Hsia Jiun-Yi, Chang Chi-Chang, Liu Chung-Feng, Chou Chia-Lin, Yang Ching-Chieh
Division of Thoracic Surgery, Department of Surgery, Chung Shan Medical University Hospital, Taichung 402367, Taiwan.
School of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan.
Diagnostics (Basel). 2024 Jul 8;14(13):1461. doi: 10.3390/diagnostics14131461.
Predicting and improving the response of rectal cancer to second primary cancers (SPCs) remains an active and challenging field of clinical research. Identifying predictive risk factors for SPCs will help guide more personalized treatment strategies. In this study, we propose that experience data be used as evidence to support patient-oriented decision-making. The proposed model consists of two main components: a pipeline for extraction and classification and a clinical risk assessment. The study includes 4402 patient datasets, including 395 SPC patients, collected from three cancer registry databases at three medical centers; based on literature reviews and discussion with clinical experts, 10 predictive variables were considered risk factors for SPCs. The proposed extraction and classification pipelines that classified patients according to importance were age at diagnosis, chemotherapy, smoking behavior, combined stage group, and sex, as has been proven in previous studies. The C5 method had the highest predicted AUC (84.88%). In addition, the proposed model was associated with a classification pipeline that showed an acceptable testing accuracy of 80.85%, a recall of 79.97%, a specificity of 88.12%, a precision of 85.79%, and an F1 score of 79.88%. Our results indicate that chemotherapy is the most important prognostic risk factor for SPCs in rectal cancer survivors. Furthermore, our decision tree for clinical risk assessment illuminates the possibility of assessing the effectiveness of a combination of these risk factors. This proposed model may provide an essential evaluation and longitudinal change for personalized treatment of rectal cancer survivors in the future.
预测并改善直肠癌对第二原发性癌症（SPCs）的反应仍然是临床研究中一个活跃且具有挑战性的领域。识别SPCs的预测风险因素将有助于指导更具个性化的治疗策略。在本研究中，我们建议将经验数据用作支持以患者为导向的决策的证据。所提出的模型由两个主要部分组成：一个用于提取和分类的流程以及一个临床风险评估。该研究包括从三个医疗中心的三个癌症登记数据库收集的4402个患者数据集，其中包括395名SPC患者；基于文献综述以及与临床专家的讨论，10个预测变量被视为SPCs的风险因素。如先前研究所证明的那样，所提出的根据重要性对患者进行分类的提取和分类流程包括诊断时的年龄、化疗、吸烟行为、联合分期组和性别。C5方法具有最高的预测AUC（84.88%）。此外，所提出的模型与一个分类流程相关联，该流程显示出可接受的测试准确率为80.85%、召回率为79.97%、特异性为88.12%、精确率为85.79%以及F1分数为79.88%。我们的结果表明，化疗是直肠癌幸存者中SPCs最重要的预后风险因素。此外，我们用于临床风险评估的决策树阐明了评估这些风险因素组合有效性的可能性。这个所提出的模型可能为未来直肠癌幸存者的个性化治疗提供重要的评估和纵向变化。
