Obeidat Akef, Arabi Tarek Ziad, Sabbah Belal Nedal, Alaswad Marwan, Mariyam Nida, Albitar Mohammed Hady
College of Medicine, Alfaisal University, Riyadh, Saudi Arabia.
Ann Med Surg (Lond). 2025 Jun 25;87(8):4765-4769. doi: 10.1097/MS9.0000000000003482. eCollection 2025 Aug.
BACKGROUND: Spindle cell variants of squamous cell carcinoma represent rare and aggressive malignancies with poorly understood prognostic factors and treatment outcomes. This study leverages machine learning approaches alongside traditional statistical methods to analyze a large cohort from the Surveillance, Epidemiology, and End Results (SEER) database. METHODS: We conducted a retrospective analysis of 1086 patients with spindle cell variants of squamous cell carcinoma from the SEER database. Traditional Cox regression and machine learning approaches, including random survival forests (RSF), gradient boosted survival (GBSurv), and DeepSurv models, were employed to identify prognostic factors and predict survival outcomes. Model performance was evaluated using concordance indices and decision curve analysis. RESULTS: Of the 1086 patients included in the analysis, patients were diagnosed with spindle cell variants of squamous cell carcinoma between 1992 and 2021. Median age was 70 years (IQR: 60-77). Primary tumor sites included larynx (21.4%), lung and bronchus (20.2%), and tongue (10.7%). 32.0% had localized disease, 25.4% had regional disease, and 17.2% had distant disease, with 25.4% having unknown stage. Treatment modalities included radiation therapy in 51.9% and chemotherapy in 30.7% of patients. In the multivariate Cox model, kidney and renal pelvis tumors showed the highest risk (HR: 6.28, 95% CI: 2.26-17.45, < 0.001), followed by urinary bladder (HR: 2.72, 95% CI: 1.56-4.74, < 0.001) and lung/bronchus sites (HR: 1.94, 95% CI: 1.51-2.50, < 0.001). The RSF model demonstrated superior discriminative ability (C-index: 0.733, 95% CI: 0.680-0.784) compared to GBSurv (C-index: 0.294, 95% CI: 0.239-0.351) and DeepSurv (C-index: 0.314, 95% CI: 0.255-0.378) approaches. CONCLUSIONS: The findings suggest that anatomical site and disease stage significantly influence survival, while current treatment modalities show limited impact on outcomes. The superior performance of the RSF model indicates potential value in using machine learning for risk stratification in clinical practice.
背景：鳞状细胞癌的梭形细胞变体是罕见且侵袭性强的恶性肿瘤，其预后因素和治疗结果尚不清楚。本研究利用机器学习方法和传统统计方法，对监测、流行病学和最终结果（SEER）数据库中的一个大型队列进行分析。 方法：我们对SEER数据库中1086例鳞状细胞癌梭形细胞变体患者进行了回顾性分析。采用传统的Cox回归和机器学习方法，包括随机生存森林（RSF）、梯度增强生存（GBSurv）和深度生存（DeepSurv）模型，来识别预后因素并预测生存结果。使用一致性指数和决策曲线分析评估模型性能。 结果：在纳入分析的1086例患者中，患者在1992年至2021年期间被诊断为鳞状细胞癌的梭形细胞变体。中位年龄为70岁（四分位间距：60 - 77岁）。原发肿瘤部位包括喉（21.4%）、肺和支气管（20.2%）以及舌（10.7%）。32.0%为局限性疾病，25.4%为区域性疾病，17.2%为远处疾病，25.4%分期未知。治疗方式包括51.9%的患者接受放射治疗，30.7%的患者接受化疗。在多变量Cox模型中，肾脏和肾盂肿瘤显示出最高风险（风险比：6.28，95%置信区间：2.26 - 17.45，P < 0.001），其次是膀胱（风险比：2.72，95%置信区间：1.56 - 4.74，P < 0.001）和肺/支气管部位（风险比：1.94，95%置信区间：1.51 - 2.50，P < 0.001）。与GBSurv（一致性指数：0.294，95%置信区间：0.239 - 0.351）和DeepSurv（一致性指数：0.314，95%置信区间：0.255 - 0.378）方法相比，RSF模型表现出更好的判别能力（一致性指数：0.733，95%置信区间：0.680 - 0.784）。 结论：研究结果表明，解剖部位和疾病分期对生存有显著影响，而目前的治疗方式对结果的影响有限。RSF模型的优越性能表明在临床实践中使用机器学习进行风险分层具有潜在价值。
Cochrane Database Syst Rev. 2022-9-26
J Stomatol Oral Maxillofac Surg. 2024-9
J Cancer Res Clin Oncol. 2023-9
J Multidiscip Healthc. 2023-6-26
Front Oncol. 2023-1-4
Orphanet J Rare Dis. 2020-6-9
Transl Lung Cancer Res. 2019-9
Zotero 插件