Park Dougho, Cho Jae Man, Yang Joong Won, Yang Donghoon, Kim Mansu, Oh Gayeoul, Kwon Heum Dai
Department of Rehabilitation Medicine, Pohang Stroke and Spine Hospital, Pohang, South Korea.
Department of Neurosurgery, Pohang Stroke and Spine Hospital, Pohang, South Korea.
Front Surg. 2022 Sep 6;9:1010420. doi: 10.3389/fsurg.2022.1010420. eCollection 2022.
Therapeutic decisions for degenerative cervical myelopathy (DCM) are complex and should consider various factors. We aimed to develop machine learning (ML) models for classifying expert-level therapeutic decisions in patients with DCM.
This retrospective cross-sectional study included patients diagnosed with DCM, and the diagnosis of DCM was confirmed clinically and radiologically. The target outcomes were defined as conservative treatment, anterior surgical approaches (ASA), and posterior surgical approaches (PSA). We performed the following classifications using ML algorithms: multiclass, one-versus-rest, and one-versus-one. Two ensemble ML algorithms were used: random forest (RF) and extreme gradient boosting (XGB). The area under the receiver operating characteristic curve (AUC-ROC) was the primary metric. We also identified the variable importance for each classification.
In total, 304 patients were included (109 conservative, 66 ASA, 125 PSA, and 4 combined surgeries). For multiclass classification, the AUC-ROC of RF and XGB models were 0.91 and 0.92, respectively. In addition, ML models showed AUC-ROC values of >0.9 for all types of binary classifications. Variable importance analysis revealed that the modified Japanese Orthopaedic Association score and central motor conduction time were the two most important variables for distinguishing between conservative and surgical treatments. When classifying ASA and PSA, the number of involved levels, age, and body mass index were important contributing factors.
ML-based classification of DCM therapeutic options is valid and feasible. This study can be a basis for establishing generalizable ML-based surgical decision models for DCM. Further studies are needed with a large multicenter database.
退行性颈椎脊髓病（DCM）的治疗决策复杂，应考虑多种因素。我们旨在开发机器学习（ML）模型，用于对DCM患者的专家级治疗决策进行分类。
这项回顾性横断面研究纳入了诊断为DCM的患者，DCM的诊断通过临床和影像学检查得以确认。目标结局定义为保守治疗、前路手术入路（ASA）和后路手术入路（PSA）。我们使用ML算法进行了以下分类：多类、一对其余和一对一。使用了两种集成ML算法：随机森林（RF）和极端梯度提升（XGB）。受试者操作特征曲线下面积（AUC-ROC）是主要指标。我们还确定了每种分类的变量重要性。
总共纳入了304例患者（109例保守治疗、66例ASA、125例PSA和4例联合手术）。对于多类分类，RF和XGB模型的AUC-ROC分别为0.91和0.92。此外，ML模型在所有类型的二元分类中AUC-ROC值均>0.9。变量重要性分析显示，改良日本骨科协会评分和中枢运动传导时间是区分保守治疗和手术治疗的两个最重要变量。在对ASA和PSA进行分类时，受累节段数、年龄和体重指数是重要的影响因素。
基于ML的DCM治疗方案分类是有效且可行的。本研究可为建立适用于DCM的基于ML的可推广手术决策模型奠定基础。需要使用大型多中心数据库进行进一步研究。
