Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:69-72. doi: 10.1109/EMBC48229.2022.9871647.
Being diagnosed with breast cancer (BC) can be a traumatic experience for patients who may experience symptoms of depression. In order to facilitate the prevention of such symptoms, it is crucial to understand how and why depressive symptoms emerge and evolve for each individual, from diagnosis through treatment and recovery. In the present work, data from a multicentric study of 706 BC patients followed for 12 months are analyzed. First, a trajectory-based unsupervised clustering based on K-means is performed to capture the dynamic patterns of change in patients' depressive symptoms after BC diagnosis and to identify distinct trajectory clusters. Then a supervised learning approach was employed to build a classification model of depression progression and to identify potential predictors. Patients were clustered into 4 groups: stable low, stable high, improving, and worsening depressive symptoms. In a nested cross-validation pipeline, the performance of the Support Vector Machine model for discriminating between "good" and "poor" progression was 0.78±0.05 in terms of AUC. Several psychological variables emerged as highly predictive of the evolution of depressive symptoms with the most important ones being negative affectivity and anxious preoccupation. Clinical Relevance-The findings of the present study may help clinicians tailor individualized psychological interventions aiming at alleviating the burden of these symptoms in women with breast cancer and improving their overall well-being.
被诊断患有乳腺癌（BC）可能会给患者带来创伤，他们可能会出现抑郁症状。为了便于预防这些症状，了解每个个体从诊断到治疗和康复过程中抑郁症状是如何出现和演变的至关重要。在本研究中，对 706 名接受 12 个月随访的 BC 患者的多中心研究数据进行了分析。首先，采用基于 K-means 的无监督聚类轨迹分析方法，以捕捉患者在 BC 诊断后抑郁症状变化的动态模式，并确定不同的轨迹聚类。然后，采用有监督学习方法构建抑郁进展的分类模型，并确定潜在的预测因素。患者被分为 4 组：稳定低、稳定高、改善和恶化的抑郁症状。在嵌套交叉验证管道中，支持向量机模型区分“良好”和“不良”进展的性能在 AUC 方面为 0.78±0.05。一些心理变量被证明对抑郁症状的演变具有高度预测性，其中最重要的是负性情感和焦虑关注。临床意义-本研究的结果可能有助于临床医生针对乳腺癌女性量身定制个体化的心理干预措施，以减轻这些症状的负担，提高她们的整体幸福感。
