian cancer patients with lung metastases based on the Surveillance, Epidemiology and End Results Database (SEER) of the United States and to establish a survival prediction model.
    Methods Clinical data of 1 804 ovarian cancer patients diagnosed with lung metastases from 2010 to 2015 were collected, and they were divided into modeling set (1 203 cases) and validation set (601 cases) in a 2:1 ratio. Independent prognostic factors of ovarian cancer patients with lung metastasis were evaluated by Cox regression analysis and Nomogram prediction model was established. C index, receiver operating characteristic (ROC) curve and correction curve were used to evaluate the accuracy of the model.
    Results Multivariate Cox regression analysis showed that age>80 years old (HR=1.42; 95%CI, 1.15 to 1.76), moderately or poorly differentiated or undifferentiated tumor (HR=3.96, 4.24, 3.03; 95%CI, 1.21 to 12.98, 1.34 to 13.43, 0.95 to 9.70), Nx stage of N stage (HR=1.25; 95%CI, 1.06 to 1.47), ten positive lymph nodes or more (HR=1.44, 95%CI, 1.01 to 2.10), bone metastasis (HR=1.42; 95%CI, 1.10 to 1.83), liver metastasis (HR=1.28; 95%CI, 1.12 to 1.47), elevated CA125 (HR=1.89; 95%CI, 1.18 to 3.05) were independent risk factors for overall survival (P < 0.05). The epithelial type II of histological type (HR=0.70; 95%CI, 0.52 to 0.92), surgical R0 resection or other surgical methods (HR=0.40, 0.54, 95%CI, 0.31 to 0.51, 0.45 to 0.66), chemotherapy (HR=0.31, 95%CI, 0.26 to 0.36) and married status (HR=0.86, 95%CI, 0.75 to 0.99) were independent protective factors for overall survival (P < 0.05). Age>80 years old, no surgery, no chemotherapy, liver metastasis, bone metastasis, and elevated cancer antigen-125 (CA125) were independent risk factors for cancer-specific survival (P < 0.05). Nomogram prediction models were established for the overall survival rate and cancer-specific survival rate, and the C index of internal and external verification were 0.767, 0.761 and 0.750, 0.742, and the areas under the curve were 0.775, 0.783 and 0.749, 0.764, respectively.
    Conclusion Identifying the independent prognostic factors of patients with ovarian cancer with lung metastasis and establishing a quantitative Nomogram prediction model will help clinicians evaluate the prognosis more accurately.

     

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