Refining feasibility assessment of endoscopic ear surgery: a radiomics model utilizing machine learning on external auditory canal CT scans

Abstract

Background

Feasibility assessment of endoscopic ear surgery (EES) relies solely on subjective evaluation by surgeons.

Objective

Extracting radiomic features from preoperative CT images of the external auditory canal, we aim to classify EES patients into easy and difficult groups and improve accuracy in determining surgery feasibility.

Methods

85 patients’ external auditory canal CT scans were collected and 139 radiomic features were extracted using PyRadiomics. The most relevant features were selected and three machine learning algorithms (logistic regression, support vector machine, and random forest) were compared using K-fold cross-validation (k = 5) to predict surgical feasibility.

Results

The best-performing machine learning model, the support vector machine (SVM), was selected to predict the difficulty of EES. The proposed model achieved a high accuracy of 86.5%, and F1 score of 84.6%. The area under the ROC curve was 0.93, indicating good discriminatory power.

Conclusions and significance

The proposed machine learning model provides a reliable and accurate method for classifying patients undergoing otologic surgery based on preoperative imaging data. The model can help clinicians to better prepare for challenging surgical cases and optimize treatment plans for individual patients.

Chinese Abstract

背景：内窥镜耳手术 (EES) 的可行性评估完全依赖于外科医生的主观评价。

目的：从术前外耳道 CT 图像中提取放射组学特征, 目的是将 EES 患者分为简单组和困难组, 并提高确定手术可行性的准确度。

方法：收集了 85 例患者的外耳道 CT 扫描, 使用 PyRadiomics 提取了 139 个放射组学特征。 选择了最相关的特征, 使用 K 折交叉验证 (k=5) 来比较三种机器学习算法（逻辑回归、支持向量机和随机森林）, 以预测手术可行性。

结果：表现最好的机器学习模型是支持向量机（SVM）, 被选择用于预测 EES 的难度。 这个模型达到了 86.5% 的高精度, F1得分为84.6%。 ROC曲线下面积为0.93, 显示良好的分别功能。

结论和意义：我们建议的机器学习模型提供了一种可靠和准确的基于术前影像学数据对接受耳科手术的患者进行分类的方法。 这种模型可以帮助临床医生更好地准备具有挑战性的手术病例, 并优化针对单个患者的治疗方案。

Keywords:

• Endoscopic ear surgery
• external auditory canal
• radiomics
• image analysis
• feature extraction
• machine learning

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This study was supported by National Natural Science Foundation of China [No. 82171146], Zhejiang Provincial Natural Science Foundation of China [No. LZ2211130001] Natural Science Foundation of Zhejiang Province