The First Clinical College, Guangdong Medical University, Zhanjiang, Guangdong, 524023, China.
Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE, USA.
J Cancer Res Clin Oncol. 2024 Jul 25;150(7):364. doi: 10.1007/s00432-024-05886-0.
Signet ring cell carcinoma (SRCC) is a rare type of lung cancer. The conventional survival nomogram used to predict lung cancer performs poorly for SRCC. Therefore, a novel nomogram specifically for studying SRCC is highly required.
Baseline characteristics of lung signet ring cell carcinoma were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression and random forest analysis were performed on the training group data, respectively. Subsequently, we compared results from these two types of analyses. A nomogram model was developed to predict 1-year, 3-year, and 5-year overall survival (OS) for patients, and receiver operating characteristic (ROC) curves and calibration curves were used to assess the prediction accuracy. Decision curve analysis (DCA) was used to assess the clinical applicability of the proposed model. For treatment modalities, Kaplan-Meier curves were adopted to analyze condition-specific effects.
We obtained 731 patients diagnosed with lung signet ring cell carcinoma (LSRCC) in the SEER database and randomized the patients into a training group (551) and a validation group (220) with a ratio of 7:3. Eight factors including age, primary site, T, N, and M.Stage, surgery, chemotherapy, and radiation were included in the nomogram analysis. Results suggested that treatment methods (like surgery, chemotherapy, and radiation) and T-Stage factors had significant prognostic effects. The results of ROC curves, calibration curves, and DCA in the training and validation groups demonstrated that the nomogram we constructed could precisely predict survival and prognosis in LSRCC patients. Through deep verification, we found the constructed model had a high C-index, indicating that the model had a strong predictive power. Further, we found that all surgical interventions had good effects on OS and cancer-specific survival (CSS). The survival curves showed a relatively favorable prognosis for T0 patients overall, regardless of the treatment modality.
Our nomogram is demonstrated to be clinically beneficial for the prognosis of LSRCC patients. The surgical intervention was successful regardless of the tumor stage, and the Cox proportional hazard (CPH) model had better performance than the machine learning model in terms of effectiveness.
印戒细胞癌（SRCC）是一种罕见的肺癌类型。用于预测肺癌的传统生存诺模图在 SRCC 中的表现不佳。因此，非常需要一种专门用于研究 SRCC 的新型诺模图。
从监测、流行病学和最终结果（SEER）数据库中获得肺印戒细胞癌的基线特征。分别对训练组数据进行单变量和多变量 Cox 回归和随机森林分析。然后，我们比较了这两种分析的结果。建立了一个预测患者 1 年、3 年和 5 年总生存率（OS）的诺模图模型，并使用接收者操作特征（ROC）曲线和校准曲线评估预测准确性。决策曲线分析（DCA）用于评估所提出模型的临床适用性。对于治疗方式，采用 Kaplan-Meier 曲线分析特定条件的影响。
我们从 SEER 数据库中获得了 731 例肺印戒细胞癌（LSRCC）患者，并将患者随机分为训练组（551 例）和验证组（220 例），比例为 7:3。包括年龄、原发部位、T、N 和 M.Stage、手术、化疗和放疗在内的 8 个因素包括在诺模图分析中。结果表明，治疗方法（如手术、化疗和放疗）和 T 期因素对预后有显著影响。在训练组和验证组中，ROC 曲线、校准曲线和 DCA 的结果表明，我们构建的诺模图可以精确预测 LSRCC 患者的生存和预后。通过深入验证，我们发现构建的模型具有较高的 C 指数，表明该模型具有较强的预测能力。此外，我们发现所有手术干预对 OS 和癌症特异性生存（CSS）都有良好的效果。生存曲线总体上显示 T0 患者的预后相对较好，无论治疗方式如何。
我们的诺模图被证明对 LSRCC 患者的预后具有临床益处。手术干预无论肿瘤分期如何都取得了成功，Cox 比例风险（CPH）模型在有效性方面优于机器学习模型。
