Post-operative tumour progression in patients with non-functioning pituitary neuroendocrine tumours is variable. The aim of this study was to use machine learning (ML) models to improve the prediction of post-operative outcomes in patients with NF PitNET. We studied data from 383 patients who underwent surgery with or without radiotherapy, with a follow-up period between 6 months and 15 years. ML models, including k-nearest neighbour (KNN), support vector machine (SVM), and decision tree, showed superior performance in predicting tumour progression when compared with parametric statistical modelling using logistic regression, with SVM achieving the highest performance. The strongest predictor of tumour progression was the extent of surgical resection, with patient age, tumour volume, and the use of radiotherapy also showing influence. No features showed an association with tumour recurrence following a complete resection. In conclusion, this study demonstrates the potential of ML models in predicting post-operative outcomes for patients with NF PitNET. Future work should look to include additional, more granular, multicentre data, including incorporating imaging and operative video data.
无功能性垂体神经内分泌肿瘤患者术后肿瘤进展存在差异性。本研究旨在利用机器学习模型改进对NF PitNET患者术后结局的预测。我们分析了383例接受手术（部分联合放疗）患者的数据，随访期为6个月至15年。与采用逻辑回归的参数统计模型相比，包括K近邻算法、支持向量机和决策树在内的机器学习模型在预测肿瘤进展方面表现出更优性能，其中支持向量机模型效果最佳。肿瘤进展的最强预测因子是手术切除范围，患者年龄、肿瘤体积及放疗应用也显示出影响作用。在完全切除的病例中，未发现任何特征与肿瘤复发存在关联。本研究证实了机器学习模型在预测NF PitNET患者术后结局方面的潜力，未来研究应纳入更精细的多中心数据，包括影像学及手术视频资料。
肿瘤(癌症)患者之家