Uysal Emre, Durak Gorkem, Kotek Sedef Ayse, Bagci Ulas, Berber Tanju, Gurdal Necla, Akkus Yildirim Berna
Department of Radiation Oncology, University of Health Science, Prof. Dr. Cemil Tascioglu City Hospital, Istanbul 34390, Turkey.
Machine & Hybrid Intelligence Lab, Department of Radiology, Northwestern University, Chicago, IL 60611, USA.
Diagnostics (Basel). 2025 Jul 10;15(14):1747. doi: 10.3390/diagnostics15141747.
: Current developments in computer-aided systems rely heavily on complex and computationally intensive algorithms. However, even a simple approach can offer a promising solution to reduce the burden on clinicians. Addressing this, we aim to employ unsupervised cluster analysis to identify prognostic subgroups of non-small-cell lung cancer (NSCLC) patients with brain metastasis (BM). Simple-yet-effective algorithms designed to identify similar group characteristics will assist clinicians in categorizing patients effectively. : We retrospectively collected data from 95 NSCLC patients with BM treated at two oncology centers. To identify clinically distinct subgroups, two types of unsupervised clustering methods-two-step clustering (TSC) and hierarchical cluster analysis (HCA)-were applied to the baseline clinical data. Patients were categorized into prognostic classes according to the Diagnosis-Specific Graded Prognostic Assessment (DS-GPA). Survival curves for the clusters and DS-GPA classes were generated using Kaplan-Meier analysis, and the differences were assessed with the log-rank test. The discriminative ability of three categorical variables on survival was compared using the concordance index (C-index). : The mean age of the patients was 61.8 ± 0.9 years, and the majority (77.9%) were men. Extracranial metastasis was present in 71.6% of the patients, with most (63.2%) having a single BM. The DS-GPA classification significantly divided the patients into prognostic classes ( < 0.001). Furthermore, statistical significance was observed between clusters created by TSC ( < 0.001) and HCA ( < 0.001). HCA showed the highest discriminatory power (C-index = 0.721), followed by the DS-GPA (C-index = 0.709) and TSC (C-index = 0.650). : Our findings demonstrated that the TSC and HCA models were comparable in prognostic performance to the DS-GPA index in NSCLC patients with BM. These results suggest that unsupervised clustering may offer a data-driven perspective on patient stratification, though further validation is needed to clarify its role in prognostic modeling.
当前计算机辅助系统的发展严重依赖于复杂且计算密集型的算法。然而，即使是一种简单的方法也能为减轻临床医生的负担提供一个有前景的解决方案。针对这一点，我们旨在采用无监督聚类分析来识别非小细胞肺癌（NSCLC）脑转移（BM）患者的预后亚组。旨在识别相似组特征的简单而有效的算法将帮助临床医生有效地对患者进行分类。
我们回顾性收集了在两个肿瘤中心接受治疗的95例NSCLC脑转移患者的数据。为了识别临床上不同的亚组，将两种类型的无监督聚类方法——两步聚类（TSC）和层次聚类分析（HCA）——应用于基线临床数据。根据诊断特异性分级预后评估（DS - GPA）将患者分类为预后类别。使用Kaplan - Meier分析生成聚类和DS - GPA类别的生存曲线，并使用对数秩检验评估差异。使用一致性指数（C指数）比较三个分类变量对生存的判别能力。
患者的平均年龄为61.8±0.9岁，大多数（77.9%）为男性。71.6%的患者存在颅外转移，大多数（63.2%）有单个脑转移灶。DS - GPA分类显著将患者分为预后类别（<0.001）。此外，在TSC（<0.001）和HCA（<0.001）创建的聚类之间观察到统计学显著性。HCA显示出最高的判别能力（C指数 = 0.721），其次是DS - GPA（C指数 = 0.709）和TSC（C指数 = 0.650）。
我们的研究结果表明，在NSCLC脑转移患者中，TSC和HCA模型在预后性能方面与DS - GPA指数相当。这些结果表明，无监督聚类可能为患者分层提供数据驱动的观点，尽管需要进一步验证以阐明其在预后建模中的作用。
