Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Technol Cancer Res Treat. 2022 Jan-Dec;21:15330338221132035. doi: 10.1177/15330338221132035.
This study aimed to establish a nomogram to predict overall survival in lung squamous cell carcinoma patients with metastasis for clinical decision-making. We investigated lung squamous cell carcinoma patients diagnosed with stage M1 in the Surveillance, Epidemiology, and Final Results database between 2010 and 2015. They were divided into training cohort and validation cohort. In the training cohort, statistically significant prognostic factors were identified using univariate and multivariate Cox regression analysis, and an individualized nomogram model was developed. The model was evaluated by C-index, area under the curve, calibration plot, decision curve analysis, and risk group stratification. In total, 9910 patients were included in our study, including 6937 in the training cohort and 2937 in the validation cohort. Factors containing age, T stage, N stage, bone metastasis, brain metastasis, liver metastasis, surgery, chemotherapy, and radiotherapy were independent prognostic factors for overall survival and were used in the construction of the nomogram. The C-index in the training cohort and validation cohort were 0.711 (95% confidenc interval: 0.705-0.717) and 0.707 (95% confidenc interval: 0.697-0.717), respectively. The time-dependent area under the curve of both groups was higher than 0.7 within 5 years. Calibration plots indicated that the nomogram-predicted survival was consistent with the recorded 6-month, 1-year, and 2-year prognoses. Furthermore, decision curve analysis revealed that the nomogram was clinically useful and had a better discriminative ability to recognize patients at high risk than the TNM criteria-based tumor staging. And then we developed an overall survival risk classification system based on the nomogram total points for each patient, which divided all patients into a high-risk group and a low-risk group. Finally, we implemented this nomogram in a free online tool. We constructed a nomogram and a corresponding risk classification system predicting the overall survival of lung squamous cell carcinoma patients with metastasis. These tools can assist in patients' counseling and guide treatment decision-making.
本研究旨在建立一个列线图模型，以预测转移性肺鳞癌患者的总生存期，为临床决策提供依据。我们调查了 2010 年至 2015 年期间 Surveillance, Epidemiology, and Final Results 数据库中诊断为 M1 期的肺鳞癌患者。他们被分为训练队列和验证队列。在训练队列中，使用单因素和多因素 Cox 回归分析确定了统计学上显著的预后因素，并建立了个体化列线图模型。通过 C 指数、曲线下面积、校准图、决策曲线分析和风险分组分层来评估模型。本研究共纳入 9910 例患者，其中训练队列 6937 例，验证队列 2937 例。包含年龄、T 分期、N 分期、骨转移、脑转移、肝转移、手术、化疗和放疗的因素是总生存的独立预后因素，并用于构建列线图。训练队列和验证队列的 C 指数分别为 0.711（95%置信区间：0.705-0.717）和 0.707（95%置信区间：0.697-0.717）。两组的时间依赖性曲线下面积在 5 年内均高于 0.7。校准图表明，列线图预测的生存与记录的 6 个月、1 年和 2 年预后一致。此外，决策曲线分析表明，该列线图具有临床应用价值，并且比基于 TNM 标准的肿瘤分期更能识别高危患者，具有更好的区分能力。然后，我们根据列线图总分，为每位患者建立了一个总体生存风险分类系统，将所有患者分为高风险组和低风险组。最后，我们将该列线图实现为一个免费的在线工具。我们构建了一个列线图和相应的风险分类系统，用于预测转移性肺鳞癌患者的总生存期。这些工具可以帮助患者咨询，并指导治疗决策。
