Department of Auditory Neuroscience, Institute of Experimental Medicine, Czech Academy of Sciences, Prague, Czech Republic.
Department of Cognitive Systems and Neurosciences, Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University, Jugoslávských partyzánů 1580/3, 160 00, Prague 6, Czech Republic.
Sci Rep. 2021 Sep 15;11(1):18376. doi: 10.1038/s41598-021-97819-x.
Decision making on the treatment of vestibular schwannoma (VS) is mainly based on the symptoms, tumor size, patient's preference, and experience of the medical team. Here we provide objective tools to support the decision process by answering two questions: can a single checkup predict the need of active treatment?, and which attributes of VS development are important in decision making on active treatment? Using a machine-learning analysis of medical records of 93 patients, the objectives were addressed using two classification tasks: a time-independent case-based reasoning (CBR), where each medical record was treated as independent, and a personalized dynamic analysis (PDA), during which we analyzed the individual development of each patient's state in time. Using the CBR method we found that Koos classification of tumor size, speech reception threshold, and pure tone audiometry, collectively predict the need for active treatment with approximately 90% accuracy; in the PDA task, only the increase of Koos classification and VS size were sufficient. Our results indicate that VS treatment may be reliably predicted using only a small set of basic parameters, even without the knowledge of individual development, which may help to simplify VS treatment strategies, reduce the number of examinations, and increase cause effectiveness.
治疗前庭神经鞘瘤（VS）的决策主要基于症状、肿瘤大小、患者偏好和医疗团队的经验。在这里，我们提供客观的工具来通过回答两个问题来支持决策过程：单次检查能否预测是否需要积极治疗？以及 VS 发展的哪些特征在积极治疗决策中很重要？使用机器学习对 93 名患者的病历进行分析，通过两种分类任务来实现目标：一种是基于案例的独立时间推理（CBR），其中每个病历都被视为独立的；另一种是个性化动态分析（PDA），在此期间，我们分析了每个患者状态的个体发展。使用 CBR 方法，我们发现 Koos 肿瘤大小分类、语音接受阈值和纯音测听，综合起来可以预测积极治疗的需求，准确率约为 90%；在 PDA 任务中，只有 Koos 分类和 VS 大小的增加是足够的。我们的结果表明，即使不了解个体发展情况，仅使用一小部分基本参数也可以可靠地预测 VS 的治疗，这可能有助于简化 VS 的治疗策略，减少检查次数，并提高疗效。
