Nascimben Mauro, Lippi Lorenzo, de Sire Alessandro, Invernizzi Marco, Rimondini Lia
Center for Translational Research on Autoimmune and Allergic Diseases-CAAD, Department of Health Sciences, Università del Piemonte Orientale "A. Avogadro", 28100 Novara, Italy.
Enginsoft SpA, 35129 Padua, Italy.
Cancers (Basel). 2023 Jan 4;15(2):336. doi: 10.3390/cancers15020336.
Breast cancer-related lymphedema (BCRL) could be one consequence of breast cancer (BC). Although several risk factors have been identified, a predictive algorithm still needs to be made available to determine the patient's risk from an ensemble of clinical variables. Therefore, this study aimed to characterize the risk of BCRL by investigating the characteristics of autogenerated clusters of patients. The dataset under analysis was a multi-centric data collection of twenty-three clinical features from patients undergoing axillary dissection for BC and presenting BCRL or not. The patients' variables were initially analyzed separately in two low-dimensional embeddings. Afterward, the two models were merged in a bi-dimensional prognostic map, with patients categorized into three clusters using a Gaussian mixture model. The prognostic map represented the medical records of 294 women (mean age: 59.823±12.879 years) grouped into three clusters with a different proportion of subjects affected by BCRL (probability that a patient with BCRL belonged to Cluster A: 5.71%; Cluster B: 71.42%; Cluster C: 22.86%). The investigation evaluated intra- and inter-cluster factors and identified a subset of clinical variables meaningful in determining cluster membership and significantly associated with BCRL biological hazard. The results of this study provide potential insight for precise risk assessment of patients affected by BCRL, with implications in prevention strategies, for instance, focusing the resources on identifying patients at higher risk.
乳腺癌相关淋巴水肿（BCRL）可能是乳腺癌（BC）的一种后果。尽管已经确定了几个风险因素，但仍需要一种预测算法，以便根据一系列临床变量来确定患者的风险。因此，本研究旨在通过调查患者自动生成的聚类特征来描述BCRL的风险。所分析的数据集是一个多中心数据收集，包含接受腋窝清扫术的乳腺癌患者的23项临床特征，这些患者是否出现BCRL。患者变量最初在两个低维嵌入中分别进行分析。之后，将这两个模型合并到一个二维预后图中，使用高斯混合模型将患者分为三个聚类。该预后图展示了294名女性（平均年龄：59.823±12.879岁）的病历，她们被分为三个聚类，其中受BCRL影响的受试者比例不同（BCRL患者属于A聚类的概率：5.71%；B聚类：71.42%；C聚类：22.86%）。该调查评估了聚类内和聚类间的因素，并确定了一组临床变量，这些变量对于确定聚类成员有意义，并且与BCRL生物危害显著相关。本研究结果为精确评估BCRL患者的风险提供了潜在的见解，对预防策略有影响，例如，将资源集中于识别高风险患者。
