Abstract：
To compare shallow machine learning models and deep neural network (DNN) model in prediction of vestibular schwannoma (VS) surgical outcome.
One hundred eighty-eight patients with VS were included; all underwent suboccipital retrosigmoid sinus approach, and preoperative magnetic resonance imaging recorded a series of patient characteristics. Degree of tumor resection was collected during surgery, and facial nerve function was evaluated on the eighth day after surgery. Potential predictors of VS surgical outcome were obtained by univariate analysis, including tumor diameter, tumor volume, tumor surface area, brain tissue edema, tumor property, and tumor shape. This study proposes a DNN framework to predict the prognosis of VS surgical outcomes based on potential predictors and compares it with a series of classic machine learning algorithms including logistic regression.
The results showed that 3 predictors of tumor diameter, tumor volume, and tumor surface area were the most important prognostic factors for VS surgical outcomes, followed by tumor shape, while brain tissue edema and tumor property were the least influential. Different from shallow machine learning models, such as logistic regression with average performance (area under the curve: 0.8263; accuracy: 81.38%), the proposed DNN shows better performance, where area under the curve and accuracy were 0.8723 and 85.64%, respectively.
Based on potential risk factors, DNN can be exploited to achieve preoperative automatic assessment of VS surgical outcomes, and its performance is significantly better than other methods. It is therefore highly warranted to continue to investigate their utility as complementary clinical tools in predicting surgical outcomes preoperatively.
One hundred eighty-eight patients with VS were included; all underwent suboccipital retrosigmoid sinus approach, and preoperative magnetic resonance imaging recorded a series of patient characteristics. Degree of tumor resection was collected during surgery, and facial nerve function was evaluated on the eighth day after surgery. Potential predictors of VS surgical outcome were obtained by univariate analysis, including tumor diameter, tumor volume, tumor surface area, brain tissue edema, tumor property, and tumor shape. This study proposes a DNN framework to predict the prognosis of VS surgical outcomes based on potential predictors and compares it with a series of classic machine learning algorithms including logistic regression.
The results showed that 3 predictors of tumor diameter, tumor volume, and tumor surface area were the most important prognostic factors for VS surgical outcomes, followed by tumor shape, while brain tissue edema and tumor property were the least influential. Different from shallow machine learning models, such as logistic regression with average performance (area under the curve: 0.8263; accuracy: 81.38%), the proposed DNN shows better performance, where area under the curve and accuracy were 0.8723 and 85.64%, respectively.
Based on potential risk factors, DNN can be exploited to achieve preoperative automatic assessment of VS surgical outcomes, and its performance is significantly better than other methods. It is therefore highly warranted to continue to investigate their utility as complementary clinical tools in predicting surgical outcomes preoperatively.
摘要：
目的：比较浅层机器学习模型和深度神经网络（DNN）模型在前庭神经鞘瘤（VS）手术结果预测中的应用。
方法：纳入188例VS患者，均接受枕下乙状窦后入路，术前磁共振成像记录了一系列患者特征。术中收集肿瘤切除程度,术后第8天进行面神经功能评价。通过单因素分析获得VS手术结果的潜在预测因子，包括肿瘤直径，肿瘤体积,肿瘤表面积，脑组织水肿，肿瘤性质，和肿瘤形状。这项研究提出了一个DNN框架来预测VS手术结果的预后，并将其与包括逻辑回归在内的一系列经典机器学习算法进行比较。
结果：结果显示，肿瘤直径的3个预测因子，肿瘤体积,和肿瘤表面积是VS手术结果最重要的预后因素，其次是肿瘤的形状，而脑组织水肿和肿瘤性质影响最小。与浅层机器学习模型不同，例如具有平均性能的逻辑回归（曲线下面积：0.8263；准确性：81.38％），所提出的DNN显示出更好的性能，其中曲线下面积和准确度分别为0.8723和85.64%，分别。
结论：基于潜在的风险因素，可以利用DNN来实现VS手术结果的术前自动评估，并且其性能明显优于其他方法。因此，非常有必要继续研究它们作为预测术前手术结果的补充临床工具的效用。
方法：纳入188例VS患者，均接受枕下乙状窦后入路，术前磁共振成像记录了一系列患者特征。术中收集肿瘤切除程度,术后第8天进行面神经功能评价。通过单因素分析获得VS手术结果的潜在预测因子，包括肿瘤直径，肿瘤体积,肿瘤表面积，脑组织水肿，肿瘤性质，和肿瘤形状。这项研究提出了一个DNN框架来预测VS手术结果的预后，并将其与包括逻辑回归在内的一系列经典机器学习算法进行比较。
结果：结果显示，肿瘤直径的3个预测因子，肿瘤体积,和肿瘤表面积是VS手术结果最重要的预后因素，其次是肿瘤的形状，而脑组织水肿和肿瘤性质影响最小。与浅层机器学习模型不同，例如具有平均性能的逻辑回归（曲线下面积：0.8263；准确性：81.38％），所提出的DNN显示出更好的性能，其中曲线下面积和准确度分别为0.8723和85.64%，分别。
结论：基于潜在的风险因素，可以利用DNN来实现VS手术结果的术前自动评估，并且其性能明显优于其他方法。因此，非常有必要继续研究它们作为预测术前手术结果的补充临床工具的效用。
