Background

Conditional survival (CS) takes into consideration the duration of survival post-surgery and can provide valuable additional insights. The aim of this study was to investigate the risk factors associated with reduced one-year postoperative conditional survival in patients diagnosed with stage III T3–T4 colon cancer and real-time prognosis prediction. Furthermore, we aim to develop pertinent nomograms and predictive models.

Methods

Clinical data and survival outcomes of patients diagnosed with stage III T3–T4 colon cancer were obtained from the Surveillance, Epidemiology, and End Results (SEER) database, covering the period from 2010 to 2019. Patients were divided into training and validation cohorts at a ratio of 7:3. The training set consisted of a total of 11,386 patients for conditional overall survival (cOS) and 11,800 patients for conditional cancer-specific survival (cCSS), while the validation set comprised 4876 patients for cOS and 5055 patients for cCSS. Univariate and multivariate Cox regression analyses were employed to identify independent risk factors influencing one-year postoperative cOS and cCSS. Subsequently, predictive nomograms for cOS and cCSS at 2-year, 3-year, 4-year, and 5-year intervals were constructed based on the identified prognostic factors. The performance of these nomograms was rigorously assessed through metrics including the concordance index (C-index), calibration curves, and the area under curve (AUC) derived from the receiver operating characteristic (ROC) analysis. Clinical utility was further evaluated using decision curve analysis (DCA).

Results

A total of 18,190 patients diagnosed with stage III T3–T4 colon cancer were included in this study. Independent risk factors for one-year postoperative cOS and cCSS included age, pT stage, pN stage, pretreatment carcinoembryonic antigen (CEA) levels, receipt of chemotherapy, perineural invasion (PNI), presence of tumor deposits, the number of harvested lymph nodes, and marital status. Sex and tumor site were significantly associated with one-year postoperative cOS, while radiation therapy was notably associated with one-year postoperative cCSS. In the training cohort, the developed nomogram demonstrated a C-index of 0.701 (95% CI, 0.711–0.691) for predicting one-year postoperative cOS and 0.701 (95% CI, 0.713–0.689) for one-year postoperative cCSS. Following validation, the C-index remained robust at 0.707 (95% CI, 0.721–0.693) for one-year postoperative cOS and 0.700 (95% CI, 0.716–0.684) for one-year postoperative cCSS. ROC and calibration curves provided evidence of the model's stability and reliability. Furthermore, DCA underscored the nomogram’s superior clinical utility.

Conclusions

Our study developed nomograms and predictive models for postoperative stage III survival in T3–T4 colon cancer with the aim of accurately estimating conditional survival. Survival bias in our analyses may lead to overestimation of survival outcomes, which may limit the applicability of our findings.

中文翻译：

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III期T3-T4结肠癌患者手术切除后的条件生存分析和实时预后预测：SEER数据库分析

背景

条件生存 (CS) 考虑手术后生存的持续时间，可以提供有价值的额外见解。本研究的目的是调查与 III 期 T3-T4 结肠癌患者术后一年条件生存率降低相关的危险因素以及实时预后预测。此外，我们的目标是开发相关的列线图和预测模型。

方法

诊断为 III 期 T3-T4 结肠癌的患者的临床数据和生存结果来自监测、流行病学和最终结果 (SEER) 数据库，涵盖 2010 年至 2019 年期间。患者被分为训练组和验证组。比例为7：3。训练集总共包括 11,386 名条件性总生存期 (cOS) 患者和 11,800 名条件性癌症特异性生存期 (cCSS) 患者，而验证集包括 4876 名 cOS 患者和 5055 名 cCSS 患者。采用单变量和多变量 Cox 回归分析来确定影响术后一年 cOS 和 cCSS 的独立危险因素。随后，根据确定的预后因素构建了 2 年、3 年、4 年和 5 年间隔的 cOS 和 cCSS 预测列线图。这些列线图的性能通过指标进行严格评估，包括一致性指数 ( C指数)、校准曲线和从受试者工作特征 (ROC) 分析得出的曲线下面积 (AUC)。使用决策曲线分析（DCA）进一步评估临床效用。

结果

这项研究共纳入了 18,190 名诊断为 III 期 T3-T4 结肠癌的患者。术后一年 cOS 和 cCSS 的独立危险因素包括年龄、pT 分期、pN 分期、治疗前癌胚抗原 (CEA) 水平、接受化疗、神经周围侵犯 (PNI)、肿瘤沉积的存在、获取的淋巴结数量、和婚姻状况。性别和肿瘤部位与术后一年的 cOS 显着相关，而放射治疗与术后一年的 cCSS 显着相关。在训练队列中，开发的列线图显示预测术后一年 cOS 的 C 指数为 0.701（95% CI，0.711-0.691），预测术后一年 cCSS 的 C 指数为 0.701（95% CI，0.713-0.689）。经过验证，术后一年 cOS 的 C 指数保持在 0.707（95% CI，0.721-0.693），术后一年 cCSS 的 C 指数保持在 0.700（95% CI，0.716-0.684）。 ROC 和校准曲线提供了模型稳定性和可靠性的证据。此外，DCA 还强调了列线图卓越的临床实用性。

结论

我们的研究开发了 T3-T4 结肠癌术后 III 期生存率的列线图和预测模型，目的是准确估计条件生存率。我们分析中的生存偏差可能会导致对生存结果的高估，这可能会限制我们研究结果的适用性。