Establishment and Validation of a Prognostic Nomogram for Predicting Postoperative Overall Survival in Advanced Stage III–IV Colorectal Cancer Patients

IF 2.9 2区医学 Q2 ONCOLOGY

Cancer Medicine Pub Date : 2024-11-15 DOI:10.1002/cam4.70385

Pengwei Lou, Dongmei Luo, Yuting Huang, Chen Chen, Shuai Yuan, Kai Wang

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Abstract

Background

Most colorectal cancer (CRC) patients are at an advanced stage when they are first diagnosed. Risk factors for predicting overall survival (OS) in advanced stage CRC patients are crucial, and constructing a prognostic nomogram model is a scientific method for survival analysis.

Methods

A total of 2956 advanced stage CRC patients were randomised into training and validation groups at a 7:3 ratio. Univariate and multivariate Cox proportional hazards regression analyses were used to screen risk factors for OS and subsequently construct a prognostic nomogram model for predicting 1-, 3-, 5-, 8- and 10-year OS of advanced stage CRC patients. The performance of the model was demonstrated by the area under the curve (AUC) values, calibration curves and decision curve analysis (DCA). Kaplan–Meier curves were used to plot the survival probabilities for different strata of each risk factor.

Results

There was no statistically significant difference (p > 0.05) in the 32 clinical variables between patients in the training and validation groups. Univariate and multivariate Cox proportional hazards regression analyses demonstrated that age, location, TNM, chemotherapy, liver metastasis, lung metastasis, MSH6, CEA, CA199, CA125 and CA724 were risk factors for OS. We estimated the AUC values for the nomogram model to predict 1-, 3-, 5-, 8- and 10-year OS, which in the training group were 0.826 (95% CI: 0.807–0.845), 0.836 (0.819–0.853), 0.839 (0.820–0.859), 0.835 (0.809–0.862) and 0.825 (0.779–0.870) respectively; in the validation group, the corresponding AUC values were 0.819 (0.786–0.852), 0.831 (0.804–0.858), 0.830 (0.799–0.861), 0.815 (0.774–0.857) and 0.802 (0.723–0.882) respectively. Finally, the 1-, 3-, 5-, 8- and 10-year OS rates for advanced stage CRC patients were 73.4 (71.8–75.0), 49.5 (47.8–51.4), 43.3 (41.5–45.2), 40.1 (38.1–41.9) and 38.6 (36.6–40.8) respectively.

Conclusion

We constructed and validated an original nomogram for predicting the postoperative OS of advanced stage CRC patients, which can help facilitates physicians to accurately assess the individual survival of postoperative patients and identify high-risk patients.

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建立并验证用于预测晚期 III-IV 期结直肠癌患者术后总生存期的预后提名图

背景：大多数结直肠癌（CRC）患者在首次确诊时已处于晚期。预测晚期 CRC 患者总生存期（OS）的风险因素至关重要，而构建预后提名图模型是一种科学的生存分析方法：方法：将 2956 例晚期 CRC 患者按 7:3 的比例随机分为训练组和验证组。方法：将 2956 例晚期 CRC 患者按 7:3 的比例随机分为训练组和验证组，采用单变量和多变量 Cox 比例危险回归分析筛选出 OS 的风险因素，并随后构建了预测晚期 CRC 患者 1 年、3 年、5 年、8 年和 10 年 OS 的预后提名图模型。该模型的性能通过曲线下面积（AUC）值、校准曲线和决策曲线分析（DCA）得到了证明。卡普兰-梅耶曲线用于绘制每个风险因素的不同分层的生存概率：在 32 个临床变量中，训练组和验证组患者的差异无统计学意义（P > 0.05）。单变量和多变量Cox比例危险回归分析表明，年龄、部位、TNM、化疗、肝转移、肺转移、MSH6、CEA、CA199、CA125和CA724是影响OS的危险因素。我们估算了预测 1、3、5、8 和 10 年 OS 的提名图模型的 AUC 值，训练组的 AUC 值分别为 0.826（95% CI：0.807-0.845）、0.836（0.819-0.853）、0.839（0.820-0.859）、0.835（0.809-0.在验证组中，相应的 AUC 值分别为 0.819（0.786-0.852）、0.831（0.804-0.858）、0.830（0.799-0.861）、0.815（0.774-0.857）和 0.802（0.723-0.882）。最后，晚期CRC患者的1年、3年、5年、8年和10年OS率分别为73.4（71.8-75.0）、49.5（47.8-51.4）、43.3（41.5-45.2）、40.1（38.1-41.9）和38.6（36.6-40.8）：我们构建并验证了预测晚期 CRC 患者术后 OS 的原始提名图，有助于医生准确评估术后患者的个体生存率并识别高危患者。

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来源期刊

Cancer Medicine ONCOLOGY-

CiteScore

5.50

自引率

2.50%

发文量

907

审稿时长

19 weeks

期刊介绍： Cancer Medicine is a peer-reviewed, open access, interdisciplinary journal providing rapid publication of research from global biomedical researchers across the cancer sciences. The journal will consider submissions from all oncologic specialties, including, but not limited to, the following areas: Clinical Cancer Research Translational research ∙ clinical trials ∙ chemotherapy ∙ radiation therapy ∙ surgical therapy ∙ clinical observations ∙ clinical guidelines ∙ genetic consultation ∙ ethical considerations Cancer Biology: Molecular biology ∙ cellular biology ∙ molecular genetics ∙ genomics ∙ immunology ∙ epigenetics ∙ metabolic studies ∙ proteomics ∙ cytopathology ∙ carcinogenesis ∙ drug discovery and delivery. Cancer Prevention: Behavioral science ∙ psychosocial studies ∙ screening ∙ nutrition ∙ epidemiology and prevention ∙ community outreach. Bioinformatics: Gene expressions profiles ∙ gene regulation networks ∙ genome bioinformatics ∙ pathwayanalysis ∙ prognostic biomarkers. Cancer Medicine publishes original research articles, systematic reviews, meta-analyses, and research methods papers, along with invited editorials and commentaries. Original research papers must report well-conducted research with conclusions supported by the data presented in the paper.